102 Commits
Author SHA1 Message Date
John Lancaster 2a2700b78c uv.lock update 2026-08-07 19:14:59 -05:00
John Lancaster 7162645ff8 instructions update 2026-08-07 19:13:41 -05:00
John Lancaster 44edffb8b7 changed to skill provider 2026-08-07 19:09:54 -05:00
John Lancaster c6817a074e prompt toc update 2026-08-06 23:18:14 -05:00
John Lancaster 7ac90d29dd template updates 2026-08-06 23:17:50 -05:00
John Lancaster 0dc06f72ca engine/session updates 2026-07-31 22:15:51 -05:00
John Lancaster cd11ea8255 fixed caching due to docs symlink 2026-07-31 15:39:09 -05:00
John Lancaster bc21643e8c jsfiddle prompt 2026-07-31 15:12:18 -05:00
John Lancaster 1ed5856db0 asyncgenerator fix 2026-07-30 20:51:16 -05:00
John Lancaster 70695ff218 toc update 2026-07-30 01:29:19 -05:00
John Lancaster a238fb4dc3 example of 2 database backends 2026-07-30 01:29:05 -05:00
John Lancaster b6f109cf91 fastapi updates 2026-07-30 01:28:39 -05:00
John Lancaster 3abafc4850 prune 2026-07-30 01:04:11 -05:00
John Lancaster d4c7952175 task tweak 2026-07-30 01:03:51 -05:00
John Lancaster da58e20b69 mounting docs 2026-07-30 01:03:34 -05:00
John Lancaster d79025538b extra javascript 2026-07-30 01:03:24 -05:00
John Lancaster 7b1e5fcacb focused styling 2026-07-30 01:02:26 -05:00
John Lancaster 37461fd880 mathjax 2026-07-30 00:41:54 -05:00
John Lancaster 226f19b2c6 more styling 2026-07-30 00:41:26 -05:00
John Lancaster a18c8456d3 pydantic-settings update 2026-07-30 00:08:48 -05:00
John Lancaster 34e6d693ab uvicorn startup 2026-07-30 00:01:18 -05:00
John Lancaster efba051cb5 css reference page 2026-07-29 23:47:24 -05:00
John Lancaster c9b6e137f2 mount change 2026-07-29 23:38:04 -05:00
John Lancaster 8f26051a52 nicegui consolidation 2026-07-29 23:37:48 -05:00
John Lancaster bc0d6ede49 simplified a bit 2026-07-26 20:11:25 -05:00
John Lancaster aed2e41ef0 started crud reference 2026-07-26 19:35:01 -05:00
John Lancaster d999a04144 improvements 2026-07-26 19:10:40 -05:00
John Lancaster 4818e86a1e improving async fastapi sqlmodel skill 2026-07-26 17:57:55 -05:00
John Lancaster b6393f1222 renamed async fastapi skill 2026-07-26 17:40:33 -05:00
John Lancaster 3897eabfbc authoring reference 2026-07-26 17:39:42 -05:00
John Lancaster 9e0097708c docstrings 2026-07-26 17:25:55 -05:00
John Lancaster 42ea105bee WIP simplifying load/startup 2026-07-26 17:23:39 -05:00
John Lancaster 5e20f69cfe settings 2026-07-26 14:09:08 -05:00
John Lancaster 007d823c0a ruff rules 2026-07-21 08:52:32 -05:00
John Lancaster 27f783fc90 app factory 2026-07-21 08:49:36 -05:00
John Lancaster 7970e76d4f config updates 2026-07-21 08:43:31 -05:00
John Lancaster 70dd0f45d9 declarative logging 2026-07-08 22:39:46 -05:00
John Lancaster 963805c551 logging skill updates 2026-07-08 21:03:38 -05:00
John Lancaster a3ca1a65c2 task updates 2026-07-02 23:23:11 -05:00
John Lancaster 94dd47cc19 logging references 2026-07-02 23:22:55 -05:00
John Lancaster b3d4e55a15 uv.lock update 2026-07-02 23:05:12 -05:00
John Lancaster 7b2b80ecf2 config updates 2026-07-02 23:05:05 -05:00
John Lancaster d4ca78dbfb renamed python-logging 2026-07-02 23:04:54 -05:00
John Lancaster eeeb6ecdbe async sqlmodel 2026-06-26 00:53:18 -05:00
John Lancaster 0177496fab pydantic-settings skill 2026-06-25 21:51:49 -05:00
John Lancaster 00498a2fed rename 2026-06-24 08:55:04 -05:00
John Lancaster 913ba66d8b pytest scaffold 2026-06-24 08:41:11 -05:00
John Lancaster e2c199c1b7 usage notes 2026-06-24 08:38:43 -05:00
John Lancaster 45a1e56d1c binding dataclass page 2026-06-23 19:46:35 -05:00
John Lancaster a6ccc14917 greenfield architecture 2026-06-22 10:00:21 -05:00
John Lancaster a0ae38d0cc authoring prompt 2026-06-22 09:39:57 -05:00
John Lancaster 34d3808bbb shim creation prompt 2026-06-22 08:50:26 -05:00
John Lancaster 1cfe9c8e40 docs 2026-06-22 08:39:18 -05:00
John Lancaster 313c4ecb1e authoring page 2026-06-22 08:15:35 -05:00
John Lancaster ea5450f6cb pytest principles 2026-06-22 07:59:18 -05:00
John Lancaster ab53c239bf rename 2026-06-21 22:55:58 -05:00
John Lancaster 35d7fa1718 tag conventions 2026-06-21 22:52:26 -05:00
John Lancaster e93462ec3b better tagging 2026-06-21 22:46:53 -05:00
John Lancaster 58a94ad9b6 doc updates 2026-06-21 22:35:05 -05:00
John Lancaster c893173fcc contract updates 2026-06-21 22:29:46 -05:00
John Lancaster 123c491413 tightening 2026-06-21 22:12:02 -05:00
John Lancaster 3c7f7e61b7 mcp details skills 2026-06-21 21:56:27 -05:00
John Lancaster 76ea9ebbda in process fixes 2026-06-21 21:24:37 -05:00
John Lancaster 0aa7ace272 asyncio testing 2026-06-21 21:05:10 -05:00
John Lancaster 4da2b0ac83 better web tests 2026-06-21 21:01:11 -05:00
John Lancaster 806bb15bcc link skill script 2026-06-21 21:00:55 -05:00
John Lancaster ff7cd4a07f testing page updates 2026-06-21 20:54:33 -05:00
John Lancaster 3603471699 fixes 2026-06-21 20:24:05 -05:00
John Lancaster dcbf570a13 ipywidgets 2026-06-21 20:22:50 -05:00
John Lancaster d3b336f1e3 started tasks 2026-06-21 18:15:56 -05:00
John Lancaster 1f7e63267a doc updates 2026-06-21 18:12:58 -05:00
John Lancaster 69cd9037a3 mcp tests 2026-06-21 18:04:46 -05:00
John Lancaster b9bb11ac02 web tests 2026-06-21 17:57:01 -05:00
John Lancaster 4958eeb3ef connection tests 2026-06-21 17:51:16 -05:00
John Lancaster 5a31ba6390 mcp endpoint test scaffold 2026-06-21 17:48:33 -05:00
John Lancaster 36347ff4a5 models 2026-06-21 17:35:01 -05:00
John Lancaster c189677717 started model tests 2026-06-21 17:09:03 -05:00
John Lancaster 37fa9b6c6f better data models 2026-06-21 16:53:43 -05:00
John Lancaster 34923b51d7 prune 2026-06-21 16:15:47 -05:00
John Lancaster 2d65d83162 docstrings 2026-06-21 16:15:37 -05:00
John Lancaster 3a6e2665dd pruning 2026-06-21 16:01:08 -05:00
John Lancaster b98d8b782a added current doc collection tests 2026-06-21 15:58:00 -05:00
John Lancaster 36032040ae added prompt ingestion 2026-06-21 15:51:51 -05:00
John Lancaster 4f05f13e45 better typing 2026-06-21 15:41:22 -05:00
John Lancaster 57347077a9 better pytest 2026-06-21 15:36:31 -05:00
John Lancaster 7fec3a4337 ty checking 2026-06-21 15:29:57 -05:00
John Lancaster c5b7733528 typing skill improvements 2026-06-21 15:20:00 -05:00
John Lancaster 3c5db37223 test content 2026-06-21 15:13:44 -05:00
John Lancaster 29130c3a0c registry ingest test scaffolding 2026-06-21 12:58:42 -05:00
John Lancaster aec3500370 typing 2026-06-21 12:41:14 -05:00
John Lancaster 9c8ab70c06 python typing skill 2026-06-21 12:37:19 -05:00
John Lancaster 9a9432cc55 frozen pydantic models 2026-06-21 12:26:12 -05:00
John Lancaster 4320a251f5 ruff workflow 2026-06-21 12:14:46 -05:00
John Lancaster caa4a5079a WIP loading 2026-06-21 11:34:07 -05:00
John Lancaster 993dc6a879 file loading 2026-06-21 10:06:25 -05:00
John Lancaster dab539489a started manual refactor 2026-06-21 09:13:30 -05:00
John Lancaster 197fa32f2c rename 2026-06-20 20:37:09 -05:00
John Lancaster c653c7024b structured tests 2026-06-20 20:36:44 -05:00
John Lancaster 82b50fb63b testing page 2026-06-20 20:34:10 -05:00
John Lancaster f8e0c14d46 started prompt mechanics 2026-06-20 20:27:32 -05:00
John Lancaster 098a2418ee shims 2026-06-20 20:03:54 -05:00
John Lancaster 7f672b9c8f pytest naming convention 2026-06-20 20:02:03 -05:00
131 changed files with 9319 additions and 3983 deletions
@@ -0,0 +1,19 @@
---
name: Authoring Content
description: "Use when editing Markdown under docs/. Routes authors to the canonical docs ownership, layout, and symlink guidance."
applyTo: 'docs/**/*.md'
---
For edits under `docs/`, use the [Authoring Guide](../../docs/authoring.md) as the entry point for content placement and contracts.
For source-tree ownership, symlink, packaging, or runtime questions, follow [Source Tree Ownership](../../docs/authoring.md). Treat that section as authoritative instead of restating its guidance here.
Primary references:
- [Skill contract](../../docs/contracts/skill_contract.md)
- [Prompt contract](../../docs/contracts/prompt.md)
- [Frontmatter contract](../../docs/contracts/frontmatter.md)
- [URI contract](../../docs/contracts/uris.md)
- `skill://zensical-docs/SKILL.md`
Inspect `skill://zensical-docs/_manifest` only when a supporting documentation reference is needed.
@@ -0,0 +1,15 @@
---
name: FastMCP Python Guidance
description: Route FastMCP Python changes to the Personal MCP source-reference skill.
applyTo: '**/*.py'
---
A core part of this repository is [FastMCP](https://gofastmcp.com/servers/server).
For FastMCP implementation or protocol questions, load `skill://mcp-details/SKILL.md` first.
Inspect `skill://mcp-details/_manifest` only when a source reference is needed, then read the relevant supporting path. For FastMCP Python APIs, prefer the supporting reference that covers SDKs and FastMCP.
This repository is resource-first and exposes resources as tools only as a fallback. In tool-only clients, use `list_resources` and `read_resource` with the same `skill://` URIs.
Reconcile the skill guidance with the installed FastMCP version and the repository's existing implementation before editing.
@@ -0,0 +1,19 @@
---
name: Pytest Scaffolding Guidance
description: Route tests edits to the Personal MCP pytesting resource.
applyTo: 'tests/**'
---
When editing files under `tests/`, use `skill://pytesting/SKILL.md` as the primary guidance source for test scaffolding and pytest authoring decisions.
Execution pattern:
1. Load `skill://pytesting/SKILL.md` first.
2. Inspect `skill://pytesting/_manifest` only when a supporting reference is needed.
3. Apply only the portions relevant to the file being edited.
4. Keep tests focused, deterministic, and aligned with repository conventions.
5. Include source-document links for any feature-level recommendation.
If task intent is ambiguous, ask one clarifying question before editing.
Be sure to also refer to the [testing page](../../docs/testing.md) page for design detail
@@ -0,0 +1,19 @@
---
name: VS Code Configuration
description: Route .vscode edits to the Personal MCP VS Code configuration skill resource.
applyTo: '.vscode/**'
---
When editing files under `.vscode/`, use `skill://vscode-configuration/SKILL.md` as the primary guidance source.
Execution pattern:
1. Load `skill://vscode-configuration/SKILL.md` first.
2. Inspect `skill://vscode-configuration/_manifest` and select only the matching reference page for the current file type:
- `launch.json` -> debug launch configurations.
- `tasks.json` -> tasks.json project tasks.
- `mcp.json` -> mcp.json MCP server configuration.
3. Prefer the smallest safe config change and keep settings explicit.
4. Include source-document links for any feature-level recommendation.
If task intent is ambiguous, ask one clarifying question before editing.
@@ -4,6 +4,10 @@ description: Use the Zensical docs MCP resource when editing Markdown documentat
applyTo: '**/*.md'
---
When editing Markdown files in this repository, use the Zensical docs resource `resource://skills/zensical-docs/document` for relevant documentation authoring guidance.
When editing Markdown files in this repository, use `skill://zensical-docs/SKILL.md` for relevant documentation authoring guidance. Inspect `skill://zensical-docs/_manifest` only when a supporting reference is needed.
Prefer Zensical-native documentation conventions when they cover the need cleanly, while preserving expected MkDocs compatibility unless the Zensical guidance intentionally diverges.
Always check to make sure the entries in `/home/john/Documents/prompts/zensical.toml` are up to date with any changes.
Also ensure that top-level pages specify icons in their front matter
+1 -1
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@@ -61,7 +61,7 @@ Example valid ids:
- `fastapi-uv-docker`
- `zensical-docs`
- `pytest-scaffolding`
- `pytesting`
Example invalid ids:
-12
View File
@@ -1,12 +0,0 @@
---
name: New Skill Bootstrap
description: Create and fully implement a new docs-first skill in this repository.
argument-hint: skill-id and goal for the new skill
agent: agent
---
# New Skill Bootstrap
Use the canonical bootstrap guidance in [docs/skills/new-skill/SKILL.md](../../docs/skills/new-skill/SKILL.md).
If the request is to create or implement a new skill, load that skill document and follow it as the source of truth.
+2
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@@ -2,3 +2,5 @@
__pycache__
.cache*
site/
*.log*
+86
View File
@@ -0,0 +1,86 @@
{
"version": "2.0.0",
"tasks": [
{
"label": "Ruff: Check",
"type": "shell",
"command": "uv",
"args": [
"run",
"ruff",
"check",
"."
],
"options": {
"cwd": "${workspaceFolder}"
},
"problemMatcher": []
},
{
"label": "Ty: Check",
"type": "shell",
"command": "uv",
"args": [
"run",
"ty",
"check"
],
"options": {
"cwd": "${workspaceFolder}"
},
"problemMatcher": []
},
{
"label": "Docs: Build",
"type": "shell",
"command": "uv",
"args": [
"run",
"zensical",
"build"
],
"options": {
"cwd": "${workspaceFolder}"
},
"group": "build",
"problemMatcher": []
},
{
"label": "Server: Run (uvicorn)",
"type": "shell",
"command": "uv",
"args": [
"run",
"uvicorn",
"personal_mcp.main:create_app",
"--factory",
"--host",
"127.0.0.1",
"--port",
"8000",
"--reload"
],
"options": {
"cwd": "${workspaceFolder}"
},
"isBackground": true,
"problemMatcher": []
},
{
"label": "Docker: Compose Up (Build)",
"type": "shell",
"command": "docker",
"args": [
"compose",
"up",
"--build",
"-d"
],
"options": {
"cwd": "${workspaceFolder}"
},
"isBackground": true,
"problemMatcher": []
}
]
}
+32 -21
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@@ -1,43 +1,54 @@
# syntax=docker/dockerfile:1
FROM python:3.12-slim AS builder
COPY --from=ghcr.io/astral-sh/uv:0.8.4 /uv /uvx /bin/
FROM python:3.14-slim AS builder
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
UV_COMPILE_BYTECODE=1 \
UV_LINK_MODE=copy
UV_LINK_MODE=copy \
UV_LOCKED=1
WORKDIR /app
COPY pyproject.toml uv.lock ./
COPY src ./src
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=zensical.toml,target=zensical.toml \
--mount=type=bind,source=docs/,target=docs/ \
uvx zensical build
RUN uv sync --frozen --no-dev
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=uv.lock,target=uv.lock \
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
uv sync --no-install-project
COPY docs ./docs
COPY zensical.toml ./
# COPY --chown=appuser:appuser . /app
RUN uv run zensical build
# RUN --mount=type=cache,target=/root/.cache/uv \
# uv sync --no-editable
FROM python:3.12-slim AS runtime
FROM python:3.14-slim AS runtime
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
PATH="/app/.venv/bin:$PATH" \
PERSONAL_MCP_HOST=0.0.0.0 \
PERSONAL_MCP_PORT=8765
PERSONAL_MCP_SITE_DIR=/app/site
WORKDIR /app
RUN groupadd --system --gid 1001 appuser \
&& useradd --system --uid 1001 --gid appuser --create-home --home-dir /home/appuser appuser
COPY --from=builder --chown=appuser:appuser /app /app
EXPOSE 8765
RUN groupadd --system --gid 1001 appuser && \
useradd --system --uid 1001 --gid appuser appuser
COPY --from=ghcr.io/astral-sh/uv:latest --chown=appuser:appuser /uv /uvx /bin/
COPY --from=builder --chown=appuser:appuser /app/.venv /app/.venv
COPY --from=builder --chown=appuser:appuser /app/site /app/site
COPY --chown=appuser:appuser ./docs /app/docs
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=uv.lock,target=uv.lock \
--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
--mount=type=bind,source=src/,target=src/ \
uv sync --no-editable --refresh-package prompts
USER appuser
CMD ["uvicorn", "personal_mcp.main:app", "--host", "0.0.0.0", "--port", "8765"]
CMD ["uvicorn", "personal_mcp.main:create_app", "--factory", "--host", "0.0.0.0", "--port", "8765"]
+1 -1
View File
@@ -3,6 +3,6 @@ services:
build:
context: .
dockerfile: Dockerfile
restart: unless-stopped
ports:
- "8765:8765"
restart: unless-stopped
+76 -60
View File
@@ -16,17 +16,19 @@ The system is complete in three layers:
2. Catalog resources provide normalized discovery.
3. Zensical builds a static site from those same Markdown sources and the FastAPI app serves it in the FastMCP runtime process.
Prompt documents under `docs/prompts/` are also indexed and exposed as first-class catalog and prompt surfaces.
This architecture is anchored by three contracts:
1. Docs-first authored content contract under `docs/` with strict per-skill ownership.
2. `SKILL.md` frontmatter contract with Anthropic fields plus `x-personal-mcp` metadata.
3. Canonical resource URI contract with break-and-replace policy for contract changes.
2. Standard `SKILL.md` frontmatter consumed directly by FastMCP.
3. Native `skill://` resource URIs with break-and-replace policy for contract changes.
Detailed contract pages:
1. [Content Contract](./content.md)
2. [Frontmatter Contract](./frontmatter.md)
3. [URI Contract](./uris.md)
1. [Content Contract](./contracts/index.md#content-contract)
2. [Frontmatter Contract](./contracts/frontmatter.md)
3. [URI Contract](./contracts/uris.md)
This architecture keeps authored content human-friendly while preserving machine-stable contracts.
@@ -49,40 +51,59 @@ Each skill encapsulates one methodology domain in a docs-owned directory:
The skill document and references are the authored source of truth; runtime code indexes and serves these files without becoming a second authored source.
Each skill publishes resource families:
Each skill publishes three native resource families:
1. document
1. `skill://<name>/SKILL.md` for primary instructions
2. `skill://<name>/_manifest` for file discovery and integrity metadata
3. `skill://<name>/{path*}` for supporting files
The document resource returns canonical Markdown, while clients can perform any downstream section extraction they need.
The main resource returns canonical Markdown. The generated manifest lists real relative paths, sizes, and SHA256 hashes so clients can load supporting material selectively.
### Prompt Modules
Prompt guidance can be authored in `docs/prompts/` using either canonical prompt directories (`docs/prompts/<prompt-id>/PROMPT.md`) or legacy markdown files during migration.
Prompt modules publish two additive surfaces:
1. prompt resources for catalog and document retrieval
2. MCP prompt objects for prompt-list/get-prompt style client workflows
This keeps authored markdown as source-of-truth while allowing clients to discover and invoke prompts directly.
### Catalog Module
The catalog is the canonical discovery layer and publishes normalized records for all modules. It may also expose a minimal set of read-only discovery tools that resolve back to the same canonical markdown content when a client chat surface does not expose MCP resource attachment.
The catalog publishes normalized records for prompts. Skills use FastMCP's native resource discovery and client utilities instead of a parallel catalog.
Typical catalog resources:
1. resource://catalog/skills_index
2. resource://catalog/skills/{skill_id}
1. resource://catalog/prompts_index
2. resource://catalog/prompts_index{?q,tag,cursor,limit}
3. resource://catalog/prompts/{prompt_id}
Only canonical catalog resources are part of the runtime contract in this phase.
### Registry Loader
The runtime composition includes a startup registry loader that reads packaged docs resources using `importlib.resources.files(...)` and `Traversable` APIs.
Importing the package does not read or parse documentation. The MCP server and FastAPI application factories initialize content when constructing a runnable server. The prompt/docs registry reads packaged resources through `importlib.resources.files(...)` and `Traversable` APIs; the native skills provider receives the packaged `personal_mcp/docs/skills` filesystem path.
Loader responsibilities:
1. Parse SKILL.md frontmatter for each skill.
2. Validate schema and cross-field constraints before any resource registration.
3. Build an in-memory registry keyed by `skill_id`.
4. Fail fast for duplicate ids, missing markdown files, broken reference mappings, and invalid `depends_on` values.
1. Parse and validate prompt frontmatter.
2. Build the prompt catalog and MCP prompt objects.
3. Index authored Markdown for `resource://docs/{path*}`.
Registry load failure is a startup error, not a partial runtime warning.
Skill loading is owned by `SkillsDirectoryProvider`, which scans the packaged skills directory and constructs native resources before the server starts serving requests.
The immutable registry is cached for the process lifetime. Each Uvicorn worker constructs and retains its own registry because worker processes do not share Python objects. Registry load failure is a server-factory startup error, not a package-import error or partial runtime warning.
### Content Sources
Content is authored in markdown under `docs/` and managed as long-form reference material. Skill documents and companion references now live under `docs/skills/`, while project-authored pages remain alongside them in the docs tree. Resource handlers expose the same authored documents through stable resource URIs.
The repository root `docs/` directory is the only authored source. The `src/personal_mcp/docs` path is a relative symlink to that directory for source-checkout and editable-install workflows; it is not a second content tree and packaging does not depend on traversing it.
For wheel builds, Hatchling's normal `src/personal_mcp` package traversal follows the relative `docs` symlink and archives its targets as regular files under `personal_mcp/docs/`. No `force-include` mapping is used because that would add the same archive paths twice. The prompt/docs registry uses [`importlib.resources.files`](https://docs.python.org/3/library/importlib.resources.html#importlib.resources.files), while `SkillsDirectoryProvider` scans the package-relative filesystem path. Neither path depends on the current working directory.
### Static Docs Surface
Static docs are built directly from two markdown source streams:
@@ -92,65 +113,60 @@ Static docs are built directly from two markdown source streams:
The merged docs tree is built by Zensical into static files and served by the FastAPI app.
Generated `site/` files are deployment assets for the human-facing static site. They are separate from the authored Markdown resources packaged under `personal_mcp/docs/`.
## Data Flow
```mermaid
flowchart TD
A[Authored Markdown] --> C[Resource Handlers]
B[Pattern Metadata] --> D[Catalog Resources]
A --> E[Zensical Static Build]
E --> H[FastAPI Static Mount]
H --> I[Served Docs Site]
D --> I
A[Authored Skill Directories] --> B[SkillsDirectoryProvider]
B --> C[Native Skill Resources]
D[Authored Prompts and Docs] --> E[Prompt and Docs Registry]
E --> F[Prompt Catalog and Docs Resources]
A --> G[Zensical Static Build]
D --> G
G --> H[FastAPI Static Mount]
```
## Contracts
### Metadata Contract
Each skill declares frontmatter in `docs/skills/<skill-id>/SKILL.md`.
Each skill declares standard frontmatter in `docs/skills/<skill-id>/SKILL.md`.
For the full field-level contract, validation model, and FastMCP metadata mapping, see [Frontmatter Contract](./frontmatter.md).
For the full field-level contract, validation model, and FastMCP metadata mapping, see [Frontmatter Contract](./contracts/frontmatter.md).
Anthropic-facing required fields:
Required fields:
1. name
2. description
Repository indexing metadata is declared in `x-personal-mcp`:
1. id
2. version
3. tags
4. capabilities
5. depends_on
6. optional references map (for nested entries, overrides, and aliases)
No `metadata.yaml` sidecar is part of the end-state contract.
The directory name is the provider identity and must match `name`. There is no skill catalog metadata or sidecar.
### URI Contract
Canonical resource URIs are:
For the full URI semantics, parameter validation rules, and compatibility policy, see [URI Contract](./uris.md).
For the full URI semantics, parameter validation rules, and compatibility policy, see [URI Contract](./contracts/uris.md).
1. resource://skills/<skill_id>/document
2. resource://skills/<skill_id>/references/<ref_id>
3. resource://catalog/skills_index
4. resource://catalog/skills/{skill_id}
5. resource://docs/{path*}
1. skill://<skill_name>/SKILL.md
2. skill://<skill_name>/_manifest
3. skill://<skill_name>/<supporting_path>
4. resource://docs/{path*}
5. resource://catalog/prompts_index
6. resource://catalog/prompts_index{?q,tag,cursor,limit}
7. resource://catalog/prompts/{prompt_id}
8. resource://prompts/{prompt_id}/document
Validation rules:
1. `skill_id` is lowercase kebab-case and must satisfy the stable skill id contract.
2. `ref_id` is lowercase kebab-case and must resolve from either:
- top-level auto-discovery of `references/*.md` filename stems, or
- an explicit `x-personal-mcp.references` entry.
3. `path*` resolves only to normalized markdown paths under `docs/`.
1. `skill_name` is the lowercase kebab-case skill directory name.
2. `supporting_path` is a provider-validated relative path within that skill.
3. Docs `path*` resolves only to normalized Markdown paths under `docs/`.
### Resource Registration Contract
Resources are registered from the validated registry, not by ad hoc per-skill hardcoding.
Skill resources are registered by one `SkillsDirectoryProvider`; prompt and docs resources remain registered from the validated registry.
Registration rules:
@@ -200,21 +216,22 @@ Markdown remains easy to review, while contracts remain stable for clients.
### Client Independence
Clients can use Ask, Edit, or Agent modes without requiring server-owned prompt orchestration. However, MCP affordances are still chat-surface-dependent: some clients or sessions expose resource attachment directly, while others make tool invocation the more reliable retrieval path.
Clients can use Ask, Edit, or Agent modes without requiring prompt-first orchestration. Prompt objects are available as an additive MCP surface, while resource retrieval remains the canonical source path. MCP affordances are still chat-surface-dependent: some clients or sessions expose resource attachment directly, while others make tool invocation the more reliable retrieval path.
## Authoring and Publishing Lifecycle
1. Update markdown reference content.
2. Update metadata if capability surface changes.
3. Build static docs with Zensical.
4. Serve built output through FastAPI static mount.
2. Keep skill `name` and directory identity aligned.
3. Build static docs with Zensical and run provider tests.
4. Package authored docs into `personal_mcp/docs/`.
5. Serve native MCP resources and the static docs mount.
## Scope and Non-Goals
In-scope:
1. Resource-first methodology delivery
2. Catalog-based discovery
2. Native FastMCP skill discovery
3. Pre-built static docs hosting in app runtime
Out-of-scope:
@@ -223,16 +240,15 @@ Out-of-scope:
2. Large tool inventories duplicating static guidance across skill modules
3. Separate dynamic docs service at runtime
Allowed exception:
1. A small catalog-level tool layer is acceptable when it improves client interoperability without creating a second source of truth for skill content.
The prompt catalog remains an independent surface. Tool-only skill clients use generic resource tools rather than a skill-specific compatibility layer.
## Example Content Inputs
Existing markdown reference sets are valid examples of authored source material for this architecture:
1. docs/skills/pytest-scaffolding/references/pytest-docs.md
2. docs/skills/python-logging-dictconfig/references/python-logging-docs.md
3. docs/skills/fastapi-uv-docker/references/fastapi-best-practices.md
1. docs/skills/pytesting/references/pytest-docs.md
2. docs/skills/python-logging/references/python-logging-docs.md
3. docs/skills/python-logging/references/json-file-logging.md
4. docs/skills/fastapi-uv-docker/references/fastapi-best-practices.md
These inputs are treated as content sources, while resource URIs and catalog payloads remain the machine-facing contracts.
These inputs are treated as content sources, while native skill URIs and generated manifests form the machine-facing skill contract.
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---
icon: lucide/pencil
---
# Authoring Guide
This page defines the practical workflow for maintaining skills, prompts, and project documentation while keeping root `docs/` as the only authored source.
Primary references:
1. [Skill Contract](./contracts/skill_contract.md)
2. [Prompt Contract](./contracts/prompt.md)
3. [Frontmatter Contract](./contracts/frontmatter.md)
4. [URI Contract](./contracts/uris.md)
5. [Zensical documentation skill](./skills/zensical-docs/SKILL.md)
## Source Tree Ownership
Edit content only under root `docs/`. The `src/personal_mcp/docs` path is a relative symlink for editable installs; do not author through a copied package tree.
Hatchling's normal package traversal follows `src/personal_mcp/docs` during wheel builds and archives the linked targets as regular files under `personal_mcp/docs/`. Do not add a `force-include` entry for root `docs/`; it duplicates those wheel paths. The installed package therefore gives `SkillsDirectoryProvider` a regular filesystem directory while Zensical builds the human site directly from root `docs/`.
Generated `site/` content is a build artifact and must not be edited by hand.
## Content Layout
```text
docs/
*.md
contracts/
prompts/<prompt-id>/
PROMPT.md
references/
skills/<skill-name>/
SKILL.md
references/
```
Keep skill and prompt files inside their owning directories. Relative links may cross sections, but content ownership should remain clear.
## Skill Authoring
A skill is discovered when a direct child of `docs/skills/` contains `SKILL.md`.
Required frontmatter:
```yaml
---
name: <skill-name>
description: <what the skill does and when to use it>
---
```
Rules:
1. Use lowercase kebab-case for the directory and `name`.
2. Keep `name` exactly equal to the directory name.
3. Write a specific description because clients use it for discovery.
4. Do not add `x-personal-mcp`, versions, tags, capabilities, or reference mappings.
5. Put supporting material anywhere beneath the skill directory, normally under `references/`.
6. Link supporting files from `SKILL.md` so humans and agents understand when to load them.
FastMCP recursively scans every skill file and generates `skill://<name>/_manifest`. Supporting-resource identity is the real relative path, not a synthetic reference id.
Recommended sequence:
1. Draft or revise `SKILL.md` routing guidance.
2. Add focused supporting files.
3. Verify relative links.
4. Run the provider tests and docs build.
5. Restart running servers because production uses `reload=False`.
## Prompt Authoring
Prompts remain registry-backed:
1. Keep one canonical `PROMPT.md`.
2. Align directory name, `name`, and `x-personal-mcp.id`.
3. Include `resource://prompts/<prompt-id>/document` in capabilities.
4. Define arguments beneath `x-personal-mcp.arguments`.
5. Keep long rationale and sources in `references/`.
Prompt argument names must be valid Python identifiers. Each argument accepts optional `title`, `description`, and `required`; unknown fields fail strict validation.
## Frontmatter Safety
1. Quote scalar values containing `:`.
2. Quote values with reserved YAML characters such as `#`, `{}`, `[]`, or leading `*`.
3. Use block scalars for punctuation-heavy multiline text.
4. Keep fields within the applicable skill or prompt contract.
## Writing Quality
1. Prefer focused sections and descriptive headings.
2. Link feature-level claims to authoritative sources.
3. Use relative links for internal pages.
4. Keep code examples minimal and actionable.
5. Avoid bare URLs in prose.
6. Load only supporting material relevant to the immediate task.
## Copilot Routing
Active instructions should point directly to native main resources:
1. `skill://zensical-docs/SKILL.md`
2. `skill://pytesting/SKILL.md`
3. `skill://vscode-configuration/SKILL.md`
When deeper guidance is needed, read the selected skill's `_manifest` and fetch supporting files by their listed path. Tool-only clients use `list_resources` and `read_resource` over the same URIs.
## Validation Checklist
```bash
uv run pytest tests/skills/test_provider.py tests/web/test_mcp_skills.py -q
uv run zensical build
uv run ruff check .
uv run ty check
uv run pytest
```
For packaging changes, also build and inspect an installed wheel so provider path resolution is verified outside the editable checkout.
## Navigation
When adding or moving pages:
1. update `zensical.toml`
2. keep top-level page icons in frontmatter
3. rebuild the site
4. verify internal links and navigation labels
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---
icon: lucide/file-text
---
# Content Contract
This page defines the authored content contract for the docs-first MCP architecture.
## Canonical Source Of Truth
1. All authored Markdown lives under `docs/`.
2. MCP resources and static docs are two distribution surfaces of the same authored files.
3. No parallel authored markdown is allowed in `src/` or other package-only paths.
## Canonical Skill Shape
Each skill is one directory under `docs/skills/`:
```text
docs/
skills/
<skill-id>/
SKILL.md
references/
... (one or more markdown files, optional nested folders)
```
Rules:
1. `SKILL.md` is required for every skill.
2. `references/` is the only place for skill-specific supporting docs.
3. Nested folders inside `references/` are allowed so a skill can reorganize internals without changing global architecture.
4. Skill directories are independent ownership boundaries; no cross-skill file writes.
## File Placement And Ownership Boundaries
1. Top-level project docs stay in `docs/*.md`.
2. Skill docs stay in `docs/skills/<skill-id>/...`.
3. A skill may link to other skills, but must not store content inside another skill's directory.
4. Server and runtime code may index and serve docs, but must not be the source of authored markdown.
## Metadata Location Constraint
1. Skill metadata is embedded in YAML frontmatter in `SKILL.md`.
2. No `metadata.yaml` sidecar exists in the end state.
3. Reference lookup metadata is documented and explicit: top-level `references/*.md` are auto-discovered from filenames, while `SKILL.md` frontmatter declares overrides and nested mappings when needed.
## Skill Id Contract
`skill-id` is the public identifier and should satisfy all rules below:
1. Format: lowercase kebab-case only.
2. Character set: `a-z`, `0-9`, and `-`.
3. Must start with a letter.
4. No underscores, spaces, dots, or uppercase characters.
5. Directory name should equal `skill-id` in each committed revision.
6. Frontmatter `id` should equal directory name in each committed revision.
7. Treat `skill-id` as immutable after release; any rename is a breaking replacement and clients must move to the new id.
Valid examples:
1. `fastapi-uv-docker`
2. `zensical-docs`
3. `pytest-scaffolding`
Invalid examples:
1. `fastapi_uv_docker`
2. `Zensical-Docs`
3. `docs.zensical`
## Invariants
This contract guarantees:
1. One authored source tree in `docs/` for both website and MCP.
2. One skill directory maps to one skill identity per revision.
3. Namespace and slug drift is minimized by keeping directory and frontmatter ids aligned per revision.
4. Per-skill reference structure can evolve without changing cross-skill architecture.
5. Packaging for stdio is deterministic because authored content is path-stable.
## Non-Goals
This contract does not define:
1. URI versioning policy details.
2. The full frontmatter schema.
3. Migration instructions from the current architecture.
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---
icon: lucide/braces
---
# Frontmatter Contract
This page defines the authored frontmatter contracts for native FastMCP skills and registry-backed prompts.
## Skill Frontmatter
Skills use the standard Agent Skills fields consumed by the [FastMCP Skills Provider](https://gofastmcp.com/servers/providers/skills):
```yaml
---
name: <skill-id>
description: <what the skill does and when to use it>
---
```
Rules:
1. `name` and `description` are required.
2. `name` must equal the skill directory name.
3. The repository uses lowercase kebab-case directory names.
4. Skill frontmatter contains no `x-personal-mcp` catalog metadata.
5. Supporting files require no frontmatter manifest. The provider discovers files recursively and generates `_manifest` with relative paths, byte sizes, and SHA256 hashes.
The provider uses the directory name as the URI identity and the frontmatter `description` as the main resource description. Repository tests enforce directory/name parity and reject extra skill frontmatter fields.
## Prompt Frontmatter
Prompts remain registry-backed and retain repository metadata:
```yaml
---
name: <prompt-id>
description: <what the prompt does and when to use it>
x-personal-mcp:
id: <prompt-id>
version: <semver>
tags:
- <tag>
capabilities:
- resource://prompts/<prompt-id>/document
arguments:
<argument-name>:
title: <display title>
description: <input guidance>
required: true
---
```
Prompt rules:
1. `name`, `description`, and `x-personal-mcp` are required.
2. `x-personal-mcp.id`, `name`, and the prompt directory name must match.
3. `version` must be semantic version text.
4. `capabilities` must include `resource://prompts/<prompt-id>/document`.
5. Argument names must be valid Python identifiers.
6. Argument entries accept optional `title`, `description`, and `required` fields.
7. Unknown prompt fields are rejected by the strict Pydantic registry models.
See the MCP [prompts concept documentation](https://modelcontextprotocol.io/docs/learn/server-concepts#prompts) and [schema reference](https://modelcontextprotocol.io/specification/latest/schema) for the protocol-level prompt shape.
## Validation Timing
Skill validation is file- and provider-oriented:
1. `SkillsDirectoryProvider` discovers each directory containing `SKILL.md`.
2. FastMCP parses the description and scans all files when the provider is created.
3. Repository tests enforce the stricter standard-only frontmatter and directory/name rules.
Prompt validation remains registry-oriented and fails server startup for invalid metadata, duplicate prompt ids, or malformed arguments.
## Invariants
1. Skills remain directly portable to tools that understand standard Agent Skills directories.
2. Native skill discovery has no parallel catalog metadata source.
3. Prompts retain the richer metadata required by their catalog and MCP prompt-object surfaces.
4. All authored content remains under `docs/`.
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---
icon: lucide/file-check-2
---
# Contracts
This section groups the core data and contract documents for the repository.
## Pages
1. [Prompt Contract](./prompt.md)
2. [Skill Contract](./skill_contract.md)
3. [Frontmatter Contract](./frontmatter.md)
4. [URI Contract](./uris.md)
Use these pages as the normative source for authored content layout, frontmatter schema, and canonical MCP URI semantics.
## Content Contract
This page defines the authored content contract for the docs-first MCP architecture.
## Canonical Source Of Truth
1. All authored Markdown lives under `docs/`.
2. MCP resources and static docs are two distribution surfaces of the same authored files.
3. No parallel authored markdown is allowed in `src/` or other package-only paths.
## Canonical Content Shape
Authored content is organized under `docs/`:
```mermaid
---
config:
treeView:
rowIndent: 20
lineThickness: 2
themeVariables:
treeView:
labelColor: '#FFFFFF'
lineColor: '#FFFFFF'
---
treeView-beta
"docs/"
"*.md (top-level docs pages)"
"contracts/"
"prompt.md"
"skill_contract.md"
"frontmatter.md"
"uris.md"
"prompts/"
"<prompt-id>/"
"PROMPT.md"
"skills/"
"<skill-id>/"
"SKILL.md"
"references/..."
```
## File Placement And Ownership Boundaries
1. Top-level project docs stay in `docs/*.md`.
2. Skill docs stay in `docs/skills/<skill-id>/...`.
3. Prompt docs stay in `docs/prompts/<prompt-id>/...`.
4. A skill or prompt may link across sections, but must not store content in another artifact's directory.
5. Server and runtime code may index and serve docs, but must not be the source of authored markdown.
## Delegated Contracts
1. Skill-specific directory, metadata, and id rules are defined in [Skill Contract](./skill_contract.md).
2. Prompt-specific directory, metadata, and id rules are defined in [Prompt Contract](./prompt.md).
## Invariants
This contract guarantees:
1. One authored source tree in `docs/` for both website and MCP.
2. Skill and prompt artifacts remain path-stable within their own sections.
3. Cross-surface publishing remains deterministic because authored content paths are canonical.
## Non-Goals
This contract does not define:
1. URI versioning policy details.
2. The full frontmatter schema.
3. Detailed skill rules (see [Skill Contract](./skill_contract.md)).
4. Detailed prompt rules (see [Prompt Contract](./prompt.md)).
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---
icon: lucide/messages-square
---
# Prompt Contract
This page defines the canonical contract for prompts in the docs-first MCP architecture.
## Canonical Prompt Shape
Each prompt is one directory under `docs/prompts/`:
```mermaid
---
config:
treeView:
rowIndent: 20
lineThickness: 2
themeVariables:
treeView:
labelColor: '#FFFFFF'
lineColor: '#FFFFFF'
---
treeView-beta
"docs/"
"... (other docs)"
"prompts/"
"<prompt-id>/"
"PROMPT.md"
"references/"
"... (one or more markdown files, optional nested folders)"
```
Rules:
1. `PROMPT.md` is required for every prompt.
2. `references/` is the only place for prompt-specific supporting docs.
3. Nested folders inside `references/` are allowed so a prompt can reorganize internals without changing global architecture.
4. Prompt directories are independent ownership boundaries; no cross-prompt file writes.
## Metadata Location Constraint
1. Prompt metadata is embedded in YAML frontmatter in `PROMPT.md`.
2. No `metadata.yaml` sidecar exists in the end state.
3. Reference lookup metadata is documented and explicit: top-level `references/*.md` are auto-discovered from filenames, while `PROMPT.md` frontmatter declares overrides and nested mappings when needed.
## Prompt Id Contract
`prompt-id` is the public identifier and should satisfy all rules below:
1. Format: lowercase kebab-case only.
2. Character set: `a-z`, `0-9`, and `-`.
3. Must start with a letter.
4. No underscores, spaces, dots, or uppercase characters.
5. Directory name should equal `prompt-id` in each committed revision.
6. Frontmatter `id` should equal directory name in each committed revision.
7. Treat `prompt-id` as immutable after release; any rename is a breaking replacement and clients must move to the new id.
Valid examples:
1. `pytest-fill-scaffold`
2. `review-pr-comments`
3. `scaffold-fastapi-service`
Invalid examples:
1. `fill_pytest_scaffold`
2. `Prompt-Template`
3. `docs.prompt`
## Direct Documentation Inclusion
1. For direct API documentation, use mkdocstrings directives rather than pasting large code blocks.
2. Keep manually-authored code examples short and task-focused; large implementation excerpts are out of scope for this contract.
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---
icon: lucide/brain-circuit
---
# Skill Contract
This page defines the canonical contract for skills in the docs-first MCP architecture.
## Canonical Skill Shape
Each skill is one directory under `docs/skills/`:
```mermaid
---
config:
treeView:
rowIndent: 20
lineThickness: 2
themeVariables:
treeView:
labelColor: '#FFFFFF'
lineColor: '#FFFFFF'
---
treeView-beta
"docs/"
"... (other docs)"
"skills/"
"<skill-id>/"
"SKILL.md"
"references/"
"... (one or more markdown files, optional nested folders)"
```
Rules:
1. `SKILL.md` is required for every skill.
2. `references/` is the only place for skill-specific supporting docs.
3. Nested folders inside `references/` are allowed so a skill can reorganize internals without changing global architecture.
4. Skill directories are independent ownership boundaries; no cross-skill file writes.
## Metadata Location Constraint
1. `SKILL.md` frontmatter contains only standard `name` and `description` fields.
2. No `metadata.yaml` sidecar or repository-specific skill metadata block exists.
3. The provider discovers supporting files recursively; their real relative paths are published in the generated `_manifest`.
## Skill Id Contract
`skill-id` is the public identifier and should satisfy all rules below:
1. Format: lowercase kebab-case only.
2. Character set: `a-z`, `0-9`, and `-`.
3. Must start with a letter.
4. No underscores, spaces, dots, or uppercase characters.
5. Directory name equals `skill-id` in each committed revision.
6. Frontmatter `name` equals the directory name.
7. Treat `skill-id` as immutable after release; any rename is a breaking replacement and clients must move to the new id.
Valid examples:
1. `fastapi-uv-docker`
2. `zensical-docs`
3. `pytesting`
Invalid examples:
1. `fastapi_uv_docker`
2. `Zensical-Docs`
3. `docs.zensical`
## Provider Publication
[`SkillsDirectoryProvider`](https://gofastmcp.com/servers/providers/skills) scans `docs/skills/` with `supporting_files="template"` and publishes:
1. `skill://<skill-id>/SKILL.md`
2. `skill://<skill-id>/_manifest`
3. `skill://<skill-id>/{path*}` for supporting files
Only the main file and manifest appear in `resources/list`. Clients inspect the manifest before reading supporting paths.
## Direct Documentation Inclusion
1. For direct API documentation, use mkdocstrings directives rather than pasting large code blocks.
2. Keep manually-authored code examples short and task-focused; large implementation excerpts are out of scope for this contract.
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---
icon: lucide/link
---
# URI Contract
This page defines the public resource URI contract for native skills, registry-backed prompts, and general authored documentation.
## Native Skill URIs
The [FastMCP Skills Provider](https://gofastmcp.com/servers/providers/skills) publishes each skill through the `skill://` scheme:
1. `skill://<skill-name>/SKILL.md`
2. `skill://<skill-name>/_manifest`
3. `skill://<skill-name>/<supporting-path>`
The first two are concrete resources returned by `resources/list`. Supporting files use a per-skill wildcard resource template when the provider is configured with `supporting_files="template"`:
```text
skill://<skill-name>/{path*}
```
### Main File
`skill://<skill-name>/SKILL.md` returns the canonical authored skill document. The skill directory name supplies `<skill-name>`, and the resource description comes from `SKILL.md` frontmatter.
### Manifest
`skill://<skill-name>/_manifest` returns JSON containing the skill name and every file beneath its directory. Each file entry includes:
1. relative POSIX path
2. byte size
3. SHA256 hash
Clients read the manifest before requesting supporting files. FastMCP client utilities such as `list_skills()` and `get_skill_manifest()` understand this contract directly.
### Supporting Files
Supporting files retain their real skill-relative paths. For example:
```text
skill://pytesting/references/pytest-docs.md
```
FastMCP confines reads to the selected skill directory. Absolute paths, traversal outside the directory, missing files, directories, and symlinks that resolve outside the skill root are rejected.
## Prompt And Docs URIs
Prompts and general documentation retain the existing registry-backed resource surface:
1. `resource://catalog/prompts_index`
2. `resource://catalog/prompts_index{?q,tag,cursor,limit}`
3. `resource://catalog/prompts/{prompt_id}`
4. `resource://prompts/{prompt_id}/document`
5. `resource://docs/{path*}`
Prompt ids remain lowercase kebab-case. The docs wildcard accepts normalized relative POSIX Markdown paths beneath `docs/` and rejects absolute paths, traversal segments, backslashes, and non-Markdown targets.
## Discovery Order
For skills:
1. list resources or call FastMCP `list_skills()`
2. select a skill by name and description
3. read `skill://<skill-name>/SKILL.md`
4. read `_manifest` when supporting material may be needed
5. fetch only the supporting paths relevant to the task
For prompts, use the prompt catalog or MCP prompt-object APIs.
## Compatibility Policy
The native `skill://` family directly replaces the repository's former custom skill URI and catalog surfaces. No compatibility aliases or dual registrations are maintained. Prompt and general-doc URIs are unaffected.
Skill renames are breaking because the directory name is part of every native skill URI. Supporting-file renames change the corresponding manifest path and URI.
## Sources
1. [FastMCP Skills Provider](https://gofastmcp.com/servers/providers/skills)
2. [MCP resources](https://modelcontextprotocol.io/specification/latest/server/resources)
3. [RFC 3986 URI syntax](https://www.rfc-editor.org/rfc/rfc3986)
4. [RFC 6570 URI templates](https://www.rfc-editor.org/rfc/rfc6570)
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## Purpose
This page explains how the GitHub Copilot extension in VS Code behaves as an MCP client when connected to `personal-mcp`, including why tools can appear while resource attachment appears unavailable.
This page explains how GitHub Copilot in VS Code consumes native skill resources from `personal-mcp`, including sessions where tools are visible but resource attachment is not.
## Core Model
## Capability Lanes
Copilot interacts with MCP servers through separate capability lanes:
Copilot interacts with MCP servers through independently exposed lanes:
1. tools (invoked by the model during execution)
2. resources (attached as read-only context)
3. prompts (server-provided prompt templates)
1. tools invoked during execution
2. resources attached as read-only context
3. server-provided prompts
These lanes are related but independently gated in the client.
This server publishes skills as native `skill://` resources, prompts through registry-backed resources and MCP prompt objects, and generic resource fallback tools through FastMCP.
Reliable paths are:
## Native Skill Resources
1. attach MCP resources explicitly through `Add Context > MCP Resources` or `MCP: Browse Resources`
2. let Copilot invoke MCP tools when the task and tool descriptions make that relevant
3. invoke MCP prompts explicitly with `/server.prompt` when your server exposes them
For every skill, Copilot can discover:
## What Actually Happens In VS Code
1. `skill://<name>/SKILL.md`
2. `skill://<name>/_manifest`
3. `skill://<name>/{path*}` supporting-file template
### MCP server side
The main resource description comes from `SKILL.md`. The manifest discloses supporting paths, sizes, and SHA256 hashes. This is the only skill discovery contract; there is no parallel skill catalog.
Your server can advertise resources and serve them correctly. In this project that includes catalog resources and skill document resources.
## Resource Picker Availability
### Copilot session side
`MCP Resources...` in Add Context requires both:
The chat surface exposes tools, resources, and prompts through different UI paths. In practice, you can encounter sessions where tool use is available but MCP resource attachment is not exposed in `Add Context`.
1. a connected server advertising resource capability
2. a chat surface that exposes MCP resource attachment
That is why you can sometimes see MCP tools before you see `Add Context > MCP Resources`.
A successful `resources/list` response does not guarantee the picker appears in every session type. Use `MCP: Browse Resources` to distinguish server availability from chat UI availability.
## Why The Picker Sometimes Shows Only Tools
## Recommended Workflow
`MCP Resources...` in Add Context requires at least:
When resource attachment is available:
1. at least one connected MCP server advertises resource capability
2. the current chat surface exposes MCP resource attachment
1. browse the server's resources
2. attach one relevant `skill://<name>/SKILL.md`
3. attach `_manifest` only if supporting detail may be needed
4. attach only selected supporting files
If the second condition is not met, resources can be available on the server while still being absent from the picker.
When only tools are available:
## Practical Workflow
1. call `list_resources`
2. select a native main skill URI by name and description
3. call `read_resource` for that URI
4. read `_manifest` and supporting files only as needed
Use this sequence to confirm behavior:
Both paths resolve through the same FastMCP provider.
1. run `MCP: Browse Resources` and verify resources exist
2. use `MCP: List Servers` to verify the server is enabled and running
3. open Copilot Chat
4. check `Add Context` for `MCP Resources...`
5. if still missing, restart the server and reload VS Code window
## Prompt Examples
## Recommended Usage Pattern
1. rely on canonical catalog resources for discovery (`skills_index`, then `skills/{skill_id}`)
2. fetch only selected skill documents for context
3. keep slash commands for deterministic fallback flows
When resource attachment is unavailable in the active session, use ResourcesAsTools first, then thin catalog discovery tools as parity fallback:
1. `list_resources`
2. `read_resource`
3. `search_patterns`
4. `get_pattern_by_id`
5. `get_skill_document_by_id`
Canonical naming policy:
1. Prefer the five canonical tool names above in prompts and instructions.
2. For compatibility with clients that emit `catalog_*` naming, the server also exposes:
- `catalog_search_patterns`
- `catalog_get_pattern_by_id`
- `catalog_get_skill_document_by_id`
3. Canonical and compatibility alias tools return equivalent payloads for the same input.
The first two are generated from the canonical resource surface and should be preferred in tool-only clients.
These should stay read-only, minimal, and schema-aligned with catalog resources.
For very large tool catalogs, server operators can optionally enable tool search mode (`regex` or `bm25`) while keeping `list_resources` and `read_resource` pinned as always-visible fallback tools.
## What To Type In Copilot Chat
Use prompts that tell Copilot which MCP feature path to take.
### If `MCP Resources...` is available
Use the resource attachment UI first, then ask Copilot to work from the attached material.
Example:
Resource attachment:
```text
I attached the catalog resources and the FastAPI async SQLAlchemy modernization skill document. Use that context to propose a migration plan for this repo.
Use the attached personal-mcp skill as guidance, then reconcile it with the repository before proposing changes.
```
If you want to keep the attachment sequence explicit, use:
Tool-only discovery:
```text
I attached personal-mcp catalog resources first. Use them to identify the best matching skill, then work only from the selected skill document.
Call list_resources, choose the best matching skill://.../SKILL.md resource, and read it. Inspect its _manifest only if a supporting file is needed. Load at most two candidate skills.
```
### If only tools are available
Ask Copilot to explicitly use resource-backed tools first.
Example resource-backed prompt:
Direct loading:
```text
Use personal-mcp tool fallback by first calling list_resources, then read_resource for resource://catalog/skills_index and the selected resource://skills/<skill-id>/document URI. Use only that loaded skill context in your answer.
Read skill://async-fastapi-sqlmodel/SKILL.md and apply only the sections relevant to this repository.
```
If needed, use the thin catalog tools.
Example discovery prompt:
Supporting material:
```text
Use the personal-mcp catalog tools to search for the most relevant skill for FastAPI async SQLAlchemy modernization. Then load the selected skill document and use it as context for your answer.
Read skill://pytesting/_manifest, select the one reference relevant to async test lifecycle, and use that file with the main skill instructions.
```
Example direct-load prompt:
## Repository Instruction Pattern
```text
Call get_skill_document_by_id for fastapi-async-sqlalchemy-modernization and use that document as the main context for this task.
```
Example bounded-selection prompt:
```text
Search personal-mcp skills for NiceGUI UI customization, select at most 2 strong matches, load the best skill document, and answer using only that material plus the workspace code.
```
## Repo Instructions Example
Repo instructions are the best place to teach Copilot when MCP content is relevant and which path to prefer.
If you add a repo-level `copilot-instructions.md`, keep the rule simple: prefer catalog-first discovery, keep loaded skill context small, and fall back to tools when resource attachment is unavailable.
Instructions can strongly steer behavior, but they do not guarantee that VS Code will auto-attach MCP resources for a request. For reliable resource use, either attach resources explicitly or prompt Copilot to use the fallback tools.
Example:
A repo-level instruction should name the native retrieval order and context budget:
```md
# MCP Usage
When a task matches a personal-mcp skill:
When a task may benefit from personal-mcp skills, use this sequence:
1. Start with personal-mcp catalog discovery when the task appears to match documented implementation patterns.
2. Prefer MCP resources when the chat surface exposes resource attachment.
3. If MCP resource attachment is unavailable, use `list_resources`/`read_resource` first, then thin catalog tools if needed.
4. Load only the most relevant skill document or at most 2 skill documents.
5. Treat skill documents as guidance, then reconcile them with the actual repository code before making changes.
Preferred discovery order:
1. `resource://catalog/skills_index`
2. `resource://catalog/skills/{skill_id}`
3. `resource://skills/<skill-id>/document`
4. `resource://skills/<skill-id>/references/<ref-id>` when needed
Tool fallback order:
1. `list_resources`
2. `read_resource`
3. `search_patterns`
4. `get_pattern_by_id`
5. `get_skill_document_by_id`
If confidence is low after catalog discovery, ask one clarifying question before loading more skill documents.
1. Prefer an already attached native skill resource.
2. Otherwise use `list_resources` and select one `skill://<name>/SKILL.md` resource by description.
3. Read `_manifest` only when supporting material is needed.
4. Load at most two candidate main files and only the relevant supporting paths.
5. Reconcile guidance with the current repository before editing.
```
That instruction style does two useful things:
Instructions steer behavior but do not force VS Code to attach resources automatically.
1. it tells Copilot to prefer the MCP server when relevant without forcing it on every prompt
2. it keeps context size bounded so skill loading does not become noisy or expensive
## Prompt Objects
If you want stronger behavior, add one more line that names the MCP server directly:
Prompt modules remain separate from skills. When the client supports MCP prompt APIs, use prompt listing and `get_prompt` for parameterized workflows. Authored `PROMPT.md` remains the source of truth for each prompt.
```md
Use the `personal-mcp` server for skill discovery whenever the task involves documented implementation patterns available from the catalog.
```
## Troubleshooting
## Known Gotcha
A successful `resources/list` response from the server does not guarantee the resource picker appears in every Copilot session type. UI availability is session-capability-dependent.
1. Use `MCP: List Servers` to confirm the server is enabled.
2. Use `MCP: Browse Resources` to confirm native skill resources exist.
3. Restart the MCP server after changing skill files because production uses `reload=False`.
4. Reload the VS Code window if the server is healthy but the resource picker remains stale.
5. In tool-only sessions, verify `list_resources` and `read_resource` are visible.
## Further Reading
### VS Code docs
1. [Add and manage MCP servers](https://code.visualstudio.com/docs/agent-customization/mcp-servers)
2. [MCP configuration reference](https://code.visualstudio.com/docs/agents/reference/mcp-configuration)
3. [Manage context for AI](https://code.visualstudio.com/docs/chat/copilot-chat-context)
4. [AI features cheat sheet](https://code.visualstudio.com/docs/agents/reference/ai-features-cheat-sheet)
### Project docs
1. [Resource-First Pattern Module Architecture](./architecture.md)
2. [Static Docs Hosting Pattern](./mcp_layout.md)
3. [Skill Usage Mechanics](./usage.md)
1. [FastMCP Skills Provider](https://gofastmcp.com/servers/providers/skills)
2. [Add and manage MCP servers](https://code.visualstudio.com/docs/agent-customization/mcp-servers)
3. [MCP configuration reference](https://code.visualstudio.com/docs/agents/reference/mcp-configuration)
4. [Manage context for AI](https://code.visualstudio.com/docs/chat/copilot-chat-context)
5. [Skill Usage Mechanics](./usage.md)
-338
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@@ -1,338 +0,0 @@
---
icon: lucide/braces
---
# Frontmatter Contract
This page defines the `SKILL.md` frontmatter and FastMCP metadata contract.
## Anthropic Frontmatter Support
Across Anthropic API and Agent Skills surfaces:
1. Required fields for custom skill bundles are `name` and `description`.
2. `name` must be 1-64 characters, lowercase letters, numbers, and hyphens only, with no XML tags, and must not use the reserved words `anthropic` or `claude`.
3. `description` must be 1-1024 characters, non-empty, and contain no XML tags.
Portable optional fields from the Agent Skills specification:
1. `license`
2. `compatibility`
3. `metadata`
4. `allowed-tools`
Claude Code-specific optional fields:
1. `when_to_use`
2. `argument-hint`
3. `arguments`
4. `disable-model-invocation`
5. `user-invocable`
6. `allowed-tools`
7. `disallowed-tools`
8. `model`
9. `effort`
10. `context`
11. `agent`
12. `hooks`
13. `paths`
14. `shell`
Repository contract decisions:
1. Treat `name` and `description` as required in all `SKILL.md` files.
2. Keep Anthropic-facing semantics in standard fields.
3. Keep MCP indexing metadata in a namespaced extension block.
4. Preserve forward compatibility by allowing additive optional metadata fields over time.
## Canonical Frontmatter Schema
Use this two-layer pattern:
1. Anthropic layer: top-level fields intended for Anthropic and Agent Skills behavior.
2. Repository layer: one namespaced block, `x-personal-mcp`, for MCP catalog and routing metadata.
Canonical shape:
```yaml
---
name: <skill-id>
description: <what this skill does and when to use it>
# Optional Anthropic and Agent Skills fields
when_to_use: <extra trigger guidance>
allowed-tools: <space-separated string or YAML list>
disable-model-invocation: false
user-invocable: true
license: <optional>
compatibility: <optional>
# Repository-specific metadata
x-personal-mcp:
id: <skill-id>
version: <semver>
tags:
- <tag>
capabilities:
- resource://skills/<skill-id>/document
depends_on: []
# Optional: overrides and nested references only.
# Top-level references/*.md are auto-discovered.
references:
<ref-id>:
path: references/<file>.md
mime_type: text/markdown
title: <short title>
---
```
## Repository Metadata Field Rules
Rules for `x-personal-mcp`:
1. `id` is required, must follow the skill id rules from the content contract, and must equal the directory name.
2. `version` is required and must be a semantic version string.
3. `tags` is optional and should be a list of kebab-case discovery labels.
4. `capabilities` is required and lists the MCP URIs the skill publishes.
5. `depends_on` is optional and lists other skill ids.
6. `references` is an optional map keyed by `ref-id` for overrides and nested entries.
Reference entry rules:
1. `ref-id` is lowercase kebab-case.
2. `path` is a skill-relative markdown path and must stay inside the same skill directory.
3. Top-level files under `references/*.md` are auto-discovered with `ref-id` derived from a normalized filename stem (lowercase kebab-case).
4. Nested folders under `references/` are not auto-discovered and must be declared explicitly.
5. `mime_type` defaults to `text/markdown` when omitted.
6. `title` is an optional display label.
7. Renaming `ref-id` values is allowed when needed; optional aliases may be used during transitions.
## Auto-Generated Reference IDs
Top-level markdown files directly under `references/` are auto-registered as MCP references even when `x-personal-mcp.references` is empty.
How `ref-id` is derived:
1. Start from the filename stem (without `.md`).
2. Normalize to lowercase kebab-case.
3. Publish at `resource://skills/<skill-id>/references/<ref-id>`.
Examples:
1. `references/ruff-docs.md` -> `ref-id: ruff-docs`
2. `references/Ruff Integrations.md` -> `ref-id: ruff-integrations`
3. `references/python_logging_docs.md` -> `ref-id: python-logging-docs`
When to use explicit `x-personal-mcp.references` entries:
1. The file is nested, for example `references/guides/ci.md`.
2. You need to override defaults (`title`, `mime_type`, or custom `ref-id`).
3. You need compatibility aliases during a rename.
## Validation Models
The normative model uses Pydantic v2 with change-friendly validation:
```python
from __future__ import annotations
import re
from pathlib import PurePosixPath
from typing import Any
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
SKILL_ID_RE = re.compile(r"^[a-z][a-z0-9-]*$")
SEMVER_RE = re.compile(r"^(0|[1-9]\d*)\.(0|[1-9]\d*)\.(0|[1-9]\d*)(?:[-+][0-9A-Za-z.-]+)?$")
class ReferenceEntry(BaseModel):
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
path: str
mime_type: str = "text/markdown"
title: str | None = None
@field_validator("path")
@classmethod
def validate_reference_path(cls, value: str) -> str:
p = PurePosixPath(value)
if p.is_absolute() or ".." in p.parts:
raise ValueError("reference path must be a relative in-skill path")
if not str(p).startswith("references/"):
raise ValueError("reference path must stay under references/")
if p.suffix.lower() != ".md":
raise ValueError("reference path must target a markdown file")
return str(p)
class PersonalMcpMetadata(BaseModel):
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
id: str
version: str
tags: list[str] = Field(default_factory=list)
capabilities: list[str] = Field(min_length=1)
depends_on: list[str] = Field(default_factory=list)
references: dict[str, ReferenceEntry] = Field(default_factory=dict)
@field_validator("id")
@classmethod
def validate_id(cls, value: str) -> str:
if not SKILL_ID_RE.fullmatch(value):
raise ValueError("id must be lowercase kebab-case and start with a letter")
return value
@field_validator("version")
@classmethod
def validate_version(cls, value: str) -> str:
if not SEMVER_RE.fullmatch(value):
raise ValueError("version must be semver")
return value
@field_validator("depends_on")
@classmethod
def validate_depends_on(cls, value: list[str]) -> list[str]:
for dep in value:
if not SKILL_ID_RE.fullmatch(dep):
raise ValueError(f"invalid depends_on skill id: {dep}")
return value
@field_validator("references")
@classmethod
def validate_reference_ids(cls, value: dict[str, ReferenceEntry]) -> dict[str, ReferenceEntry]:
for ref_id in value:
if not SKILL_ID_RE.fullmatch(ref_id):
raise ValueError(f"invalid reference id: {ref_id}")
return value
@model_validator(mode="after")
def ensure_primary_capability(self) -> "PersonalMcpMetadata":
expected = f"resource://skills/{self.id}/document"
if expected not in self.capabilities:
raise ValueError(f"capabilities must include {expected}")
return self
class SkillFrontmatter(BaseModel):
model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
name: str = Field(min_length=1, max_length=64)
description: str = Field(min_length=1, max_length=1024)
when_to_use: str | None = None
allowed_tools: str | list[str] | None = Field(default=None, alias="allowed-tools")
disallowed_tools: str | list[str] | None = Field(default=None, alias="disallowed-tools")
disable_model_invocation: bool | None = Field(default=None, alias="disable-model-invocation")
user_invocable: bool | None = Field(default=None, alias="user-invocable")
argument_hint: str | None = Field(default=None, alias="argument-hint")
arguments: str | list[str] | None = None
license: str | None = None
compatibility: str | None = None
metadata: dict[str, str] | None = None
x_personal_mcp: PersonalMcpMetadata = Field(alias="x-personal-mcp")
@field_validator("name")
@classmethod
def validate_name(cls, value: str) -> str:
if not SKILL_ID_RE.fullmatch(value):
raise ValueError("name must be lowercase kebab-case and start with a letter")
if "anthropic" in value or "claude" in value:
raise ValueError("name must not contain reserved words anthropic or claude")
return value
@model_validator(mode="after")
def cross_validate(self) -> "SkillFrontmatter":
if self.x_personal_mcp.id != self.name:
raise ValueError("x-personal-mcp.id must exactly match name")
return self
def validate_skill_frontmatter(raw: dict[str, Any], skill_dir_name: str) -> SkillFrontmatter:
model = SkillFrontmatter.model_validate(raw)
if model.name != skill_dir_name:
raise ValueError("frontmatter name must exactly match skill directory name")
return model
```
Validation behavior contract:
1. Validate required core fields and relationships during registry load before FastMCP resource or tool registration.
2. Allow unknown additive fields so frontmatter can evolve without blocking startup.
3. Treat hard contract violations, including missing required fields, invalid ids, and broken required mappings, as startup errors.
4. Treat non-critical compatibility issues as warnings when possible.
5. Error messages should include the skill path and failing field for CI readability.
Projection mode contract for Anthropic API upload pipelines:
1. Parse with `SkillFrontmatter` first.
2. Emit Anthropic-safe frontmatter with standard fields only.
3. Serialize repository metadata into standard `metadata` as namespaced keys.
4. Preserve the canonical authored source in `x-personal-mcp`; projection output is a build artifact.
## Anthropic Upload Compatibility Rule
1. Anthropic documentation guarantees behavior for standard frontmatter fields but does not explicitly guarantee handling of arbitrary unknown top-level keys.
2. Publishing pipelines that target strict API compatibility should support a projection mode that emits only standard frontmatter fields for upload.
3. In projection mode, repository extension metadata is serialized into the standard `metadata` field as namespaced keys or JSON-encoded values, while source-of-truth authoring remains in `x-personal-mcp`.
## FastMCP Native Metadata Surfaces
Resources support native definition metadata:
1. `name`
2. `description`
3. `mime_type`
4. `tags`
5. `annotations`, including `readOnlyHint` and `idempotentHint`
6. `icons`
7. `meta`
8. `version`
9. `enabled`, which is deprecated in FastMCP v3 in favor of server-level enable and disable controls
Resources also support runtime metadata through `ResourceContent.meta` and `ResourceResult.meta`.
Tools support native definition metadata:
1. `name`
2. `description`
3. `tags`
4. `annotations`, including `title`, `readOnlyHint`, `destructiveHint`, `idempotentHint`, and `openWorldHint`
5. `icons`
6. `meta`
7. `version`
8. `timeout`
9. `output_schema`
10. `run_in_thread`
11. `enabled`, which is deprecated in FastMCP v3 in favor of server-level enable and disable controls
Tools also support runtime metadata through `ToolResult.meta`.
## Frontmatter To FastMCP Mapping Contract
At server startup, map `x-personal-mcp` into FastMCP registration as follows:
1. `x-personal-mcp.id` defines the canonical URI namespace and identity checks.
2. `description` becomes the default description for the primary skill document resource.
3. `x-personal-mcp.tags` maps to resource and tool tags.
4. `x-personal-mcp.version` maps to resource and tool version metadata.
5. `x-personal-mcp.capabilities` becomes the registered URI list and catalog exposure.
6. `x-personal-mcp.references[*]` becomes resource templates or concrete resources with `mime_type`, read-only annotations, and `meta` that includes `skill_id`, `ref_id`, and source `path`.
7. `x-personal-mcp.depends_on` becomes catalog dependency graph metadata and validation inputs.
## Invariants
This contract guarantees:
1. Anthropic-required frontmatter stays valid for custom skill upload and Claude Code loading.
2. MCP-specific metadata remains embedded in `SKILL.md` frontmatter, with no `metadata.yaml` sidecar.
3. FastMCP registration uses native metadata fields for resources and tools.
4. Reference ids and metadata can evolve with low-friction updates while internal file layout under `references/` stays refactor-friendly.
## Non-Goals
This contract does not define:
1. URI versioning and deprecation rollout policy details.
2. Migration script design from existing `metadata.yaml` files.
3. Runtime caching and indexing performance tuning.
+6 -5
View File
@@ -22,10 +22,10 @@ Install dependencies first:
uv sync
```
Run the app locally with the static docs rebuilt first:
Run the app locally with the static docs rebuilt first, using [Uvicorn factory mode](https://www.uvicorn.org/settings/#application):
```bash
uv run zensical build && uv run uvicorn personal_mcp.main:app --host 127.0.0.1 --port 8765
uv run zensical build && uv run uvicorn personal_mcp.main:create_app --factory --host 127.0.0.1 --port 8765
```
Build and run the Docker image with the same exposed port:
@@ -39,9 +39,10 @@ When the server is running, the health check is available at `/healthz` and the
## Architecture
- [Resource-First Pattern Module Architecture](./architecture.md)
- [Content Contract](./content.md)
- [Frontmatter Contract](./frontmatter.md)
- [URI Contract](./uris.md)
- [Contracts](./contracts/index.md)
- [Content Contract](./contracts/index.md#content-contract)
- [Frontmatter Contract](./contracts/frontmatter.md)
- [URI Contract](./contracts/uris.md)
- [Static Docs Hosting Pattern](./mcp_layout.md)
- [Skill Usage Mechanics](./usage.md)
- [Copilot MCP Mechanics](./copilot.md)
+25
View File
@@ -0,0 +1,25 @@
window.MathJax = {
tex: {
inlineMath: [['\\(', '\\)']],
displayMath: [['\\[', '\\]']],
processEscapes: true,
processEnvironments: true
},
options: {
ignoreHtmlClass: '.*|',
processHtmlClass: 'arithmatex'
}
};
document$.subscribe(() => {
MathJax.startup.output.clearCache();
MathJax.typesetClear();
MathJax.texReset();
MathJax.typesetPromise();
});
component$.subscribe(({ ref }) => {
if (ref.classList.contains('md-annotation')) {
MathJax.typesetPromise([ref]);
}
});
+45 -60
View File
@@ -32,55 +32,35 @@ treeView-beta
"zensical.toml"
"docs"
"index.md"
"architecture.md"
"content.md"
"frontmatter.md"
"<project-docs>.md"
"contracts"
"index.md"
"<contract-pages>.md"
"mcp_layout.md"
"uris.md"
"prompts"
"<prompt-id>"
"PROMPT.md"
"references"
"skills"
"new-skill"
"SKILL.md"
"references"
"copilot-customization"
"SKILL.md"
"references"
"fastapi-async-sqlalchemy-modernization"
"SKILL.md"
"references"
"fastapi-uv-docker"
"SKILL.md"
"references"
"nicegui"
"SKILL.md"
"references"
"nicegui-ui-customization"
"SKILL.md"
"references"
"pytest-scaffolding"
"SKILL.md"
"references"
"python-logging-dictconfig"
"SKILL.md"
"references"
"vscode-configuration"
"SKILL.md"
"references"
"zensical-docs"
"<skill-id>"
"SKILL.md"
"references"
"<reference>.md"
"site"
"static build output"
"src"
"personal_mcp"
"__init__.py"
"main.py"
"mcp.py"
"web"
"app.py"
"docs_mount.py"
"catalog"
"server.py"
"<catalog-modules>.py"
"registry"
"<registry-modules>.py"
"web"
"<web-modules>.py"
"skills"
"document_loader.py"
"<skills-modules>.py"
```
Notes:
@@ -88,7 +68,8 @@ Notes:
1. docs contains both project-authored pages and the canonical skill Markdown tree.
2. site contains static build output only.
3. docs/skills contains canonical skill Markdown and reference Markdown.
4. MCP resources and docs site read from the same Markdown sources.
4. docs/prompts contains canonical prompt Markdown used for prompt catalog and document surfaces.
5. MCP resources and docs site read from the same Markdown sources.
## Runtime Composition
@@ -99,20 +80,22 @@ The runtime process serves two surfaces:
```mermaid
flowchart TD
A[Docs Registry Loader] --> B[Validated In-Memory Registry]
B --> C[FastMCP Resource Registration]
C --> D[MCP Transport]
C --> E[FastAPI Application]
E --> F[Static Mount /docs]
F --> G[Zensical site output directory]
A[Packaged Skill Directory] --> B[SkillsDirectoryProvider]
C[Packaged Prompts and Docs] --> D[Validated Registry]
B --> E[FastMCP Server]
D --> E
E --> F[MCP Transport]
E --> G[FastAPI Application]
G --> H[Static Mount /docs]
H --> I[Zensical Site Output]
```
Runtime guarantees:
1. Docs registry load and validation happen before resource exposure.
1. The skills provider and prompt/docs registry initialize before resource exposure.
2. Duplicate resource and template registration fails startup (`on_duplicate="error"`).
3. Resource registration is metadata-driven from SKILL frontmatter and reference manifests.
4. Legacy per-skill Python servers and `metadata.yaml` sidecars are not part of the runtime.
3. Skill resources come directly from `SkillsDirectoryProvider` directory discovery.
4. Legacy per-skill Python servers, custom skill catalogs, and metadata sidecars are not part of the runtime.
## Build and Publish Flow
@@ -139,23 +122,24 @@ MCP resources map directly to canonical Markdown documents.
Example mapping model:
1. docs/skills/<skill-id>/SKILL.md -> resource://skills/<skill_id>/document
2. docs/skills/<skill-id>/references/<file>.md -> resource://skills/<skill_id>/references/<ref_id> (via frontmatter references manifest)
1. docs/skills/<skill-id>/SKILL.md -> skill://<skill-id>/SKILL.md
2. docs/skills/<skill-id>/<path> -> skill://<skill-id>/<path>
3. docs/<path>.md -> resource://docs/{path*}
Catalog discovery resources are:
1. resource://catalog/skills_index
2. resource://catalog/skills/{skill_id}
1. resource://catalog/prompts_index
2. resource://catalog/prompts_index{?q,tag,cursor,limit}
3. resource://catalog/prompts/{prompt_id}
Registry-backed registration details:
Resource registration details:
1. `resource://skills/{skill_id}/document` resolves to each skill's SKILL.md.
2. `resource://skills/{skill_id}/references/{ref_id}` resolves through frontmatter reference manifests.
3. `resource://docs/{path*}` resolves normalized markdown paths under `docs/`.
4. Resource metadata includes explicit mime type and read-only/idempotent annotations.
1. `skill://<skill-id>/SKILL.md` resolves to each skill's main instructions.
2. `skill://<skill-id>/_manifest` lists every skill file with size and SHA256 hash.
3. Per-skill wildcard templates resolve validated supporting-file paths.
4. `resource://docs/{path*}` resolves normalized Markdown paths under `docs/`.
When clients cannot attach MCP resources directly, thin catalog tools may retrieve the same underlying skill documents indirectly. This does not create a second content source.
When clients cannot attach MCP resources directly, `ResourcesAsTools` exposes generic `list_resources` and `read_resource` tools over the same provider resources.
## URI Compatibility Policy
@@ -208,8 +192,9 @@ This keeps docs publication explicit and predictable.
Existing reference docs remain valid content inputs in this pattern:
1. docs/skills/pytest-scaffolding/references/pytest-docs.md
2. docs/skills/python-logging-dictconfig/references/python-logging-docs.md
3. docs/skills/fastapi-uv-docker/references/fastapi-best-practices.md
1. docs/skills/pytesting/references/pytest-docs.md
2. docs/skills/python-logging/references/python-logging-docs.md
3. docs/skills/python-logging/references/json-file-logging.md
4. docs/skills/fastapi-uv-docker/references/fastapi-best-practices.md
These are source documents, not deployment artifacts.
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---
name: authoring
description: Provide a practical checklist and baseline template for authoring docs-first MCP modules and repository-specific Copilot instruction shims.
x-personal-mcp:
id: authoring
version: 1.0.0
tags:
- authoring
- mcp
- fastmcp
- copilot
- prompts
- scaffolding
capabilities:
- resource://prompts/authoring/document
arguments:
artifact_type:
title: Artifact type
description: "Enum (case-sensitive): skill | prompt | shim."
required: true
artifact_id:
title: Artifact id
description: Lowercase kebab-case id for the module or shim.
required: true
goal:
title: Goal
description: One-sentence capability statement describing what to create and when to use it.
required: true
scope_glob:
title: Scope glob
description: Optional applyTo glob for shim outputs.
required: false
---
# Authoring Bootstrap
Use this prompt to author or update docs-first MCP modules in this repository, including repository-specific Copilot thin shims.
## Inputs
1. artifact_type: one of skill, prompt, shim
2. artifact_id: lowercase kebab-case id
3. goal: one-sentence capability statement
4. optional scope_glob for shim outputs
## Required References
Load only what matches the requested artifact:
1. Authoring workflow and validation policy: [Authoring Guide](../../authoring.md)
2. Prompt metadata and structure: [Prompt Contract](../../contracts/prompt.md)
3. Skill metadata and structure (only for skill outputs): [Skill Contract](../../contracts/skill_contract.md)
4. Thin shim mechanics and path binding: [Skill Usage Mechanics](../../usage.md)
5. Copilot resource attachment and fallback behavior: [Copilot MCP Mechanics](../../copilot.md)
## Workflow
1. Validate required inputs and ask one clarifying question if any required input is missing.
2. Keep ids and slugs aligned with folder names and frontmatter ids.
3. Enforce artifact_type enum values exactly: skill, prompt, shim.
4. If artifact_type is outside the enum, ask one correction question and stop before generating output.
5. Apply YAML safety rules for frontmatter values:
- quote values containing `:`
- prefer quotes for punctuation-heavy scalars
- use block scalars for multiline descriptions
6. Run immediate validation after frontmatter edits:
- `uv run zensical build`
- `uv run pytest -q`
7. Produce only the requested artifact type.
8. Keep guidance deterministic and minimal, with explicit references to source docs.
9. If artifact_type is shim:
- bind one applyTo scope to one `skill://<name>/SKILL.md` resource URI
- prefer MCP resource attachment first
- inspect the selected skill's `_manifest` only when supporting material is needed
- if resource attachment is unavailable, use the generic fallback tools:
1. list_resources
2. read_resource
10. Return created or updated file paths and any validation commands that should be run.
## Output Contract
Return:
1. Files created or updated.
2. Which references were used.
3. Validation commands and outcomes (or commands to run if execution is not requested).
@@ -0,0 +1,111 @@
---
name: greenfield-architecture
description: Research established patterns and design a high-level architecture for a new app or library with explicit tradeoffs and test strategy.
x-personal-mcp:
id: greenfield-architecture
version: 1.0.0
tags:
- architecture
- planning
- greenfield
- design
- testing
- prompts
capabilities:
- resource://prompts/greenfield-architecture/document
arguments:
scope_type:
title: Scope type
description: "Scope type: app or library."
required: true
intent_document:
title: Intent document
description: Optional full document describing goals, context, and desired outcomes.
required: false
problem_domain:
title: Problem domain
description: Domain and business goal for the new app or library when no full intent document is provided.
required: false
constraints:
title: Constraints
description: Runtime, deployment, and non-functional constraints.
required: false
---
# Greenfield Architecture Planner
Use this prompt to design a new software app or library architecture in generic terms.
## Inputs
1. intent_document: optional full document that explains goals, context, constraints, and desired outcomes
2. problem_domain: concise domain and one-sentence business goal when no full intent document is provided
3. scope_type: app or library
4. optional constraints: runtime, deployment, scale, non-functional priorities
If both intent_document and problem_domain are provided, treat intent_document as the primary source and use problem_domain as a summary cross-check.
## Workflow
1. Validate required inputs.
- scope_type is required
- at least one of intent_document or problem_domain must be provided
- ask one concise clarification question if inputs are incomplete or contradictory
2. Start with research before proposing architecture:
- identify at least three established patterns or methodologies used for similar systems
- summarize what each pattern optimizes for
- compare strengths, risks, and implementation complexity
3. Ask which aspects of those patterns matter most for the user context.
4. Identify major libraries or frameworks commonly used for this problem space and explain tradeoffs for each:
- strengths and weaknesses
- ecosystem maturity
- performance profile
- operational complexity
- learning curve
5. Recommend one primary stack and one fallback stack, with rationale tied to stated priorities.
6. Produce the architecture deliverables:
- high-level concepts, features, and requirements
- intended use cases and key workflows
- high-level package/module structure
- conceptual boundaries for each module (what belongs there and what does not)
- dependency and data-flow direction between modules
7. Plan incremental delivery with explicit growth paths:
- define the initial prototype slice with the smallest valuable feature set
- identify which features are intentionally deferred from the prototype
- describe extension paths that add complexity in controlled stages
- ensure each stage preserves clean module boundaries and low migration risk
8. Design a test strategy aligned to the proposed structure and staged delivery plan:
- unit, integration, contract, and end-to-end layers
- what each layer should cover in prototype stage vs extension stages
- fixture and environment setup for fast, deterministic tests
- boundary seams for mocks/fakes and minimization of nondeterministic external I/O
- CI execution approach for fast feedback and confidence
9. Call out key risks, assumptions, and open questions.
## Output Contract
Return these sections in order:
1. Research Summary
2. Pattern Comparison
3. Library and Framework Tradeoffs
4. Recommended Stack
5. Architecture Overview
6. Concepts, Features, and Requirements
7. Intended Use Cases
8. Package and Module Layout
9. Conceptual Boundary Map
10. Initial Prototype Scope
11. Extension Roadmap
12. Test Strategy
13. Risks and Open Questions
14. Next Implementation Steps
## Quality Rules
1. Keep language generic and project-agnostic.
2. Prefer established patterns over novelty unless there is a strong reason to diverge.
3. Tie each recommendation to an explicit requirement or tradeoff.
4. Make assumptions explicit and concise.
5. Ask one focused clarifying question when confidence is low instead of over-speculating.
6. Prefer architecture decisions that support starting simple and growing complexity without major rewrites.
@@ -0,0 +1,66 @@
---
name: jsfiddle-page-layout
description: Create a responsive sample page layout for a user-supplied domain and return paste-ready HTML and CSS for JSFiddle.
x-personal-mcp:
id: jsfiddle-page-layout
version: 1.1.0
tags:
- frontend
- html
- css
- jsfiddle
- layout
- prototyping
- prompts
capabilities:
- resource://prompts/jsfiddle-page-layout/document
---
# JSFiddle Page Layout
Create a polished sample page layout for the supplied domain. The result must run by pasting the markup and styles into the [JSFiddle](https://jsfiddle.net/) HTML and CSS panes.
## Inputs
1. `domain`: the product, service, organization, or subject represented by the page, including its intended audience when known
2. `layout_brief`: optional page type, required sections, content priorities, visual direction, or constraints
## Workflow
1. Infer the page's primary purpose, audience, content hierarchy, and most important user action from the inputs.
2. If the domain does not provide enough information to choose a useful page type or primary action, ask one concise clarification question before generating code.
3. Choose a visual direction and information density appropriate to the domain. Build the usable page itself, not a marketing explanation of the page.
4. Write semantic HTML with realistic domain-specific sample content. Do not use placeholder text such as lorem ipsum.
5. Build the layout with modern CSS, using [CSS Grid](https://css-tricks.com/complete-guide-css-grid-layout/) for two-dimensional page structure and [Flexbox](https://css-tricks.com/snippets/css/a-guide-to-flexbox/) for one-dimensional alignment where each fits naturally.
6. Make the page responsive at narrow mobile and desktop widths without horizontal overflow, overlapping content, or clipped text.
7. Keep the example self-contained. Use no JavaScript, build tools, external stylesheets, images, or icon libraries unless the layout brief explicitly requires them.
8. Include accessible landmarks, heading order, labels, focus styles, color contrast, and reduced-motion handling when animation is present.
9. Use CSS custom properties for the color, typography, spacing, border, and shadow system. Avoid generic framework styling and tailor the visual language to the domain.
## Design References
Use these references as comparative guidance, not as templates to copy. Select principles that fit the domain and layout brief, and do not reproduce a vendor's visual language unless the user requests it.
1. [Material Design 3 foundations](https://m3.material.io/foundations) for current approaches to layout, interaction states, design tokens, and adaptable UI systems.
2. [Apple Human Interface Guidelines](https://developer.apple.com/design/human-interface-guidelines/) for contemporary principles covering hierarchy, typography, controls, and platform-aware interaction.
3. [web.dev responsive web design basics](https://web.dev/articles/responsive-web-design-basics) for content-led breakpoints, flexible layouts, and input-aware responsiveness.
4. [Web Content Accessibility Guidelines (WCAG) 2.2](https://www.w3.org/TR/WCAG22/) as the accessibility baseline for structure, contrast, focus, reflow, and target sizing.
## Output Contract
Return exactly two fenced code blocks in this order:
1. An `html` block containing only the content for JSFiddle's HTML pane.
2. A `css` block containing only the content for JSFiddle's CSS pane.
Do not include setup instructions, design commentary, JavaScript, or prose outside the two code blocks.
## Quality Rules
1. Prefer semantic elements such as `header`, `nav`, `main`, `section`, `article`, `aside`, and `footer` when they match the content.
2. Reserve large display type for a true hero or primary page title; keep operational interfaces compact and easy to scan.
3. Use cards only for repeated items or genuinely framed tools. Do not place cards inside cards.
4. Use stable responsive constraints for grids, controls, media, and navigation so dynamic content does not shift the layout unexpectedly.
5. Avoid decorative gradients, floating color blobs, excessive rounding, and one-note palettes unless they are explicitly appropriate to the domain.
6. Ensure controls look and behave like their purpose, with visible hover and keyboard-focus states.
7. Keep all visible copy relevant to the fictional domain rather than describing the mockup or its implementation.
@@ -0,0 +1,106 @@
---
name: mcp-consumer-repo-shim
description: Create one repository-specific thin shim instruction file that binds a file scope to a user-selected Personal MCP skill resource and enforces resource-first Copilot retrieval behavior.
x-personal-mcp:
id: mcp-consumer-repo-shim
version: 1.0.0
tags:
- copilot
- mcp
- instructions
- shims
- prompts
capabilities:
- resource://prompts/mcp-consumer-repo-shim/document
arguments:
apply_to_glob:
description: File glob scope for the shim applyTo field, such as tests/** or **/*.md.
required: true
primary_skill_resource:
description: Primary native skill resource URI in the form skill://<skill-name>/SKILL.md.
required: true
shim_title:
description: Human-readable name for the instruction shim frontmatter.
required: false
companion_docs_page:
description: Optional relative docs link for human-facing companion guidance.
required: false
---
# MCP Consumer Repository Shim
Use this prompt to generate exactly one repository-scoped Copilot instruction shim for an MCP consumer repository.
## Inputs
- Required:
- apply_to_glob
- primary_skill_resource
- Optional:
- shim_title
- companion_docs_page
## Required References
Load only sections relevant to the requested shim:
1. Thin shim pattern and scope guidance: [Skill Usage Mechanics](../../usage.md)
2. VS Code Copilot MCP behavior and fallback mechanics: [Copilot MCP Mechanics](../../copilot.md)
3. Authoring workflow and validation checklist: [Authoring Guide](../../authoring.md)
4. Instruction metadata expectations and examples: [Copilot customization skill](../../skills/copilot-customization/SKILL.md)
## Workflow
1. Validate that apply_to_glob and primary_skill_resource are present.
2. Validate that primary_skill_resource uses the `skill://<skill-name>/SKILL.md` form.
3. If either value is missing or ambiguous, ask exactly one clarifying question before generating output.
4. Generate one .instructions.md file content block only.
5. Keep the shim concise and deterministic:
- include YAML frontmatter with name, description, and applyTo
- include a primary rule that uses the selected primary_skill_resource first
- include a bounded execution pattern (load primary doc, apply only relevant sections, keep edits minimal)
6. Include VS Code/Copilot integration mechanics in the shim body:
- prefer MCP resource attachment when available
- inspect `_manifest` only when the task needs supporting material
- if attachment is unavailable, use the generic fallback tools:
1. list_resources
2. read_resource
- ask one clarifying question when confidence is low
7. If companion_docs_page is provided, include it as a companion docs link line.
8. Do not generate additional files, code changes, or batch shim packs.
## Output Format
Return exactly:
1. Suggested file path line under .github/instructions/.
2. One fenced markdown block containing the full .instructions.md content.
3. A brief note (max 3 lines) describing what the shim routes and why.
## Output Template
````md
Path: .github/instructions/<slug>.instructions.md
```md
---
name: <shim-title>
description: Route <scope> edits to the Personal MCP <skill-id> resource.
applyTo: '<apply_to_glob>'
---
When editing files matching <apply_to_glob>, use <primary_skill_resource> as the primary guidance source.
Execution pattern:
1. Load the primary skill document first.
2. Apply only sections relevant to the file being edited.
3. Keep edits minimal and aligned with repository conventions.
4. Prefer MCP resource attachment when available in the current chat surface.
5. Read the selected skill's `_manifest` only when supporting material is needed.
6. If MCP resource attachment is unavailable, use `list_resources` and `read_resource`.
7. If confidence is low, ask one clarifying question before editing.
Companion docs page: <optional-relative-doc-link>
```
````
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---
name: nicegui-component-extraction
description: Extract a user-selected component from a JSFiddle page layout and implement it as a reusable NiceGUI render function with responsive styling and typed bindable state where needed.
x-personal-mcp:
id: nicegui-component-extraction
version: 1.0.0
tags:
- nicegui
- components
- frontend
- refactoring
- jsfiddle
- prompts
capabilities:
- resource://prompts/nicegui-component-extraction/document
arguments:
component:
title: Component
description: Component or page region to extract, identified by its visible label, semantic role, or selector.
required: true
source_layout:
title: Source layout
description: Optional HTML and CSS from the JSFiddle page layout prompt; when omitted, use the latest applicable output in the conversation.
required: false
target_location:
title: Target location
description: Optional target NiceGUI page, module, or package in which to create and integrate the component.
required: false
behavior_requirements:
title: Behavior requirements
description: Optional interactions, state, callbacks, or content variations the extracted component must support.
required: false
---
# NiceGUI Component Extraction
Extract one user-selected component from the output of the [JSFiddle Page Layout](../jsfiddle-page-layout/PROMPT.md) prompt and implement it as a reusable NiceGUI component in the target repository.
## Inputs
1. `component`: required visible label, semantic role, or selector identifying the component to extract
2. `source_layout`: optional HTML and CSS; when omitted, use the latest applicable JSFiddle page layout output in the conversation
3. `target_location`: optional target page, module, or package; infer it from the repository when omitted
4. `behavior_requirements`: optional interactions, state, callbacks, or content variations
If the selected component or source layout cannot be identified unambiguously, ask one concise clarification question before editing.
## Required References
Apply both references before implementation:
1. Component boundaries, responsive layout, Quasar props, Tailwind utilities, and shared CSS: [NiceGUI Page Layout and Styling](../../skills/nicegui/references/architecture-and-styling.md)
2. Typed UI state, propagation, mutable defaults, binding strictness, and version checks: [Binding Dataclasses Deep Dive](../../skills/nicegui/references/binding-dataclasses.md)
## Workflow
1. Locate the selected region in the source HTML and CSS, including its responsive rules, states, and dependencies on surrounding layout.
2. Inspect the target repository's NiceGUI version, package structure, component conventions, shared CSS loading, and nearest page call site.
3. Define the smallest reusable API for the component:
- name the public function `render_<component_name>` using snake_case
- accept content, typed state, and event callbacks as explicit parameters
- keep business rules, persistence, and service access outside the component
- preserve an established return-value convention; otherwise return the component's root NiceGUI element
4. Translate semantic HTML into native NiceGUI and Quasar elements. Do not embed the original page wholesale with `ui.html` when standard components express the structure.
5. Recreate only the CSS needed by the extracted component:
- use Quasar props for component appearance and behavior
- use NiceGUI classes and Tailwind utilities for spacing, sizing, alignment, and responsive layout
- use scoped shared CSS only where props and utilities are insufficient
- do not override Quasar field internals or duplicate globally loaded styles
6. Model editable or shared component state with a typed `@binding.bindable_dataclass` only when binding improves the interaction:
- use `field(default_factory=...)` for mutable defaults
- scope state to the appropriate page, client, or user
- keep binding transforms pure and inexpensive
- assign updated collections back to bound fields instead of relying on in-place mutation
7. Integrate the render function at the nearest target page or call site without moving unrelated page composition or domain logic into the component.
8. Preserve accessibility, focus behavior, text wrapping, stable dimensions, and the source layout's visual hierarchy.
9. Run the narrowest available tests, lint, and type checks for the changed files. For visual components, verify representative mobile, landscape desktop, and portrait desktop viewports when browser tooling is available.
## Output Contract
Complete the implementation in the target repository, then report:
1. Files created or updated.
2. The `render_*` function signature and its state or callback contract.
3. Any deliberate visual or interaction differences from the JSFiddle source.
4. Validation commands and outcomes, including viewport checks when performed.
## Quality Rules
1. Extract exactly the requested component and its necessary local dependencies.
2. Prefer the target repository's established patterns over introducing a new abstraction style.
3. Keep the component presentation-focused and reusable across pages with compatible data.
4. Do not add a bindable dataclass for static content or event-local state that is clearer as ordinary parameters.
5. Do not create a second component tree for mobile; use responsive classes and stable layout constraints.
6. Keep custom CSS tokenized, scoped to the component, and loaded once by the application's composition layer.
@@ -0,0 +1,90 @@
---
name: pytest-fill-scaffold
description: Fill scaffolded pytest test methods with assertions, fixtures, and minimal test data while preserving concise test names and one-line intent docstrings.
x-personal-mcp:
id: pytest-fill-scaffold
version: 1.0.0
tags:
- pytest
- testing
- scaffolding
- prompts
capabilities:
- resource://prompts/pytest-fill-scaffold/document
arguments:
target_files:
description: Target test file paths under tests/.
required: true
stack:
description: Runtime stack type for fixture and marker choices.
required: true
strategy:
description: Balance between minimal and comprehensive implementation.
required: false
marker_lane:
description: Preferred marker lane when applicable.
required: false
---
# Pytest Fill Scaffold
Use this prompt after test scaffolding exists and method names/docstrings are already in place.
## Inputs
- Target test file(s) under tests/.
- Stack type:
- pure-python
- fastapi
- sqlalchemy-sync
- sqlalchemy-async
- mixed
- Optional constraints:
- keep implementation minimal vs comprehensive
- marker lane target (unit, integration, smoke)
## Required References
Load these in order and use only what matches the task:
1. Core defaults: [pytest scaffolding skill](../../skills/pytesting/SKILL.md)
2. Naming/hierarchy preservation: [naming and organization](../../skills/pytesting/references/naming-and-organization.md)
3. Baseline pytest fixtures/markers: [pytest docs notes](../../skills/pytesting/references/pytest-docs.md)
4. FastAPI-specific behavior (only when needed): [fastapi testing](../../skills/pytesting/references/fastapi-testing.md)
5. SQLAlchemy-specific behavior (only when needed): [sqlalchemy testing](../../skills/pytesting/references/sqlalchemy-testing.md)
## Workflow
1. Inspect target files and treat human-reviewed docstring-only scaffolds as invariant.
2. Convert each scaffolded method into an executable test with a single behavior focus.
3. Keep one-line docstrings for class and method intent.
4. Add or refine fixtures at the nearest useful scope:
- global in tests/conftest.py only when broadly reusable
- subtree conftest.py for domain-specific fixtures
5. Assign markers consistent with cost and dependencies:
- unit for pure logic
- integration for framework/DB contracts
- smoke for thin critical-path checks
6. Validate in this order:
- uv run pytest --collect-only -q
- uv run pytest -m unit -q when unit tests are touched
- uv run pytest -q if dependencies are available
## Authoring Rules
- Prefer deterministic tests and explicit setup/teardown.
- Keep assertions precise and readable.
- Do not overfit tests to private implementation details.
- If a scaffolded class or method has only a docstring body, treat its name and hierarchy as locked.
- Do not rename, move, merge, split, or re-nest docstring-only scaffolded tests unless explicitly requested.
- Preserve existing one-line docstrings on scaffolded classes and methods unless they are factually incorrect.
- If stack details are missing and would change fixture strategy, ask one concise clarifying question before editing.
## Output Format
Return:
1. Files updated.
2. Fixture and marker decisions.
3. Which references were used and why.
4. Validation command results.
5. Risks or open questions.
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---
name: pytest-scaffold
description: Plan and optionally scaffold pytest file and class structure for selected Python modules while preserving concise behavior-focused test names and one-line intent docstrings.
x-personal-mcp:
id: pytest-scaffold
version: 1.0.0
tags:
- pytest
- testing
- scaffolding
- prompts
capabilities:
- resource://prompts/pytest-scaffold/document
arguments:
target_modules:
description: Target module path(s) under src/.
required: true
mode:
description: Execution mode, either plan-only or scaffold.
required: true
path_strategy:
description: Optional mapping preference for src to tests paths.
required: false
naming_style:
description: Optional preference for concise method naming style.
required: false
---
# Pytest Scaffold
Use this prompt to consistently plan and scaffold pytest test modules for selected Python source modules.
## Inputs
- Required:
- target_modules: one or more module paths under src/
- mode: one of plan-only or scaffold
- Optional:
- path_strategy: preference for how source paths map into tests/
- naming_style: preference for concise method naming style
## Required References
Load these in order and apply only the relevant sections:
1. Primary conventions: [Pytesting Skill](../../skills/pytesting/SKILL.md)
2. Hierarchy and naming: [Naming and Organization](../../skills/pytesting/references/naming-and-organization.md)
3. Marker and fixture defaults: [Pytest Docs Notes](../../skills/pytesting/references/pytest-docs.md)
## Workflow
1. Inspect the current tests/ layout and infer existing naming and grouping conventions.
2. Propose a concise hierarchy plan first:
- test file paths
- class hierarchy
- method naming pattern
- fixture placement choices (tests/conftest.py or subtree conftest.py)
3. If mode is scaffold, implement only the scaffold structure:
- create missing test modules
- create class hierarchy
- add one-line docstrings to each class and test method
- keep test method names short and behavior-focused
4. Treat docstring-only scaffolds as an intentionally stable baseline for later fill-in work.
5. Validate collection with:
- uv run pytest --collect-only -q
6. Report outcomes:
- files created or updated
- collection result
- ambiguities and follow-up choices
## Naming Defaults
- Class naming:
- Test<PrimarySubject> as a top-level subject class
- nested Test<MethodOrArea> classes where extra context improves readability
- Test<FunctionName> top-level classes for standalone module functions
- Method naming:
- test_<short_outcome>
- one behavior target per method
- one-line docstring for full intent
## Authoring Rules
1. Keep scope focused on structure and naming in this prompt.
2. Do not fill test implementation details unless explicitly requested.
3. Preserve established repository conventions when they are already present.
4. If input constraints conflict, ask one concise clarifying question before editing.
## Output Contract
Return:
1. Discovery summary and references used.
2. Proposed or applied test tree.
3. Class and method naming map.
4. Validation command result.
5. Open questions only when they block completion.
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---
name: async-fastapi-sqlmodel
description: 'Explain and apply async database principles for FastAPI, SQLAlchemy 2.x, and SQLModel. Use when: learning or reviewing cached AsyncEngine and session-factory lifecycles, AsyncSession scopes and injection, FastAPI lifespan and yield dependencies, transaction boundaries, concurrency safety, implicit ORM I/O, pooling, testing, or SQLModel integration.'
---
# Async FastAPI, SQLAlchemy, and SQLModel
Use this skill to explain how an async database layer works, why the recommended patterns exist, and how to evaluate code against them. Teach the runtime model before suggesting implementation changes.
Primary targets: PostgreSQL with asyncpg and SQLite with aiosqlite.
Engine and session mechanics mirror the [`nicegui-db` template repository](https://forgejo.john-stream.com/john/nicegui-db). Treat that template as the implementation baseline, then explain the rationale, lifecycle constraints, and tradeoffs behind its cached engines, session factories, context managers, dependency wiring, and `with_session` decorator. Source-specific claims in the references link to the reviewed template commit so behavior remains auditable as the template evolves.
## When to Use
- Explain an async engine, session factory, session, connection, or transaction.
- Review FastAPI lifespan or dependency-based database management.
- Diagnose shared-session concurrency, implicit I/O, cleanup, or transaction problems.
- Compare SQLModel's model conveniences with SQLAlchemy's async runtime APIs.
- Decide whether a context manager, `AsyncExitStack`, eager loading, pooling option, or explicit transaction is appropriate.
## Outcome
Produce a focused technical explanation that:
- Defines the objects involved and identifies who owns each one.
- Traces acquisition, use, transaction behavior, and cleanup.
- Separates required invariants from defaults and situational choices.
- Explains failure modes and concurrency consequences.
- Uses a minimal canonical pattern when code clarifies the mechanics.
- Links claims to the relevant reference and upstream documentation.
Do not default to producing a project plan. Give sequencing advice only when the user explicitly asks for implementation steps.
## Mental Model
Keep three ownership scopes distinct:
| Scope | Object | Purpose | Typical owner |
|---|---|---|---|
| Application process | Cached `AsyncEngine` and lifespan-owned `async_sessionmaker` | Dialect, connection pool, schema initialization, and repeatable session configuration | FastAPI lifespan |
| Request or concurrent task | `AsyncSession` | Mutable ORM identity map and transactional state | A `yield` dependency or explicit unit of work |
| Atomic operation | `SessionTransaction` | Commit all changes together or roll them back together | Service or use-case boundary |
The engine is a long-lived factory and pool, not a single database connection. The session is a mutable unit-of-work object, not a concurrency-safe global. A transaction is a consistency boundary, not merely a call to `commit()`.
## Core Principles
### Match lifetime to ownership
- Resolve one cached `AsyncEngine` per database URL during the active application lifecycle.
- Enter one owning engine scope per URL; initialize registered SQLModel metadata by default, then dispose the engine and clear cached resolution on exit.
- Configure the application `async_sessionmaker` inside the engine lifecycle; use the template's cached factory resolver only for standalone helpers that cannot receive the application factory.
- Close each session deterministically with `async with` or a FastAPI dependency that yields once.
See [engine lifecycle](references/engine.md) and [session management](references/session.md).
### Isolate mutable session state
An `AsyncSession` represents one stateful transaction in progress. Never use one session in multiple concurrent tasks, including branches of `asyncio.gather()`. Give each task its own session. Template-style `@with_session` functions inject one only when the `session` argument is omitted; a supplied session remains caller-owned, and explicit `None` is forwarded unchanged.
See [session management](references/session.md).
### Make I/O visible
Async ORM code must not unexpectedly issue SQL during ordinary attribute access. Load relationships and deferred columns explicitly with eager loader options such as `selectinload()`, use `awaitable_attrs` or `refresh()` for deliberate fallback loading, and consider `lazy="raise"` where accidental access should fail fast. `expire_on_commit=False` is a common async configuration because post-commit expiration can otherwise turn attribute reads into implicit I/O.
See [implicit ORM I/O](references/implicit_io.md).
### Put transactions around business invariants
Use `async with session.begin():` when several operations must commit or roll back as one unit. A successful exit flushes and commits; an exception rolls back. Reads still participate in SQLAlchemy's autobegin behavior unless the connection uses true DBAPI autocommit, so describe a path as read-only because of application intent and permissions, not because a session silently has no transaction.
Use `begin_nested()` only for a real SAVEPOINT requirement and account for backend-specific behavior. In SQLAlchemy 2.x, calling `session.commit()` commits the outermost transaction, not the current savepoint.
See [transaction boundaries](references/transactions.md).
### Keep framework boundaries explicit
FastAPI lifespan owns resources shared by many requests. A dependency with one `yield` owns request-scoped resources and runs cleanup after use. These are related context-manager mechanisms but solve different lifetime problems.
Use `AsyncExitStack` when lifespan acquires a variable, conditional, or mixed collection of context-managed resources. It records cleanup as resources are acquired and unwinds callbacks in reverse order. A single engine should use the direct engine context manager; `AsyncExitStack` is a composition tool, not a requirement.
See [FastAPI database integration](references/fastapi.md).
### Use SQLModel as the primary modeling layer
Default to SQLModel for table models and API data models in FastAPI applications. A SQLModel table model is also a SQLAlchemy model, and every SQLModel model is also a Pydantic model, so shared base models can reduce schema duplication while preserving access to SQLAlchemy's full ORM.
SQLModel does not replace SQLAlchemy's async engine, session, transaction, or loader mechanics. Its main tutorial currently demonstrates synchronous sessions and its advanced guide still lists comprehensive async documentation as future work. For async applications, combine SQLModel models and statements with SQLAlchemy's `AsyncSession` APIs. Use SQLAlchemy declarative models only when a concrete unsupported mapping or library constraint justifies the exception.
See [SQLModel integration](references/sqlmodel.md).
### Configure from evidence
Pool sizing, overflow, recycle, pre-ping, isolation, statement timeouts, and health checks depend on the driver, database, deployment concurrency, and failure model. Explain defaults and tradeoffs before recommending values. Avoid treating pool checkout as proof that a useful query can succeed.
See [observability and resilience](references/observability.md).
### Test through the production seam
Keep the production engine and session-factory construction path intact in tests. Select a dedicated PostgreSQL, local SQLite, or in-memory SQLite URL at that seam, then override the request-session dependency only for the test lifetime. Use a test-scoped outer transaction with SAVEPOINT-backed session commits when application code calls `commit()`; it exercises normal transaction behavior while cleanup remains deterministic.
In-memory SQLite is suitable for serial tests. For multiple simultaneous sessions, use a named shared-cache SQLite URL or a temporary file, and retain PostgreSQL integration coverage for PostgreSQL-specific behavior.
See [database testing and fixture data](references/testing.md).
## Reference Map
| Concept | Reference |
|---|---|
| Engine lifecycle and ownership | [Engine lifecycle reference](references/engine.md) |
| Session factory and scope | [Session management reference](references/session.md) |
| Transaction boundaries | [Transaction boundaries reference](references/transactions.md) |
| FastAPI lifespan composition | [FastAPI integration reference](references/fastapi.md) |
| FastAPI dependency injection | [FastAPI integration reference](references/fastapi.md) |
| Implicit I/O control in ORM | [Implicit I/O reference](references/implicit_io.md) |
| Observability and resilience | [Observability reference](references/observability.md) |
| SQLModel-first modeling | [SQLModel integration reference](references/sqlmodel.md) |
| CRUD repository and standalone functions | [Basic CRUD reference](references/crud.md) |
| Test database selection and fixture data | [Database testing reference](references/testing.md) |
## Canonical Composition Pattern
The framework-independent primitives live in [engine lifecycle](references/engine.md), [session management](references/session.md), and [transaction boundaries](references/transactions.md). Their canonical FastAPI adaptation, including lifespan state and `Annotated` dependencies, lives in [FastAPI database integration](references/fastapi.md).
For background work that outlives a request, inject the shared factory and create a new session inside that task instead of retaining the request's session.
## Explanation Procedure
1. Identify the exact concept or observed behavior in question.
2. Name the owning scope: application, request/task, or transaction.
3. Trace what state the object holds and where actual database I/O can occur.
4. Explain normal entry, successful exit, exceptional exit, and concurrent use.
5. Distinguish an invariant from a recommended default or backend-specific choice.
6. Load only the matching reference documents and cite upstream sources.
7. Show the smallest useful code pattern or contrast when prose is insufficient.
8. End with concrete checks the reader can use to inspect their own code.
When reviewing code, verify:
- The URL uses an asyncio-compatible dialect.
- Engine creation and disposal have one clear owner.
- Every session has a bounded lifetime and is not shared across tasks.
- Transaction boundaries match business invariants and exception behavior.
- Relationship and deferred-column access cannot surprise the event loop with implicit I/O.
- Pool and timeout settings are justified by deployment behavior.
- Tests exercise rollback, cleanup, concurrency, and lifespan behavior where relevant.
- Tests use a dedicated database target and preserve production session mechanics.
## Anti-Patterns to Flag
- Creating engines inside request handlers.
- Sharing one AsyncSession across concurrent tasks.
- Implicit commit/rollback behavior with unclear ownership.
- Global mutable session state.
- Lifespan cleanup that depends on implicit garbage collection.
- Treating `AsyncExitStack` as mandatory for a fixed single resource.
- Treating SQLModel's synchronous tutorial examples as the async runtime pattern.
- Allowing lazy relationship access to hide database I/O.
- Copying pool settings without relating them to worker count and database capacity.
## Output Contract
Answer in the shape best suited to the question, usually:
1. Direct explanation.
2. Underlying lifecycle or transaction mechanics.
3. Required invariants and situational tradeoffs.
4. Minimal example or code-review findings when useful.
5. Verification questions and source links.
## References
!!! info "Primary sources"
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy transaction management](https://docs.sqlalchemy.org/en/21/orm/session_transaction.html)
- [FastAPI lifespan events](https://fastapi.tiangolo.com/advanced/events/)
- [FastAPI dependencies with `yield`](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/)
- [Python `AsyncExitStack`](https://docs.python.org/3/library/contextlib.html#contextlib.AsyncExitStack)
- [SQLModel session dependency pattern](https://sqlmodel.tiangolo.com/tutorial/fastapi/session-with-dependency/)
@@ -0,0 +1,294 @@
# Basic CRUD Repository and Functions
!!! info "Primary sources"
- [SQLModel create-data tutorial](https://sqlmodel.tiangolo.com/tutorial/fastapi/multiple-models/)
- [SQLModel update-data tutorial](https://sqlmodel.tiangolo.com/tutorial/fastapi/update-extra-data/)
- [SQLModel select tutorial](https://sqlmodel.tiangolo.com/tutorial/select/)
- [SQLAlchemy `AsyncSession` API](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html#sqlalchemy.ext.asyncio.AsyncSession)
- [`nicegui-db` service functions](https://forgejo.john-stream.com/john/nicegui-db/src/commit/126bc26ad8635a86bacf684d7bda409230347597/src/nicegui_db/services/my_table.py)
??? abstract "Decision metadata"
- Status: adopted
- Decision level: advisory
- Applies to: api-runtime, workers, tests
- Last reviewed: 2026-08-06
---
## Purpose
Show a small SQLModel CRUD layer in two forms:
- independent functions for convenient standalone or composed operations;
- a repository object that groups those functions behind one domain-oriented interface.
Template-style public functions use `@with_session` and accept an optional `AsyncSession`. When the argument is omitted, the decorator resolves the cached session factory and owns a short-lived session. When supplied, the function borrows the session without controlling its lifetime or transaction. The decorator does not commit, so standalone writes need a visible transaction strategy; repository methods remain explicit-session operations for predictable composition.
Use the same vocabulary at every layer:
| Operation | Function | Repository method | Scope when session is omitted | Missing-row result |
|---|---|---|---|---|
| Create | `create_widget()` | `create()` | Owned session; no implicit commit | Not applicable |
| Read one | `get_widget()` | `get()` | Owned session | `None` |
| Read many | `list_widgets()` | `list()` | Owned session | Empty list |
| Update | `update_widget()` | `update()` | Owned session; no implicit commit | `None` |
| Delete | `delete_widget()` | `delete()` | Owned session; no implicit commit | `None` |
Functions and repository methods both put domain arguments first. Database configuration and sessions are keyword-only infrastructure arguments. This keeps call sites analogous and makes ownership choices visible.
---
## Models
Start with one table model when the application does not need distinct persistence and API schemas.
```python
from sqlmodel import Field
from sqlmodel import SQLModel
class Widget(SQLModel, table=True):
id: int | None = Field(default=None, primary_key=True)
name: str = Field(index=True)
description: str | None = None
```
This reference uses direct field arguments and full-update semantics to keep the CRUD mechanics visible. Introduce separate create, update, or public schemas only when an API boundary needs different validation, field visibility, or partial-update behavior. See [SQLModel integration](sqlmodel.md) for that larger modeling pattern.
---
## Independent CRUD Functions
Functions are the simplest default when grouping state or behavior in an object adds no value. Decorate public service functions when both standalone reads and explicit composition are useful. The assertion narrows the optional type after decorator injection and catches accidental explicit `None` calls.
```python
from sqlmodel import select
from sqlmodel.ext.asyncio.session import AsyncSession
from .session import with_session
@with_session
async def create_widget(
name: str,
description: str | None = None,
*,
session: AsyncSession | None = None,
) -> Widget:
assert session is not None, "Session must be provided by with_session decorator"
widget = Widget(name=name, description=description)
session.add(widget)
await session.flush()
return widget
@with_session
async def get_widget(
widget_id: int,
*,
session: AsyncSession | None = None,
) -> Widget | None:
assert session is not None, "Session must be provided by with_session decorator"
return await session.get(Widget, widget_id)
@with_session
async def list_widgets(
*,
offset: int = 0,
limit: int = 100,
session: AsyncSession | None = None,
) -> list[Widget]:
assert session is not None, "Session must be provided by with_session decorator"
if offset < 0:
raise ValueError("offset must be non-negative")
if not 1 <= limit <= 100:
raise ValueError("limit must be between 1 and 100")
statement = select(Widget).order_by(Widget.id).offset(offset).limit(limit)
return list(await session.scalars(statement))
@with_session
async def update_widget(
widget_id: int,
name: str,
description: str | None,
*,
session: AsyncSession | None = None,
) -> Widget | None:
assert session is not None, "Session must be provided by with_session decorator"
widget = await session.get(Widget, widget_id)
if widget is None:
return None
widget.name = name
widget.description = description
await session.flush()
return widget
@with_session
async def delete_widget(
widget_id: int,
*,
session: AsyncSession | None = None,
) -> Widget | None:
assert session is not None, "Session must be provided by with_session decorator"
widget = await session.get(Widget, widget_id)
if widget is None:
return None
await session.delete(widget)
await session.flush()
return widget
```
Update and delete load the row through the same session that mutates it. This avoids accepting detached instances from an earlier standalone read and gives both operations an explicit `None` result that the application layer can map to a domain or HTTP error. Delete returns the loaded object for callers that need its values, but that object represents a row scheduled for deletion and must not be reused as persistent state. List operations validate their bounds and order by the primary key so pagination is deterministic. Add a unique tiebreaker whenever ordering by a non-unique field.
`flush()` sends pending writes and populates ordinary generated primary keys. It does not itself commit. A decorated write called without a session will therefore roll back when its owned session closes unless the function explicitly commits. Prefer passing a transaction-scoped session so several writes compose atomically. Use `await session.refresh(widget)` only when the operation deliberately needs database-generated state that was not returned during the flush; an unconditional refresh adds another query.
---
## Repository Object
A repository can provide a stable domain-facing interface when several callers need the same grouped operations. It remains stateless here: every method requires a session and delegates to the analogous function.
```python
from sqlmodel.ext.asyncio.session import AsyncSession
class WidgetRepository:
async def create(
self,
session: AsyncSession,
name: str,
description: str | None = None,
) -> Widget:
return await create_widget(
name,
description,
session=session,
)
async def get(
self,
session: AsyncSession,
widget_id: int,
) -> Widget | None:
return await get_widget(widget_id, session=session)
async def list(
self,
session: AsyncSession,
*,
offset: int = 0,
limit: int = 100,
) -> list[Widget]:
return await list_widgets(
offset=offset,
limit=limit,
session=session,
)
async def update(
self,
session: AsyncSession,
widget_id: int,
name: str,
description: str | None,
) -> Widget | None:
return await update_widget(
widget_id,
name,
description,
session=session,
)
async def delete(
self,
session: AsyncSession,
widget_id: int,
) -> Widget | None:
return await delete_widget(widget_id, session=session)
```
The object is intentionally thin. Tests pass a transaction-scoped test session directly. The caller always owns that session and its transaction, and the repository never closes or commits it.
If a read participates in a later write, pass the same session and place both operations inside the explicit transaction. This avoids splitting one use case across sessions and keeps SQLAlchemy's autobegin behavior from obscuring transaction ownership. Add a repository only when its naming, shared query policy, dependency substitution, or domain boundary improves the application. Independent functions remain a valid and often clearer design.
---
## Transaction Ownership
Compose multiple calls under one use-case transaction. `db_transaction_scope()` owns the standalone engine, factory, session, and transaction lifetimes. Decorated CRUD functions detect the supplied session and borrow it; repository methods receive it directly.
```python
from .session import db_transaction_scope
async def replace_widget(
repository: WidgetRepository,
widget_id: int,
replacement_name: str,
replacement_description: str | None = None,
) -> Widget | None:
async with db_transaction_scope() as active_session:
deleted_widget = await repository.delete(
active_session,
widget_id,
)
if deleted_widget is None:
return None
return await repository.create(
active_session,
replacement_name,
replacement_description,
)
```
If creation fails, deletion rolls back with it. Inside an already-running application, prefer `async with session_factory.begin()` or `async with session.begin()` over `db_transaction_scope()` so the application-owned engine and factory remain in use. Do not add direct `commit()` calls to CRUD functions or repository methods because that prevents callers from composing several operations atomically. See [transaction boundaries](transactions.md) and [session management](session.md) for ownership details.
---
## Anti-Patterns
- Storing one mutable `AsyncSession` on a long-lived repository object.
- Creating sessions manually inside functions already using `@with_session`.
- Passing database configuration through every CRUD call instead of injecting a session at the data-access boundary.
- Assuming decorator-owned write sessions commit on close.
- Forwarding explicit `session=None` when decorator injection was intended.
- Accepting unbounded list queries.
- Accepting detached ORM instances for update or delete when an identifier can be resolved in the active session.
- Accessing unloaded attributes after a standalone repository read has closed its owned session.
---
## Operational Checks
- Every CRUD call receives a task-local `AsyncSession`.
- Standalone reads create and close a session at the service or application boundary.
- Standalone reads may omit `session`; decorated writes receive a transaction-scoped session or explicitly own their commit policy.
- Supplied write sessions remain caller-owned.
- Each complete write operation declares a visible transaction boundary.
- List operations have pagination and deterministic ordering where required.
- Update requires values for both mutable fields; passing `None` explicitly clears the nullable description.
- Get, update, and delete use the same identifier and missing-row semantics.
- Decorated functions accept an optional keyword-only session; repository methods require one explicitly.
- Standalone service reads load all state needed after their owned session closes.
- Repository objects hold query policy when useful, never database configuration or request-scoped session state.
---
## Testing Checks
- Create tests verify generated identifiers and persisted field values after commit.
- Get and list tests cover found, missing, pagination, and ordering behavior.
- List tests reject negative offsets and limits outside the supported range.
- Update tests cover replacement of both mutable fields, including clearing the nullable description.
- Update and delete tests cover missing identifiers without mutating the database.
- Delete tests verify the returned row and its absence after commit.
- Failure tests verify that a surrounding transaction rolls back all composed CRUD calls.
- Optional-session read tests verify borrowed sessions remain open and owned sessions close without committing.
- Decorated write tests verify supplied transactions remain caller-owned and omitted sessions do not imply a commit.
- Composition tests pass one active session through several CRUD calls and verify one atomic commit or rollback.
@@ -0,0 +1,281 @@
# Async SQLAlchemy Engine
!!! info "Primary sources"
- [Python `asynccontextmanager`](https://docs.python.org/3/library/contextlib.html#contextlib.asynccontextmanager)
- [Python `functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache)
- [SQLAlchemy connections](https://docs.sqlalchemy.org/en/21/core/connections.html)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy pooling and multiprocessing](https://docs.sqlalchemy.org/en/21/core/pooling.html#pooling-multiprocessing)
- [SQLAlchemy SQLite transaction control](https://docs.sqlalchemy.org/en/21/dialects/sqlite.html#enabling-non-legacy-sqlite-transactional-modes-with-the-sqlite3-or-aiosqlite-driver)
- [SQLAlchemy SQLite foreign-key support](https://docs.sqlalchemy.org/en/21/dialects/sqlite.html#foreign-key-support)
- [SQLite PRAGMA reference](https://www.sqlite.org/pragma.html)
- [`nicegui-db` engine implementation](https://forgejo.john-stream.com/john/nicegui-db/src/commit/126bc26ad8635a86bacf684d7bda409230347597/src/nicegui_db/db/engine.py)
---
## Engine Ownership Model
Resolve one async engine for each database URL within an application, worker, command, or test lifecycle.
- SQLAlchemy guidance: the engine is intended as a long-lived, concurrent registry over pooled DB connections, not a per-operation object.
- `get_engine(database_url)` owns URL-keyed engine construction and caching.
- The composition root enters `engine_scope(database_url)` once and therefore owns initialization and disposal.
- Services and repositories receive a session or session factory; they do not resolve an engine.
!!! tip "Practical rule"
- Exactly one cached engine for each database URL during an active application-owned lifecycle.
- Exactly one active owning `engine_scope()` for a given URL.
- Zero `create_async_engine(...)` calls in feature code.
- Zero engine lookup or disposal calls in repository code.
---
## Cached Engine Resolution
[`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache) makes the database URL the engine identity:
```python
from functools import cache
from sqlalchemy.ext.asyncio import AsyncEngine
from sqlalchemy.ext.asyncio import create_async_engine
@cache
def get_engine(database_url: str) -> AsyncEngine:
engine = create_async_engine(database_url, pool_pre_ping=True)
if engine.dialect.name == "sqlite":
configure_aiosqlite_engine(engine)
return engine
```
Repeated calls with the same exact URL return the same `AsyncEngine`; different URLs produce independent cache entries. Construction configures the dialect and pool but normally does not open a database connection until the first operation. SQLite event listeners are installed only when a new cached engine is constructed, before its first connection.
Resolve settings into the final URL before calling `get_engine()`. Services and repositories should not call it directly: the cache controls construction identity, not ownership.
## Owning Engine Scope
Use one [`asynccontextmanager`](https://docs.python.org/3/library/contextlib.html#contextlib.asynccontextmanager) to pair cached resolution and optional schema initialization with disposal:
```python
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from sqlalchemy.ext.asyncio import AsyncEngine
from sqlmodel import SQLModel
@asynccontextmanager
async def engine_scope(
database_url: str,
*,
initialize: bool = True,
) -> AsyncGenerator[AsyncEngine]:
engine = get_engine(database_url)
if initialize:
await initialize_db(database_url)
try:
yield engine
finally:
await dispose_engine(database_url)
async def initialize_db(database_url: str) -> None:
from . import models # noqa: F401
engine = get_engine(database_url)
async with engine.begin() as connection:
await connection.run_sync(SQLModel.metadata.create_all)
async def dispose_engine(database_url: str) -> None:
engine = get_engine(database_url)
try:
await engine.dispose()
finally:
get_engine.cache_clear()
```
The code that enters `engine_scope()` owns the engine. It keeps that scope open for the complete application, worker, command, or test lifecycle and passes the yielded engine into session-factory construction. Successful and exceptional exits both dispose the pool and invalidate cached engine resolution.
Initialization imports the model package so every table is registered, then runs `SQLModel.metadata.create_all()` in `engine.begin()`. This is suitable for the template and focused tests. Use migrations instead when schema evolution is part of the deployment contract. Pass `initialize=False` only when another owner provisions the schema or a test is directly exercising construction without schema setup.
`dispose_engine()` clears the complete function cache, not only the requested URL. This matches the template and is safe under its intended single-database lifecycle. Applications that own several simultaneously active database URLs need per-key lifecycle management rather than this global invalidation behavior.
Workers, scripts, and other composition roots enter `database_scope()` directly:
```python
async with database_scope(settings.database_url) as session_factory:
await run_worker(session_factory)
```
`database_scope()` is defined in [session management](session.md). It enters `engine_scope()` and creates the factory bound to the yielded engine.
Do not overlap two owning scopes for the same URL. Both resolve the same cached engine, and the first scope to exit disposes it and clears the cache while the other still refers to it. For several fixed databases, use one non-overlapping owner per URL and account for global cache invalidation; use [`AsyncExitStack`](https://docs.python.org/3/library/contextlib.html#contextlib.AsyncExitStack) only after adopting lifecycle semantics that support several simultaneous owners.
When directly testing engine construction or lifecycle behavior, enter `engine_scope()` in the test or fixture. Exiting the context disposes the engine even when the test fails and clears the cache for the next lifecycle.
See [FastAPI database integration](fastapi.md) for adapting `database_scope()` to application lifespan and dependency injection.
---
## Driver URLs (Project Requirement: asyncpg + aiosqlite)
Use SQLAlchemy async driver URLs:
- PostgreSQL: `postgresql+asyncpg://user:pass@host:5432/dbname`
- SQLite: `sqlite+aiosqlite:///./app.db`
!!! warning "Driver compatibility"
- Do not mix sync drivers, for example `psycopg2`, with `create_async_engine()`.
- Keep URL construction centralized in settings/config, not in feature modules.
---
## SQLite Connection and Transaction Policy
SQLite settings do not form one indivisible bundle:
- `PRAGMA foreign_keys=ON` is a correctness requirement when the schema declares foreign keys. SQLite requires it on every connection, including the connection used by `metadata.create_all()`.
- Disabling the driver's implicit `BEGIN` and emitting `BEGIN` from SQLAlchemy provides non-legacy transaction behavior for `aiosqlite`. This makes SELECT, DDL, and SAVEPOINT behavior participate in SQLAlchemy's transaction boundary consistently.
- `PRAGMA busy_timeout` is a per-connection lock-wait policy. Choose the duration from the application's latency and contention requirements.
- `PRAGMA journal_mode=WAL` is an optional file-database concurrency policy. WAL persists in the database file, cannot be enabled for an in-memory database, and is not a substitute for transaction control.
Install instance-level listeners exactly once, immediately after constructing an `aiosqlite` engine and before its first connection:
```python
from sqlalchemy import event
from sqlalchemy.engine import Connection
from sqlalchemy.engine.interfaces import DBAPIConnection
from sqlalchemy.ext.asyncio import AsyncEngine
def configure_aiosqlite_engine(
engine: AsyncEngine,
*,
busy_timeout_ms: int | None = 30_000,
enable_wal: bool = False,
) -> None:
if engine.dialect.name != "sqlite" or engine.dialect.driver != "aiosqlite":
raise ValueError("Expected a sqlite+aiosqlite engine")
if busy_timeout_ms is not None and busy_timeout_ms < 0:
raise ValueError("busy_timeout_ms must be non-negative")
@event.listens_for(engine.sync_engine, "connect")
def configure_connection(dbapi_connection: DBAPIConnection, _: object) -> None:
dbapi_connection.isolation_level = None
cursor = dbapi_connection.cursor()
try:
cursor.execute("PRAGMA foreign_keys=ON")
if busy_timeout_ms is not None:
cursor.execute(f"PRAGMA busy_timeout={busy_timeout_ms}")
if enable_wal:
cursor.execute("PRAGMA journal_mode=WAL")
journal_mode = cursor.fetchone()
if journal_mode is None or journal_mode[0].lower() != "wal":
raise RuntimeError("SQLite could not enable WAL mode")
finally:
cursor.close()
@event.listens_for(engine.sync_engine, "begin")
def begin_transaction(connection: Connection) -> None:
connection.exec_driver_sql("BEGIN")
```
The `connect` listener receives the adapted synchronous DBAPI connection exposed by `engine.sync_engine`; event callbacks themselves are synchronous even though application queries use the async engine. Setting `isolation_level=None` and adding the `begin` listener are one transaction-control strategy and must remain paired. Do not combine this pair with SQLAlchemy's driver-level `AUTOCOMMIT` isolation mode.
The default above enables foreign keys and modern transaction boundaries for file and in-memory databases. Enable WAL only for a file-backed database after deciding that its read/write concurrency model is appropriate. Treat `30_000` as an example policy, not a universal default; `connect_args={"timeout": 30.0}` at engine construction is another way to configure the underlying SQLite lock timeout.
---
## Pooling Defaults and Tuning
Default behavior is usually correct first:
- Async engines use async-compatible pooling (`AsyncAdaptedQueuePool`) by default.
- Start with defaults, then tune from observed load (`pool_size`, `max_overflow`, `pool_timeout`, `pool_recycle`).
- Enable `pool_pre_ping=True` for safer stale-connection handling in long-running services.
When to switch pool strategy:
- `NullPool` if you explicitly need no pooling (special environments, some tests, or strict cross-loop constraints).
- Keep in mind this increases connect/disconnect churn.
### When `StaticPool` Is Appropriate
Use [`StaticPool`](https://docs.sqlalchemy.org/en/21/core/pooling.html#sqlalchemy.pool.StaticPool) only when every checkout must reuse one DBAPI connection and all database access is serialized. Typical cases are:
- A serial test suite using a private in-memory SQLite database. The `sqlite+aiosqlite://` URL already selects `StaticPool` automatically, so specifying `poolclass=StaticPool` is normally redundant.
- A narrowly scoped SQLite engine that must preserve connection-local state, such as temporary tables, across SQLAlchemy connection or session checkouts.
When explicit configuration is required:
```python
from sqlalchemy.ext.asyncio import create_async_engine
from sqlalchemy.pool import StaticPool
engine = create_async_engine(
"sqlite+aiosqlite:///./test.db",
poolclass=StaticPool,
)
```
`StaticPool` is not a general performance optimization or a way to make SQLite concurrent. All sessions share one underlying connection and its single transaction state, so one session's `COMMIT` or `ROLLBACK` can interfere with another session. Do not use it when several sessions or tasks may access the engine concurrently. For concurrent in-memory work, use a named shared-cache SQLite URL so pooled connections have independent transaction state, or use a temporary file database. See [SQLite test targets](testing.md#sqlite-targets) for those patterns.
---
## Disposal Semantics
`dispose_engine(database_url)` resolves the cached engine, awaits `engine.dispose()`, and clears the engine cache in a `finally` block. `engine.dispose()` replaces/disposes the pool, but only checked-in connections are immediately closed.
Rules:
- Dispose when the app is shutting down.
- Clear cached resolution even when disposal raises, so a later lifecycle cannot receive the failed engine object.
- Dispose before reusing an engine across event loops.
- In forked child-process initialization, use `engine.dispose(close=False)` (sync API guidance) so child processes do not touch parent-held connections.
Avoid relying on garbage collection for engine cleanup in async code.
---
## Event Loop and Process Boundaries
Do not share pooled connections across boundaries:
- Multiple event loops: do not reuse the same pooled async engine across loops unless you intentionally disable pooling (`NullPool`) or dispose before handoff.
- Multiprocessing/fork: pooled connections must not be inherited for active use across process boundaries.
This prevents broken socket state and cross-process connection corruption.
---
## What Not to Do
- Create an engine inside each operation or unit of work.
- Create/dispose engines inside repository methods.
- Resolve an engine from repositories instead of injecting a session dependency.
- Keep engine creation as a hidden side effect of import-time module globals.
- Keep a session factory alive after its bound engine scope exits.
- Enter overlapping engine scopes for the same cached URL.
- Treat `cache_clear()` as per-URL invalidation when it clears every cached engine.
- Use `metadata.create_all()` as a substitute for required production migrations.
- Install the same SQLite event listeners more than once on one engine.
- Enable WAL blindly for in-memory SQLite or treat a busy timeout as a concurrency guarantee.
---
## Engine Design Checklist
- One cached engine per exact database URL during an active lifecycle.
- One owning engine scope per URL, with no overlapping owners.
- Cached resolution, optional initialization, disposal, and cache invalidation follow one framework-independent lifecycle.
- The composition root enters the database scope once and keeps it open until shutdown.
- Session factory created inside, and never outlives, its engine scope.
- Model registration occurs before `metadata.create_all()` when initialization is enabled.
- Async driver URL matches backend (`asyncpg` or `aiosqlite`).
- `aiosqlite` foreign-key and transaction listeners installed once before first use.
- WAL enabled only as an explicit policy for a file-backed SQLite database.
- Pooling strategy is explicit for non-default needs.
- No feature-path engine creation.
- Tests enter the same scope and receive deterministic disposal plus cache cleanup.
@@ -0,0 +1,192 @@
# FastAPI Database Integration
!!! info "Primary sources"
- [FastAPI lifespan events](https://fastapi.tiangolo.com/advanced/events/)
- [FastAPI dependencies with `yield`](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/)
- [FastAPI dependency overrides](https://fastapi.tiangolo.com/advanced/testing-dependencies/)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [`nicegui-db` application lifespan](https://forgejo.john-stream.com/john/nicegui-db/src/commit/126bc26ad8635a86bacf684d7bda409230347597/src/nicegui_db/ui/app.py)
- [`nicegui-db` database dependencies](https://forgejo.john-stream.com/john/nicegui-db/src/commit/126bc26ad8635a86bacf684d7bda409230347597/src/nicegui_db/ui/dependency.py)
---
## Purpose
Connect the framework-independent database tools to FastAPI:
- lifespan enters one application-owned `database_scope()`,
- application state holds settings and the resulting session factory,
- dependencies create one session per request,
- `Annotated` aliases make route ownership concise and explicit.
The underlying resource and transaction rules remain in [engine lifecycle](engine.md), [session management](session.md), and [transaction boundaries](transactions.md).
---
## Lifespan Ownership
Enter `database_scope()` once for the complete application lifecycle. Store the session factory, not the engine, because request code needs sessions rather than direct pool access:
```python
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from fastapi import FastAPI
from .config import Settings
from .config import get_database_url
from .db import database_scope
@asynccontextmanager
async def lifespan(settings: Settings, app: FastAPI) -> AsyncGenerator[None]:
app.state.settings = settings
db_url = get_database_url(settings)
try:
async with database_scope(db_url) as session_factory:
app.state.session_factory = session_factory
yield
finally:
del app.state.settings
del app.state.session_factory
```
The application factory binds `settings` to lifespan, for example with `partial(lifespan, settings)`. Lifespan does not construct resources per request. It enters the same framework-independent scope used by scripts, workers, and tests, keeps that scope open while requests are served, and lets it dispose the engine and clear cached engine resolution during shutdown.
The template's unconditional `del app.state.session_factory` mirrors an expected successful startup. If `database_scope()` raises before assignment, cleanup can raise `AttributeError` and obscure the startup error. A production hardening option is to assign a sentinel before the `try` or delete conditionally; that changes failure behavior and is not part of the exact template mechanics.
Only store the engine too when application-level code genuinely needs direct Core operations, pool instrumentation, or engine-specific diagnostics. Routes and repositories should normally receive an `AsyncSession`.
---
## Session Factory Dependency
A synchronous dependency retrieves the already-created factory from application state:
```python
from typing import Annotated
from fastapi import Depends
from fastapi import Request
from .session import SessionFactory
def _get_session_factory(request: Request) -> SessionFactory:
return request.app.state.session_factory
type SessionFactoryDep = Annotated[SessionFactory, Depends(_get_session_factory)]
```
`Depends()` does not create or cache a factory here. It only exposes the lifespan-owned object. This function is also the narrow seam that tests can override when they need a different factory.
---
## Request Session Dependencies
Use a session-only dependency for reads and other request conversations that must not commit implicitly:
```python
from collections.abc import AsyncGenerator
from sqlmodel.ext.asyncio.session import AsyncSession
async def _get_session(session_factory: SessionFactoryDep) -> AsyncGenerator[AsyncSession]:
async with session_factory() as owned_session:
yield owned_session
type SessionDep = Annotated[AsyncSession, Depends(_get_session)]
```
The dependency creates and closes one session per request. Closing rolls back any unfinished autobegun transaction; it does not commit.
---
## Route Usage
Read route:
```python
@router.get("/items/{item_id}")
async def get_item(item_id: int, session: SessionDep) -> Item | None:
return await find_item(session, item_id)
```
Write route:
```python
@router.post("/items")
async def create_item(payload: ItemCreate, session: SessionDep) -> Item:
async with session.begin():
return await insert_item(session, payload)
```
The template exposes only `SessionDep`; it does not hide commit behavior in dependency teardown. Choose one visible write convention per application:
- place `async with session.begin():` around a complete write unit, which commits on success and rolls back on exception; or
- call `await session.commit()` explicitly after all writes when the route is the complete unit, as the template's simple UI action does.
The context-manager form scales better to several statements and makes exception rollback visible. Direct `commit()` is concise but requires the route to preserve the single-commit invariant and handle any recovery needs. Do not combine both conventions in one route. Lower-level data-access functions receive the existing session and remain unaware of FastAPI.
---
## Background Work
A request session belongs to that request and must not be retained by a background task. Inject or otherwise provide the application session factory, then create a new session inside the task:
```python
async def run_background_job(session_factory: SessionFactory) -> None:
async with session_factory.begin() as session:
await process_pending_items(session)
```
If work must survive application shutdown, it needs an independently owned worker lifecycle rather than the FastAPI lifespan-owned factory.
---
## Testing and Overrides
Override the narrow dependency that matches the test objective:
- Override `_get_session_factory` to preserve production request-session behavior with a test factory.
- Override `_get_session` when a test must inject one transaction-scoped session directly.
- Verify each lifespan receives a fresh engine and session factory and removes application state during teardown.
- Remove overrides during teardown so mutable application state does not leak between tests.
```python
app.dependency_overrides[_get_session] = get_test_session
try:
yield app
finally:
app.dependency_overrides.pop(_get_session, None)
```
See [database testing](testing.md) for outer transactions, SAVEPOINT-backed fixtures, and database target selection.
---
## Anti-Patterns
- Creating an engine or session factory in a request dependency.
- Reading settings and constructing database resources from repositories.
- Storing one mutable `AsyncSession` on `app.state`.
- Sharing a request session with concurrent or background tasks.
- Assuming `SessionDep` commits when dependency cleanup runs.
- Keeping `app.state.session_factory` after its `database_scope()` exits.
- Using deprecated startup and shutdown event handlers alongside lifespan.
---
## Integration Checklist
- Lifespan enters exactly one `database_scope()` for each application lifecycle.
- Application state stores the yielded session factory.
- Session dependencies create and close one session per request.
- The session dependency owns request session closure but not commit behavior.
- Routes use `Annotated` aliases and receive sessions, not engines.
- Background tasks create their own sessions from a still-live factory.
- Tests override and restore dependencies deterministically.
@@ -66,13 +66,13 @@ roles = await user.awaitable_attrs.roles
## Practical Enforcement Model
Use phased enforcement:
Require explicit I/O behavior on every async ORM path:
1. High-traffic and latency-sensitive routes: enforce explicit eager loading.
2. Background tasks and less critical paths: track and progressively tighten.
3. Add review checks to prevent newly introduced implicit-load hotspots.
1. Define loader options for relationships and deferred columns needed by the operation.
2. Use `refresh()` or awaitable attributes only when the additional query is deliberate and visible.
3. Add review checks that reject unplanned lazy-load paths.
This keeps modernization pragmatic while reducing hidden I/O over time.
This keeps event-loop behavior predictable and makes query boundaries reviewable from the code.
---
@@ -99,9 +99,3 @@ This keeps modernization pragmatic while reducing hidden I/O over time.
- Tests verify expected data is present without hidden secondary query surprises.
- Regression tests exist for routes previously affected by implicit-load failures.
---
## Migration Notes
- Start advisory: target high-risk paths first.
- As coverage improves, elevate selected rules to mandatory in code review policy.
@@ -1,6 +1,6 @@
# FastAPI Async SQLAlchemy References Index
Purpose: concept registry for modernization guidance used by this skill.
Purpose: concept registry for the principles, mechanics, and implementation guidance used by this skill.
---
@@ -8,17 +8,21 @@ Purpose: concept registry for modernization guidance used by this skill.
| Concept | File | Status | Decision Level | Owner | Last Reviewed |
|---|---|---|---|---|---|
| Engine lifecycle and ownership | [engine.md](engine.md) | adopted | mandatory | platform/backend | 2026-06-17 |
| Session factory and scope | [session.md](session.md) | adopted | mandatory | platform/backend | 2026-06-17 |
| Engine lifecycle and ownership | [engine.md](engine.md) | adopted | mandatory | platform/backend | 2026-08-06 |
| Session factory and scope | [session.md](session.md) | adopted | mandatory | platform/backend | 2026-08-06 |
| FastAPI lifespan and dependency injection | [fastapi.md](fastapi.md) | adopted | mandatory | platform/backend | 2026-08-06 |
| Transaction boundaries | [transactions.md](transactions.md) | adopted | mandatory | platform/backend | 2026-06-17 |
| Implicit ORM I/O under asyncio | [implicit_io.md](implicit_io.md) | adopted | advisory | platform/backend | 2026-06-17 |
| Observability and resilience | [observability.md](observability.md) | adopted | mandatory | platform/backend | 2026-06-17 |
| SQLModel modeling and async boundaries | [sqlmodel.md](sqlmodel.md) | adopted | mandatory | platform/backend | 2026-08-06 |
| Basic CRUD repository and functions | [crud.md](crud.md) | adopted | advisory | platform/backend | 2026-08-06 |
| Test database targets and fixture data | [testing.md](testing.md) | adopted | mandatory | platform/backend | 2026-08-06 |
---
## How to Use This Folder
- `SKILL.md` defines the planning workflow and migration procedure.
- `SKILL.md` defines the explanatory workflow and shared mental model.
- Each concept doc defines policy-level guidance for one concern.
- Use the template in [template.md](template.md) for new concept docs.
- Keep references source-linked and implementation snippets minimal.
@@ -29,4 +33,4 @@ Purpose: concept registry for modernization guidance used by this skill.
- If a PR changes database lifecycle/session/ORM loading behavior, update the relevant concept file.
- Keep `Status`, `Decision Level`, and `Last Reviewed` current.
- Use `advisory` only when incremental rollout is intended; use `mandatory` for required runtime policy.
- Use `advisory` for recommendations that depend on application context; use `mandatory` for required runtime policy.
@@ -105,10 +105,3 @@ Readiness checks should be lightweight and bounded (timeouts), not heavy diagnos
- Readiness endpoint test covers healthy and unhealthy DB states.
- Integration test simulates disconnect/reconnect behavior.
- Load/concurrency tests validate pool behavior under stress.
---
## Migration Notes
- Start with resilient defaults (`pool_pre_ping`) and simple health policy.
- Add deeper metrics/event hooks incrementally once baseline reliability is in place.
@@ -0,0 +1,404 @@
# Async SQLAlchemy Session Management
!!! info "Primary sources"
- [Python `asynccontextmanager`](https://docs.python.org/3/library/contextlib.html#contextlib.asynccontextmanager)
- [Python `functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache)
- [Python `inspect.signature`](https://docs.python.org/3/library/inspect.html#inspect.signature)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy session basics](https://docs.sqlalchemy.org/en/21/orm/session_basics.html)
- [`nicegui-db` session implementation](https://forgejo.john-stream.com/john/nicegui-db/src/commit/126bc26ad8635a86bacf684d7bda409230347597/src/nicegui_db/db/session.py)
---
## Purpose
Define one canonical session model for SQLAlchemy asyncio:
- configure a lifespan-owned factory or resolve a URL-keyed cached factory,
- create one AsyncSession per task or unit of work,
- let callers supply a session when they already own the scope,
- never share one AsyncSession across concurrent tasks.
---
## Scope and Non-Goals
- In scope: session factory creation, task scoping, and transaction demarcation.
- Out of scope: framework dependency wiring, ORM model design, query optimization strategy, and schema migration tooling.
---
## Rules
- Create the application `async_sessionmaker` inside `database_scope()` and store it in application state for request dependencies.
- Use `get_session_factory(db_url)` and `resolve_session_factory()` for standalone decorated operations that do not receive the application factory.
- Use a fresh AsyncSession for each task or explicit unit of work.
- Let reusable service functions accept `AsyncSession | None` and apply `@with_session` when standalone invocation is useful.
- Pass an `AsyncSession` directly when composing several calls under one caller-owned scope.
- Borrow a caller-provided session without beginning, closing, committing, or rolling it back.
- Do not share AsyncSession across `asyncio.gather()` or parallel tasks.
- Prefer direct dependency injection over global scoped-session patterns in new code.
- Use explicit transaction boundaries (`async with session.begin():`) for writes.
- Use `db_transaction_scope()` when a standalone operation must own engine, factory, session, and transaction lifetimes together.
- Use `begin_nested()` directly and only when partial rollback through a database SAVEPOINT is required.
---
## Sessions and Transactions
A session and a transaction solve related but different problems:
| Concept | Responsibility | Typical lifetime |
| --- | --- | --- |
| `AsyncSession` | Provides the ORM workspace: executes queries, tracks loaded and changed objects in its identity map, and flushes pending changes. It also coordinates access to a database connection. | One task or explicit unit of work. |
| Transaction | Defines the atomic database boundary: all work inside it commits together on success or rolls back together on failure. | One complete operation that must have a single outcome. |
A transaction belongs to a session; it is not an alternative to one. The session is the interface used by application and data-access code, while the transaction determines when that work becomes permanent. A session may coordinate sequential transactions during its lifetime, although short-lived application scopes commonly use one session for one transaction.
Use a session without a helper-owned commit boundary for independent reads or lower-level functions that must participate in whatever transaction their caller controls:
```python
async with session_factory() as session:
item = await find_item(session, item_id)
```
Use an explicit transaction for writes, read-modify-write operations, or several statements that must succeed or fail as one unit:
```python
async with session_factory.begin() as session:
order = await create_order(session, order_data)
await reserve_inventory(session, order)
```
SQLAlchemy sessions use [autobegin](https://docs.sqlalchemy.org/en/21/orm/session_basics.html#auto-begin), so the first database operation normally starts a transaction even for a read. Therefore, “session-only” means that the surrounding helper owns only session lifetime and does not promise to commit; it does not mean that no database transaction exists. Closing such a session releases its resources and rolls back any unfinished transaction. An explicit `begin()` is valuable when application code must make the atomic boundary and commit ownership visible.
For most read-only operations, a session context is sufficient. Use an explicit transaction for reads when they need a defined consistency boundary, participate in a larger atomic operation, or use locking such as `SELECT ... FOR UPDATE`.
---
## Session Factory Mechanics
An `async_sessionmaker[AsyncSession]` is a reusable configuration object and callable session producer. It stores how sessions should be created, including the engine binding and options such as `expire_on_commit=False`. It is not itself a session, connection, or transaction, and calling it does not make a shared global `AsyncSession`.
The template exposes two construction paths with the same session options.
The application-owned path creates a factory inside the engine lifecycle:
```python
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from sqlalchemy.ext.asyncio import async_sessionmaker
from sqlmodel.ext.asyncio.session import AsyncSession
from .engine import engine_scope
type SessionFactory = async_sessionmaker[AsyncSession]
@asynccontextmanager
async def database_scope(
db_url: str,
*,
auto_flush: bool = True,
) -> AsyncGenerator[SessionFactory]:
async with engine_scope(db_url) as engine:
yield async_sessionmaker(
bind=engine,
class_=AsyncSession,
expire_on_commit=False,
autoflush=auto_flush,
)
```
FastAPI lifespan enters this path once and stores the yielded factory on application state. The factory must not outlive the scope because its bound engine is disposed on exit.
The standalone path caches a factory by URL and `auto_flush` policy:
```python
from functools import cache
from .engine import get_engine
@cache
def get_session_factory(
db_url: str,
*,
auto_flush: bool = True,
) -> SessionFactory:
return async_sessionmaker(
bind=get_engine(db_url),
class_=AsyncSession,
expire_on_commit=False,
autoflush=auto_flush,
)
def resolve_session_factory(settings: Settings | None = None) -> SessionFactory:
settings = settings or get_settings()
db_url = get_database_url(settings)
return get_session_factory(db_url)
```
This path lets framework-independent helpers resolve one stable factory without receiving it through every call. The tradeoff is hidden configuration resolution and a second lifecycle mechanism. `dispose_engine()` clears `get_engine`'s cache but does not clear `get_session_factory`'s cache in the template. A cached factory remains bound to the disposed engine object; SQLAlchemy can create a new pool when that engine is used again, but a later `database_scope()` for the same URL can own a different engine. Treat cached standalone resolution as process-lifetime convenience, avoid repeated application lifecycles in one process, and clear both caches together if the template evolves to support them.
Each call to `session_factory()` creates a distinct `AsyncSession`. The caller that invokes the factory owns that session lifetime and must close it, normally with `async with`:
```python
async with session_factory() as session:
...
```
The factory can be shared across operations and tasks. Sessions produced by it cannot be shared across concurrent tasks.
Passing the application factory directly has three useful consequences:
- Lower layers do not resolve settings or global resources.
- Tests can inject a test factory directly through `session_scope(session_factory=...)` or FastAPI state.
- Transaction ownership remains independent of engine construction.
---
## Database and Convenience Scopes
The template provides three framework-independent context managers:
```python
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
@asynccontextmanager
async def db_session_scope(
db_url: str | None = None,
) -> AsyncGenerator[AsyncSession]:
db_url = db_url or resolve_database_url()
async with database_scope(db_url) as session_factory, session_factory() as session:
yield session
@asynccontextmanager
async def db_transaction_scope(
db_url: str | None = None,
) -> AsyncGenerator[AsyncSession]:
db_url = db_url or resolve_database_url()
async with database_scope(db_url) as session_factory, session_factory.begin() as session:
yield session
@asynccontextmanager
async def session_scope(
*,
settings: Settings | None = None,
session_factory: SessionFactory | None = None,
session: AsyncSession | None = None,
) -> AsyncGenerator[AsyncSession]:
if session is not None:
yield session
return
session_factory = session_factory or resolve_session_factory(settings=settings)
async with session_factory() as owned_session:
yield owned_session
```
`db_session_scope()` owns a complete temporary database lifecycle and a session but does not commit. `db_transaction_scope()` owns the same resources plus a root transaction that commits on successful exit and rolls back on exception. Both initialize the schema by default because `database_scope()` enters `engine_scope()` with its default `initialize=True`. They are appropriate for scripts, commands, and isolated operations, not per-request use inside an already-running application.
`session_scope()` is the borrow-or-create helper. Its precedence is supplied session, supplied factory, then settings-based cached factory resolution. A supplied session remains entirely caller-owned; the helper does not require an active transaction and does not begin, commit, roll back, or close it. An owned session is closed on exit, and unfinished autobegun work rolls back.
Passing `session=None` is the same as omitting the session for `session_scope()` and therefore creates a session. This differs from `with_session`, which tests whether the argument name was bound rather than whether its value is non-null.
## Signature-Aware Session Injection
`with_session` allows one async function to support standalone calls and explicit composition:
```python
from collections.abc import Awaitable
from collections.abc import Callable
from functools import wraps
from inspect import signature
def with_session[**P, R](
func: Callable[P, Awaitable[R]],
) -> Callable[P, Awaitable[R]]:
sig = signature(func)
@wraps(func)
async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
bound = sig.bind_partial(*args, **kwargs)
if "session" in bound.arguments:
return await func(*args, **kwargs)
async with resolve_session_factory()() as session:
bound.arguments["session"] = session
return await func(*bound.args, **bound.kwargs)
return wrapper
```
The function must be async and expose a parameter named exactly `session`. The decorator preserves metadata with `wraps()`, binds positional and keyword arguments through the original signature, and injects a fresh session only when the caller omitted that argument.
The distinction between omitted and explicit `None` is deliberate in the implementation:
- `await operation()` injects and owns a session.
- `await operation(session=existing_session)` borrows the caller's session.
- `await operation(None)` or `await operation(session=None)` forwards `None` without injection.
The decorated function therefore types the parameter as `AsyncSession | None = None` but should assert or guard after decoration. Explicit `None` is not a request for injection. This preserves ordinary Python call binding, but it means wrappers or callers must omit the argument instead of forwarding a nullable value.
`with_session` owns session lifetime only. It does not begin or commit a transaction, so it is naturally suited to reads. Decorated writes must either manage a visible transaction or be called with a session from `db_transaction_scope()` or another caller-owned transaction. Prefer explicit factory or session injection when lifecycle transparency and test substitution matter more than call-site convenience.
---
## Function and Service Boundaries
Template service functions support both standalone and composed use by combining `@with_session` with an optional parameter:
```python
from sqlmodel import func
from sqlmodel import select
@with_session
async def count_items(session: AsyncSession | None = None) -> int:
assert session is not None, "Session must be provided by with_session decorator"
result = await session.exec(select(func.count()).select_from(Item))
return result.one()
```
The standalone call injects and closes a session:
```python
count = await count_items()
```
A larger use case passes one caller-owned session through several decorated functions:
```python
async with session_factory.begin() as session:
count = await count_items(session)
await create_item(payload, session=session)
```
The decorator sees the bound `session` argument and leaves all ownership with the caller. It never creates a SAVEPOINT or nested transaction.
For low-level helpers that should never resolve settings, require a non-optional session and leave them undecorated. Application service objects may store the immutable session factory, but they must not store a mutable session:
```python
class ItemService:
def __init__(self, session_factory: SessionFactory) -> None:
self.session_factory = session_factory
async def find(self, item_id: int) -> Item | None:
async with self.session_factory() as session:
return await find_item(session, item_id)
```
Code that already owns a transaction should call the session-required function directly. Repositories should normally remain in that session-required layer; the service or use-case boundary owns standalone session creation. This avoids optional-session APIs spreading into every data-access function.
---
## SAVEPOINTs and Partial Failure
Use [`begin_nested()`](https://docs.sqlalchemy.org/en/21/orm/session_transaction.html#using-savepoint) only when failure inside one portion of an operation should roll back that portion while preserving the outer transaction:
```python
async with db_transaction_scope() as session:
order = await insert_order(session, payload)
try:
async with session.begin_nested():
await apply_optional_discount(session, order)
except DiscountError:
pass
await reserve_inventory(session, order)
```
Important SAVEPOINT semantics:
- `begin_nested()` starts a root transaction if one is not already active, so call it inside a visible outer transaction when that ownership matters.
- Entering `begin_nested()` unconditionally flushes pending session state, regardless of the `autoflush` setting.
- Successful exit releases the SAVEPOINT; it does not commit the outer transaction.
- Exceptional exit rolls back to the SAVEPOINT and leaves the outer transaction active.
- In SQLAlchemy 2.x, `session.commit()` commits the outermost transaction. Never call it to release a SAVEPOINT; let the nested context manager manage its transaction handle.
Do not create a SAVEPOINT merely because one service calls another. SAVEPOINTs add database work and alter flush and error-recovery behavior. Use them only for explicit partial-failure requirements such as skipping one conflicting row while retaining the rest of a batch.
---
## Framework Integration
Keep framework adapters outside these session primitives. See [FastAPI database integration](fastapi.md) for lifespan ownership, `Annotated` dependency aliases, and read-versus-write request sessions.
---
## Configuration Guidance
- `expire_on_commit=False` is commonly preferred in asyncio applications to reduce accidental post-commit reload behavior.
- `AsyncSession.refresh()` is preferred over broad expiration patterns when state refresh is needed.
- [`async_sessionmaker.begin()`](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html#sqlalchemy.ext.asyncio.async_sessionmaker.begin) is a concise option when one scope must create a session, begin a transaction, commit on success, roll back on failure, and close. Do not use it when borrowing a caller's session.
## SQLModel Alignment
- Use SQLModel as the default model and statement layer while keeping the same session ownership model: one `async_sessionmaker`, one `AsyncSession` per task or unit of work.
- SQLModel does not replace SQLAlchemy async lifecycle primitives; it provides model declaration, validation, and typing ergonomics on top of them.
- Do not mix ad hoc session construction with the canonical session factory.
---
## Concurrency Rules
- One session per concurrent task.
- If work fans out into parallel tasks, each task receives its own AsyncSession.
- Pass sessions explicitly to service functions; avoid mutable global session state.
---
## Anti-Patterns
- A singleton/global AsyncSession reused across tasks or operations.
- Sharing one AsyncSession across parallel tasks.
- Passing an application-global AsyncSession to a repository constructor.
- Creating a new `async_sessionmaker` in each operation.
- Retaining a session factory after its bound engine scope exits.
- Using cached standalone factory resolution when the application factory is already available.
- Assuming `with_session` starts or commits a transaction.
- Forwarding `session=None` to a decorated function when injection was intended.
- Closing or committing a session supplied by the caller.
- Silently starting or committing a transaction on a supplied session.
- Creating a SAVEPOINT for ordinary nested service calls.
- Hiding root transaction, joined transaction, and SAVEPOINT behavior behind one mode-driven `atomic_scope()` helper.
- Calling `session.commit()` inside a SAVEPOINT scope.
- Mixing commit/rollback ownership across layers without a declared boundary.
---
## Operational Checks
- The FastAPI application factory is created inside `database_scope()` and does not outlive its bound engine.
- Cached standalone factories are used only where application-state injection is unavailable.
- `session_scope()` precedence is supplied session, supplied factory, then settings-based resolution.
- Decorated functions receive injection only when the `session` argument is omitted.
- No code path creates AsyncSession in module import side effects.
- Concurrent jobs and operations each create task-local sessions.
---
## Testing Checks
- Service constructors accept a test session factory without framework startup.
- Session-taking access functions accept a transaction-scoped test session directly.
- `session_scope()` tests cover supplied-session, supplied-factory, and settings-resolution precedence.
- `db_transaction_scope()` tests verify commit on success, rollback on failure, session closure, engine disposal, and cache cleanup.
- `with_session` tests cover omitted, positional, keyword, and explicit-`None` session arguments.
- Composition tests verify decorated service calls borrow one caller-owned session without committing it.
- SAVEPOINT tests verify local rollback preserves the outer transaction and successful exit does not commit it.
- Tests that depend on SAVEPOINT timing account for `begin_nested()` flushing pending state on entry.
- Rollback behavior is verified for failed write units.
- Parallel-task tests verify no shared AsyncSession instances.
- Lifecycle tests confirm schema initialization, factory availability, deterministic teardown, and expected cache behavior.
@@ -0,0 +1,124 @@
# SQLModel-First Modeling and Async Boundaries
!!! info "Primary sources"
- [SQLModel documentation](https://sqlmodel.tiangolo.com/)
- [SQLModel features](https://sqlmodel.tiangolo.com/features/)
- [SQLModel advanced guide](https://sqlmodel.tiangolo.com/advanced/)
- [SQLModel FastAPI session dependency tutorial](https://sqlmodel.tiangolo.com/tutorial/fastapi/session-with-dependency/)
- [SQLModel release notes](https://sqlmodel.tiangolo.com/release-notes/)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
??? abstract "Decision metadata"
- Status: adopted
- Decision level: mandatory
- Applies to: api-runtime, workers, tests
- Last reviewed: 2026-08-06
---
## Purpose
Define SQLModel as the primary model layer for async FastAPI applications and explain how it composes with SQLAlchemy's async runtime.
SQLModel is designed for FastAPI, built on Pydantic and SQLAlchemy, and intended to minimize duplication while preserving the capabilities of both. Async engine, session, transaction, and loading behavior still follow SQLAlchemy's asyncio contract.
---
## Scope and Non-Goals
- In scope: table models, API data models, SQLAlchemy interoperability, async session usage, and exception criteria.
- Out of scope: replacing SQLAlchemy's async runtime primitives or claiming that synchronous tutorial examples are async patterns.
---
## Rules
- Default to SQLModel for new table models and API data models.
- Keep SQLAlchemy engine and factory primitives as the runtime base: `create_async_engine` and `async_sessionmaker`. For SQLModel applications, use SQLModel's `AsyncSession` wrapper so its typed `exec()` API remains available.
- Keep transaction and session ownership policies identical whether models are SQLAlchemy Declarative or SQLModel.
- Use SQLModel inheritance to share validated fields while keeping table, create, update, and public contracts distinct where their semantics differ.
- Use SQLAlchemy declarative models only for a concrete unsupported mapping or third-party constraint; document the reason.
- Use SQLAlchemy relationship loading options explicitly on async paths.
---
## Recommended Patterns
### Pattern A: Data model split for API boundaries
Use distinct models for persistence and external contracts.
```python
from sqlmodel import Field, SQLModel
class UserBase(SQLModel):
email: str
display_name: str
class User(UserBase, table=True):
id: int | None = Field(default=None, primary_key=True)
class UserCreate(UserBase):
pass
class UserRead(UserBase):
id: int
```
### Pattern B: Keep SQLModel models with the async runtime
```python
from sqlmodel import select
async with database_scope(settings.database_url) as session_factory:
async with session_factory() as session:
users = (await session.exec(select(User))).all()
```
`database_scope()` enters the cached engine lifecycle, initializes registered SQLModel metadata by default, and yields the application session factory while SQLModel supplies the model and statement layer. `sqlmodel.select()` keeps SQLModel's typing-oriented statement construction, and SQLModel's `AsyncSession` adds typed `exec()` results while retaining SQLAlchemy's async lifecycle and transaction behavior. Import `AsyncSession` from `sqlmodel.ext.asyncio.session` when working with SQLModel models; use SQLAlchemy's `AsyncSession` only when the code intentionally has no SQLModel dependency.
---
## Interoperability Notes
- A SQLModel table model is a SQLAlchemy model and can participate in SQLAlchemy relationships, statements, loader options, and sessions.
- A SQLModel model is also a Pydantic model; non-table models are useful for request and response contracts.
- SQLModel's official FastAPI dependency tutorial currently uses synchronous `Session`; translate the ownership pattern, not the concrete session type, for async applications.
- SQLModel's advanced guide still lists dedicated async documentation as future work, so use SQLAlchemy's asyncio documentation as the authority for runtime mechanics.
- Prefer one query style per module to reduce cognitive overhead.
- Keep loader strategies explicit in async paths to avoid implicit I/O surprises.
---
## Anti-Patterns
- Treating SQLModel as an alternative to SQLAlchemy rather than a layer built on it.
- Copying a synchronous `Session` example into an async request path.
- Constructing sessions in handlers instead of using the application session factory.
- Mixing multiple query/session idioms within the same module without clear conventions.
---
## Operational Checks
- New model modules are SQLModel-first; exceptions state the unsupported need or constraint.
- Session/transaction ownership remains consistent across both model styles.
- Table, create, update, and public models share fields intentionally without exposing persistence-only data.
---
## Testing Checks
- Module-level tests verify CRUD semantics for SQLModel models through `AsyncSession`.
- API tests verify response/request model behavior for SQLModel-based endpoints.
- Relationship tests verify async loader strategies do not depend on implicit I/O.
---
## Version Checks
- Verify installed SQLModel, SQLAlchemy, and Pydantic versions together when using newly added typing or ORM features.
@@ -57,8 +57,3 @@ Describe what this concept governs and why it exists.
- Test 1
- Test 2
---
## Migration Notes
- Staged rollout notes and compatibility caveats.
@@ -0,0 +1,172 @@
# Testing Database Targets and Data
Use the same engine and session primitives in production and tests. Tests select a different URL and, when transaction isolation is required, bind a test session factory to one test-owned connection and outer transaction. They do not replace repositories, services, or SQLAlchemy mechanics with mocks.
## Decision Table
| Test need | Database target | Isolation approach | What it proves |
|---|---|---|---|
| Fast, serial application tests | `sqlite+aiosqlite://` | Per-test engine or connection-bound session factory over an outer transaction | ORM mappings and ordinary application behavior |
| Async code using multiple simultaneous sessions | Named SQLite shared-cache URL or temporary SQLite file | Per-test schema or cleanup strategy | Concurrent-session behavior without a database server |
| PostgreSQL-specific behavior | Dedicated PostgreSQL test database | Per-test outer transaction and SAVEPOINT | SQL, constraints, types, locking, and migrations that SQLite cannot represent |
SQLite is a useful fast target, not a drop-in PostgreSQL substitute. Keep a small PostgreSQL integration suite for PostgreSQL-specific queries, extensions, row locking, JSON semantics, collations, isolation, and migration validation.
## Shared Construction Primitives
Make the application factory accept a database URL or settings object. Production, workers, and ordinary integration tests enter the same [`database_scope()`](session.md#database-and-convenience-scopes). Tests enter the lower-level [`engine_scope()`](engine.md#owning-engine-scope) only when they need direct engine or connection ownership for schema setup, an outer transaction, or engine-specific assertions:
```python
from collections.abc import AsyncGenerator
import pytest_asyncio
from sqlalchemy.ext.asyncio import AsyncEngine
from .engine import engine_scope
@pytest_asyncio.fixture(scope="session", loop_scope="session")
async def test_engine(database_url: str) -> AsyncGenerator[AsyncEngine]:
async with engine_scope(database_url) as engine:
yield engine
```
Production passes its `postgresql+asyncpg://...` URL to `database_scope()`. A local SQLite run passes `sqlite+aiosqlite:///./app.db`. Tests pass a dedicated test URL to `database_scope()` or `engine_scope()` and receive schema initialization, deterministic disposal, and engine-cache cleanup when the context exits. Do not create an engine during module import: that makes it easy for tests to retain the production URL before an override is applied.
Use migrations to provision an integration database when migrations are part of the release contract. `metadata.create_all()` is appropriate for focused ORM tests only when it accurately represents the schema under test. Import all table models before creating metadata; [SQLModel documents that model-registration order matters](https://sqlmodel.tiangolo.com/tutorial/fastapi/tests/#import-table-models).
## Transactional Async Fixture
For tests that exercise code which commits, start an outer transaction on one test connection. Bind a test `SessionFactory` to that connection with `join_transaction_mode="create_savepoint"`. SQLAlchemy documents this as its test-suite pattern: sessions created by the factory resolve their commits through SAVEPOINTs while fixture teardown rolls back the outer transaction.
```python
from collections.abc import AsyncGenerator
import pytest_asyncio
from sqlalchemy.ext.asyncio import AsyncEngine
from sqlalchemy.ext.asyncio import async_sessionmaker
from sqlmodel.ext.asyncio.session import AsyncSession
from .session import SessionFactory
@pytest_asyncio.fixture(scope="function", loop_scope="session")
async def session_factory(test_engine: AsyncEngine) -> AsyncGenerator[SessionFactory]:
async with test_engine.connect() as connection:
transaction = await connection.begin()
factory = async_sessionmaker(
bind=connection,
class_=AsyncSession,
expire_on_commit=False,
join_transaction_mode="create_savepoint",
)
try:
yield factory
finally:
await transaction.rollback()
```
Each factory call still creates a distinct `AsyncSession`, matching [session factory mechanics](session.md#session-factory-mechanics). The factory belongs to the fixture's engine and outer transaction and must not escape either scope.
For service tests that pass a caller-owned session into decorated or undecorated service functions, derive that session from the same factory:
```python
@pytest_asyncio.fixture(scope="function", loop_scope="session")
async def session(session_factory: SessionFactory) -> AsyncGenerator[AsyncSession]:
async with session_factory() as test_session:
await test_session.begin()
yield test_session
```
The explicit `begin()` gives test code one visible transaction from the start. Session closure rolls back unfinished work; the outer connection transaction remains the final isolation boundary even if application code commits its SAVEPOINT.
For FastAPI request tests, override `_get_session_factory` so the production `SessionDep` retains its session-creation and cleanup behavior while receiving the test-bound factory. Always remove the override after the test because [FastAPI dependency overrides](https://fastapi.tiangolo.com/advanced/testing-dependencies/) are stored in a mutable application-level dictionary.
```python
from collections.abc import Generator
import pytest
from fastapi import FastAPI
from .fastapi import _get_session_factory
from .session import SessionFactory
@pytest.fixture
def app_with_test_database(app: FastAPI, session_factory: SessionFactory) -> Generator[FastAPI]:
def get_test_session_factory() -> SessionFactory:
return session_factory
app.dependency_overrides[_get_session_factory] = get_test_session_factory
try:
yield app
finally:
app.dependency_overrides.pop(_get_session_factory, None)
```
Construct `app` with test settings before lifespan starts so startup cannot resolve the production URL. The override changes request session creation; it does not prevent lifespan from entering its configured `database_scope()`.
The connection-bound factory is deliberately serial even though it creates distinct sessions: those sessions still share one connection and outer transaction. A test that verifies concurrently active sessions must use independent connections and a database target that supports them.
## SQLite Targets
### Serial in-memory tests
Use `sqlite+aiosqlite://` for a fresh in-memory database when the test runs all database work serially. SQLAlchemy's `aiosqlite` dialect uses a single-connection `StaticPool` for this target, so all sessions share one SQLite transaction state. One session's rollback can discard another session's uncommitted work.
`engine_scope()` imports the model package and creates the schema by default, then disposes the engine and clears cached resolution deterministically:
```python
from collections.abc import AsyncGenerator
import pytest_asyncio
from sqlalchemy.ext.asyncio import AsyncEngine
from .engine import engine_scope
@pytest_asyncio.fixture(scope="session", loop_scope="session")
async def test_engine() -> AsyncGenerator[AsyncEngine]:
async with engine_scope("sqlite+aiosqlite://") as engine:
yield engine
```
### Concurrent in-memory tests
Do not use the default `:memory:` target for tests that have multiple active sessions or tasks. Use a named shared-cache database instead, with a name unique to the test process:
```text
sqlite+aiosqlite:///file:test-suite?mode=memory&cache=shared&uri=true
```
This lets connections share the same in-memory database while retaining independent transaction state. A temporary file URL such as `sqlite+aiosqlite:////tmp/test.db` is often simpler when test isolation or cleanup tooling already manages files.
For both SQLite forms, enable and test the constraints your application depends on. SQLite foreign-key enforcement is disabled by default, and its transaction behavior has driver-specific differences. Keep PostgreSQL integration coverage for behavior that SQLite cannot faithfully model.
## Test Data Practices
- Build only the data a test needs, through named factory functions or pytest fixtures rather than a large global seed.
- Give each fixture a domain meaning, such as `active_account`, `expired_subscription`, or `admin_user`; avoid opaque rows with unexplained defaults.
- Set values relevant to the assertion explicitly, including timestamps, permissions, statuses, and unique identifiers. Use fixed clocks or injected clock values instead of the wall clock.
- Construct object graphs through relationships, then `await session.flush()` before reading generated identifiers or passing foreign keys onward. `flush()` exercises database constraints without ending the test transaction.
- Seed prerequisite data before creating a client request. Let the endpoint own the mutation being asserted; do not pre-insert the row that the endpoint is supposed to create.
- Use `commit()` in fixture setup only when the test specifically needs to prove post-commit behavior. With the transactional fixture, this remains isolated through the outer rollback.
- Keep shared reference data immutable and explicit. If it must be reused for performance, load it once into a dedicated test database and reset all mutable tables between tests; never depend on test order.
- Include both valid and constraint-breaking graphs where a behavior depends on foreign keys, uniqueness, nullability, or cascading deletes. SQLite-only tests should not be the sole evidence for PostgreSQL constraints.
## Completion Checks
- A test run cannot reach the production URL; production credentials are absent from the test environment.
- Production PostgreSQL, local SQLite, and in-memory SQLite all use `database_scope()` unless a test explicitly needs lower-level engine or connection ownership.
- Every test or fixture scope owns its override, session factory, connection, transaction, and session cleanup; the session-scoped engine fixture owns disposal and cache cleanup.
- Request tests override `_get_session_factory`, preserving production request-session creation and cleanup behavior.
- Test data is deterministic, minimal, and expresses the scenario under test.
- PostgreSQL integration tests cover every PostgreSQL-specific contract and run against migrations where migrations are shipped.
## Sources
- [SQLAlchemy: joining a session into an external transaction](https://docs.sqlalchemy.org/en/21/orm/session_transaction.html#joining-a-session-into-an-external-transaction-such-as-for-test-suites)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy SQLite dialect and async in-memory pooling](https://docs.sqlalchemy.org/en/21/dialects/sqlite.html#using-a-memory-database-with-multiple-coroutines)
- [FastAPI dependency overrides](https://fastapi.tiangolo.com/advanced/testing-dependencies/)
- [SQLModel testing with FastAPI](https://sqlmodel.tiangolo.com/tutorial/fastapi/tests/)
- [pytest-asyncio fixtures](https://pytest-asyncio.readthedocs.io/en/stable/how-to-guides/index.html)
@@ -30,7 +30,8 @@ Define consistent transaction demarcation for async SQLAlchemy so write behavior
- Every mutating use case must run inside an explicit transaction boundary.
- Prefer `async with session.begin():` for write units.
- Keep transaction ownership at service/use-case boundary, not deep in helper internals.
- Keep transaction ownership at a service, use-case, or explicitly documented complete-operation boundary, not deep in helper internals.
- An optional-session write may own one transaction when omitting the session clearly means standalone execution; a supplied session must remain caller-owned.
- Read paths should not auto-upgrade into hidden write behavior.
- On exception in a transaction block, rely on rollback semantics and propagate or map exceptions intentionally.
@@ -82,7 +83,7 @@ Use nested transactions only when partial failure semantics are explicitly requi
## Anti-Patterns
- Multiple commits scattered across one logical use case.
- Helper functions that commit/rollback without caller awareness.
- Helper functions that commit or roll back without an explicit ownership contract.
- Mixing implicit and explicit transaction styles in confusing ways.
- Using savepoints as a default pattern rather than a targeted tool.
@@ -90,8 +91,8 @@ Use nested transactions only when partial failure semantics are explicitly requi
## Operational Checks
- All mutating service functions declare one clear transaction boundary.
- No repository/helper performs hidden commit calls.
- All mutating services and complete operations declare one clear transaction boundary.
- No repository or helper performs hidden direct commit calls; standalone ownership is expressed through a documented transaction scope.
- Transaction style is consistent across handlers and workers.
---
@@ -102,10 +103,3 @@ Use nested transactions only when partial failure semantics are explicitly requi
- Failure path test verifies rollback behavior.
- Tests cover concurrency-sensitive write flows.
- Savepoint usage (if present) has dedicated behavior tests.
---
## Migration Notes
- First stabilize session scope, then normalize transaction ownership.
- Replace ad hoc commit patterns incrementally with bounded write units.
+10 -32
View File
@@ -1,27 +1,6 @@
---
name: copilot-customization
description: 'Plan, create, review, and debug GitHub Copilot and VS Code agent customizations, including instructions, prompt files, skills, custom agents, hooks, MCP servers, and repo-specific personal-mcp skill integration.'
argument-hint: 'What Copilot behavior are you customizing, and should it be workspace-scoped, personal, or exposed as an MCP skill resource?'
x-personal-mcp:
id: copilot-customization
version: 1.0.0
tags:
- copilot
- vscode
- customization
- instructions
- prompts
- agent-skills
- custom-agents
- hooks
- mcp
- personal-mcp
- skills
capabilities:
- resource://skills/copilot-customization/document
depends_on:
- new-skill
- zensical-docs
---
# Copilot Customization
@@ -82,31 +61,30 @@ Use either:
Choose one of these patterns:
1. Direct URI strategy:
- Reference known resources directly, such as:
- `resource://catalog/skills_index`
- `resource://catalog/skills/{skill_id}`
- `resource://skills/<skill-id>/document`
- `resource://skills/<skill-id>/references/<ref-id>`
- Read `skill://<skill-name>/SKILL.md` when the required skill is known.
- Read `skill://<skill-name>/_manifest` only when supporting material may be useful.
- Read selected supporting files at `skill://<skill-name>/<supporting-path>`.
2. Discovery-first strategy:
- Start at catalog discovery (`resource://catalog/skills_index`), select the best skill match, then load the skill document and only the minimal references needed.
- List resources, compare native main-resource names and descriptions, then load the best matching `SKILL.md`.
- In tool-only clients, use only `list_resources` and `read_resource` for the same sequence.
### Authoring guidance for shims
1. Keep shim content short and procedural; avoid copying large guidance blocks from Personal MCP.
2. State trigger conditions clearly (for example: "when creating a new skill" or "when editing docs contracts").
3. Specify whether to use direct URIs or discovery for that repo's common workflows.
4. Prefer loading only the most relevant skill document first; expand to references only when needed.
3. Specify whether to use a direct native URI or resource listing for that repo's common workflows.
4. Prefer loading only the most relevant main file first; inspect its manifest only when needed.
5. For stable repeated workflows, use explicit URIs. For broader or ambiguous requests, use discovery-first.
### Minimal shim examples
Instruction-style shim intent:
1. "For markdown edits (`applyTo: '**/*.md'`), load `resource://skills/zensical-docs/document` and apply Zensical-native documentation conventions unless they conflict with expected MkDocs compatibility."
1. "For markdown edits (`applyTo: '**/*.md'`), load `skill://zensical-docs/SKILL.md` and apply Zensical-native documentation conventions unless they conflict with expected MkDocs compatibility."
Prompt-style shim intent:
1. "For docs authoring tasks, consult `resource://skills/zensical-docs/document`, summarize the relevant authoring constraints, then propose the smallest markdown change for this repository."
1. "For docs authoring tasks, consult `skill://zensical-docs/SKILL.md`, summarize the relevant authoring constraints, then propose the smallest markdown change for this repository."
### Validation for shim implementation
@@ -134,7 +112,7 @@ Before finishing:
3. Confirm names match directory names where VS Code requires it.
4. Confirm descriptions include the phrases users are likely to ask for.
5. Confirm extra skill resources are linked from `SKILL.md`.
6. Confirm repo skill metadata exposes the correct `resource://skills/<skill-id>/document` capability.
6. Confirm native discovery exposes `skill://<skill-name>/SKILL.md`, `_manifest`, and supporting-file reads.
7. State any remaining ambiguity or user choice, such as personal vs workspace scope.
## Output Contract
@@ -1,252 +0,0 @@
---
name: fastapi-async-sqlalchemy-modernization
description: 'Create a step-by-step modernization plan for an existing FastAPI app using SQLAlchemy async patterns, context managers, and AsyncExitStack. Use when: planning migration from legacy DB setup, standardizing async engine/session lifecycles, defining transaction boundaries, and aligning with SQLAlchemy 2.x best practices.'
argument-hint: 'What is your current FastAPI + SQLAlchemy setup (sync/async driver, session pattern, lifespan usage, and deployment model)?'
x-personal-mcp:
id: fastapi-async-sqlalchemy-modernization
version: 1.0.0
tags:
- fastapi
- sqlalchemy
- async
- modernization
capabilities:
- resource://skills/fastapi-async-sqlalchemy-modernization/document
depends_on: []
---
# FastAPI Async SQLAlchemy Modernization Plan
Create an implementation-ready plan that brings an existing FastAPI application in line with modern async SQLAlchemy practices, with explicit resource lifecycles and deterministic cleanup using async context managers and AsyncExitStack.
Primary targets: PostgreSQL with asyncpg and SQLite with aiosqlite.
## When to Use
- Existing FastAPI app has ad hoc database setup or mixed sync/async access.
- Session management is inconsistent across routes/services.
- Lifespan startup and shutdown work is spread across globals and side effects.
- Team needs a migration plan first, not immediate large-scale rewrites.
## Outcome
Produce a practical modernization plan with:
- Current-state gap assessment.
- Target architecture for engine/session/transaction lifecycle.
- Branch-based migration path (low-risk staged rollout).
- Quality gates and completion checks.
- Risks, rollback strategy, and test plan.
## Top-Level Concepts
Use these concepts as the planning backbone:
1. Engine lifecycle and ownership:
One AsyncEngine per process for each DB URL, created once and disposed explicitly when the app lifecycle ends.
See the [engine lifecycle reference](references/engine.md).
2. Session factory and scope:
Use async_sessionmaker for configuration; create one AsyncSession per request or unit-of-work, never shared across concurrent tasks.
See the [session management reference](references/session.md).
3. Transaction boundaries:
Prefer context-managed begin blocks for write units and explicit read-only sessions for queries.
See the [transaction boundaries reference](references/transactions.md).
4. Lifespan composition:
Compose startup/shutdown resources with AsyncExitStack so cleanup is deterministic and ordered.
See the [engine lifecycle reference](references/engine.md).
5. Dependency injection:
Provide sessions via FastAPI dependencies with async generators/context managers, not globals.
See the [session management reference](references/session.md).
6. Implicit I/O control in ORM:
Avoid accidental lazy loads; use explicit eager-loading/refresh strategies for asyncio safety.
See the [implicit I/O reference](references/implicit_io.md).
7. Observability and resilience:
Add pool/connection settings, logging, timeout, and health checks as first-class plan items.
See the [observability reference](references/observability.md).
### Concept Reference Map
| Concept | Reference |
|---|---|
| Engine lifecycle and ownership | [Engine lifecycle reference](references/engine.md) |
| Session factory and scope | [Session management reference](references/session.md) |
| Transaction boundaries | [Transaction boundaries reference](references/transactions.md) |
| Lifespan composition | [Engine lifecycle reference](references/engine.md) |
| Dependency injection | [Session management reference](references/session.md) |
| Implicit I/O control in ORM | [Implicit I/O reference](references/implicit_io.md) |
| Observability and resilience | [Observability reference](references/observability.md) |
## Decision Points
Use these branching decisions before proposing migration steps.
| Decision | Branch A | Branch B |
|---|---|---|
| DB driver | Already async driver (e.g. asyncpg, aiosqlite): modernize in place | Sync driver: plan driver migration first |
| ORM usage | Already ORM 2.x style (`select`, `session.execute`) | Legacy Query API: add compatibility stage and refactor incrementally |
| Session scope | Request-scoped already | Global/shared sessions found: prioritize session-scope fix first |
| Lifespan | Existing FastAPI lifespan hook | No lifespan hook: introduce lifespan before broader DB changes |
| Concurrency | Background jobs/tasks use DB | No background DB use |
| Transaction style | Explicit context-managed transactions | Implicit/autobegin side effects |
## Procedure
### Step 0: Audit Current State
Inventory the app and write a concise gap list.
- Engine creation location(s) and count.
- Driver URL(s) and async compatibility.
- Session creation patterns in routes/services/background tasks.
- Transaction handling style (explicit begin/commit/rollback vs implicit).
- Lifespan startup/shutdown and cleanup behavior.
- ORM loading patterns that may trigger implicit I/O.
Completion check: every DB touchpoint is mapped to its engine, session, and transaction source.
### Step 1: Define the Target Runtime Model
Define one canonical model to migrate toward.
- Create AsyncEngine once per process.
- Configure async_sessionmaker once.
- Use per-request AsyncSession dependency.
- Keep one AsyncSession per concurrent task.
- Use context-managed transactions for writes.
Completion check: architecture diagram can explain where engine/session are created, used, and closed.
### Step 2: Plan Engine Modernization
Plan engine creation and pool behavior.
- Use `create_async_engine()` with async dialect URL.
- Standardize pool settings and pre-ping strategy where relevant.
- Decide isolation level strategy at engine level (avoid ad hoc per-operation switching unless justified).
- Define explicit disposal policy for short-lived scopes and tests.
Completion check: engine configuration is centralized and no per-request engine creation remains.
### Step 3: Plan Session Lifecycle Modernization
Define session factory and request dependency pattern.
- Build `async_sessionmaker(engine, expire_on_commit=False)` unless a strict reason says otherwise.
- Provide session via dependency that yields exactly one AsyncSession.
- Explicitly prohibit sharing a single AsyncSession across concurrent tasks.
- Prefer direct dependency passing over async_scoped_session for new designs.
Completion check: all route/service entry points receive a session from one canonical dependency.
### Step 4: Plan Transaction Demarcation
Establish consistent write and read behavior.
- Writes: `async with session.begin(): ...` for atomic units.
- Reads: execute in managed session context with explicit loader options.
- Nested/SAVEPOINT use only where required; call out backend caveats.
- Define rollback behavior for service-layer exceptions.
Completion check: every mutating use case has a declared transaction boundary.
### Step 5: Compose Lifespan with AsyncExitStack
Use async context composition as the preferred orchestration pattern.
```python
from contextlib import AsyncExitStack, asynccontextmanager
from fastapi import FastAPI
@asynccontextmanager
async def lifespan(app: FastAPI):
async with AsyncExitStack() as stack:
# Compose resources in acquisition order; cleanup is automatic in reverse order.
engine = create_async_engine(settings.database_url)
stack.push_async_callback(engine.dispose)
session_factory = async_sessionmaker(engine, expire_on_commit=False)
app.state.session_factory = session_factory
# Add other async resources with stack.enter_async_context(...) as needed.
yield
```
Planning rules:
- Register every acquired resource with AsyncExitStack at acquisition time.
- Prefer `enter_async_context()` for resources that already expose async context managers.
- Prefer `push_async_callback()` for async cleanup callables.
- Keep resource ownership in lifespan, not in route handlers.
Completion check: startup/shutdown ordering is explicit and deterministic.
### Step 6: Prevent Implicit ORM I/O Under Asyncio (Advisory Mode)
Plan for explicit loading behavior, but treat this as progressive guidance rather than a hard gate.
- Recommend eager-loading strategies (for example selectin-style loading) where relationship access is required.
- For lazy/deferred attributes, define explicit awaitable or refresh paths on high-risk and high-traffic paths first.
- Document model-level defaults and known exceptions so teams can migrate incrementally.
Completion check: critical request paths have explicit loading plans; non-critical paths have tracked follow-up items.
### Step 7: Testing and Verification Plan
Create modernization quality gates.
- Unit tests for session dependency and transaction behavior.
- Integration tests for commit/rollback semantics.
- Concurrency tests confirming one-session-per-task behavior.
- Lifespan tests verifying cleanup calls and ordering.
- Health/readiness tests including DB connectivity checks.
Completion check: all quality gates pass under the target async configuration.
### Step 8: Rollout Strategy
Plan low-risk migration phases.
1. Introduce centralized engine/session factory and lifespan orchestration.
2. Migrate read paths to new session dependency.
3. Migrate write paths to explicit transaction blocks.
4. Remove legacy globals/helpers and dead code.
5. Enable stricter linting/review checks for forbidden patterns.
Completion check: no legacy session/engine creation path remains in production code.
## Quality Criteria
A plan is complete only when it includes:
- Clear current vs target architecture.
- Branch decisions with rationale.
- Explicit context-manager patterns for resource ownership.
- AsyncExitStack composition strategy.
- Transaction policy and exception behavior.
- Concrete tests and rollout checkpoints.
- A documented advisory backlog for non-critical implicit I/O improvements.
## Anti-Patterns to Flag
- Creating engines inside request handlers.
- Sharing one AsyncSession across concurrent tasks.
- Implicit commit/rollback behavior with unclear ownership.
- Global mutable session state.
- Lifespan cleanup that depends on implicit garbage collection.
## Output Contract
Return the plan as:
1. Current-state gap summary.
2. Target architecture summary.
3. Phased migration checklist with branch notes.
4. Risk register and rollback approach.
5. Verification matrix (tests + operational checks).
## References
!!! info "Primary sources"
- [SQLAlchemy engine and connections](https://docs.sqlalchemy.org/en/21/core/connections.html)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [Python async context managers and AsyncExitStack](https://docs.python.org/3/library/contextlib.html)
@@ -1,133 +0,0 @@
# Async SQLAlchemy Engine
!!! info "Primary sources"
- [SQLAlchemy connections](https://docs.sqlalchemy.org/en/21/core/connections.html)
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy pooling and multiprocessing](https://docs.sqlalchemy.org/en/21/core/pooling.html#pooling-multiprocessing)
- [FastAPI lifespan events](https://fastapi.tiangolo.com/advanced/events/)
---
## Engine Ownership Model
Create one async engine per process per database URL and keep it for the app lifetime.
- SQLAlchemy guidance: the engine is intended as a long-lived, concurrent registry over pooled DB connections, not a per-request object.
- In FastAPI, app startup and shutdown ownership belongs in lifespan.
- Use `FastAPI(lifespan=...)` (not startup/shutdown events) for modern lifecycle wiring.
!!! tip "Practical rule"
- Exactly one `create_async_engine(...)` call in app bootstrap code.
- Zero `create_async_engine(...)` calls in request handlers.
---
## Canonical Lifespan Pattern (AsyncExitStack)
Use `@asynccontextmanager` + `AsyncExitStack` to make teardown deterministic and composable.
```python
from contextlib import AsyncExitStack, asynccontextmanager
from fastapi import FastAPI
from sqlalchemy.ext.asyncio import AsyncEngine, create_async_engine
@asynccontextmanager
async def lifespan(app: FastAPI):
async with AsyncExitStack() as stack:
engine: AsyncEngine = create_async_engine(
app.state.settings.database_url,
pool_pre_ping=True,
# Optional examples:
# echo=app.state.settings.sql_echo,
# pool_size=10,
# max_overflow=20,
)
app.state.engine = engine
# Ensure engine disposal always runs at shutdown.
stack.push_async_callback(engine.dispose)
yield
app = FastAPI(lifespan=lifespan)
```
Why this pattern:
- FastAPI executes code before `yield` at startup and after `yield` at shutdown.
- `AsyncExitStack` lets you register multiple async cleanups in one place while preserving order.
- Explicit disposal (directly awaited or via `AsyncExitStack` callback) avoids event-loop-closed warnings when objects fall out of scope.
---
## Driver URLs (Project Requirement: asyncpg + aiosqlite)
Use SQLAlchemy async driver URLs:
- PostgreSQL: `postgresql+asyncpg://user:pass@host:5432/dbname`
- SQLite: `sqlite+aiosqlite:///./app.db`
!!! warning "Driver compatibility"
- Do not mix sync drivers, for example `psycopg2`, with `create_async_engine()`.
- Keep URL construction centralized in settings/config, not in feature modules.
---
## Pooling Defaults and Tuning
Default behavior is usually correct first:
- Async engines use async-compatible pooling (`AsyncAdaptedQueuePool`) by default.
- Start with defaults, then tune from observed load (`pool_size`, `max_overflow`, `pool_timeout`, `pool_recycle`).
- Enable `pool_pre_ping=True` for safer stale-connection handling in long-running services.
When to switch pool strategy:
- `NullPool` if you explicitly need no pooling (special environments, some tests, or strict cross-loop constraints).
- Keep in mind this increases connect/disconnect churn.
---
## Disposal Semantics
`engine.dispose()` replaces/disposes the pool, but only checked-in connections are immediately closed.
Rules:
- Dispose when the app is shutting down.
- Dispose before reusing an engine across event loops.
- In forked child-process initialization, use `engine.dispose(close=False)` (sync API guidance) so child processes do not touch parent-held connections.
Avoid relying on garbage collection for engine cleanup in async code.
---
## Event Loop and Process Boundaries
Do not share pooled connections across boundaries:
- Multiple event loops: do not reuse the same pooled async engine across loops unless you intentionally disable pooling (`NullPool`) or dispose before handoff.
- Multiprocessing/fork: pooled connections must not be inherited for active use across process boundaries.
This prevents broken socket state and cross-process connection corruption.
---
## What Not to Do
- Create an engine inside every request dependency.
- Create/dispose engines inside repository methods.
- Keep engine creation as a hidden side effect of import-time module globals.
- Use deprecated FastAPI startup/shutdown events together with lifespan.
---
## Engine Design Checklist
- One engine per process per DB URL.
- Engine created in lifespan startup.
- Engine disposed in lifespan shutdown.
- Async driver URL matches backend (`asyncpg` or `aiosqlite`).
- Pooling strategy is explicit for non-default needs.
- No request-path engine creation.
@@ -1,141 +0,0 @@
# Async SQLAlchemy Session Management
!!! info "Primary sources"
- [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html)
- [SQLAlchemy session basics](https://docs.sqlalchemy.org/en/21/orm/session_basics.html)
- [FastAPI dependencies with yield](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/)
??? abstract "Decision metadata"
- Status: adopted
- Decision level: mandatory
- Applies to: api-runtime, workers, tests
- Last reviewed: 2026-06-17
---
## Purpose
Define one canonical session model for FastAPI + SQLAlchemy asyncio:
- configure one shared session factory,
- create one AsyncSession per request or per unit-of-work,
- never share one AsyncSession across concurrent tasks.
---
## Scope and Non-Goals
- In scope: session factory creation, FastAPI dependency wiring, request/task scoping, transaction demarcation.
- Out of scope: ORM model design, query optimization strategy, schema migration tooling.
---
## Rules
- Create `async_sessionmaker` once from app-owned AsyncEngine.
- Use a fresh AsyncSession for each request or explicit unit-of-work.
- Do not share AsyncSession across `asyncio.gather()` or parallel tasks.
- Prefer direct dependency injection over global scoped-session patterns in new code.
- Use explicit transaction boundaries (`async with session.begin():`) for writes.
---
## Canonical FastAPI Dependency Pattern
```python
from collections.abc import AsyncIterator
from fastapi import Depends, Request
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
def get_session_factory(request: Request) -> async_sessionmaker[AsyncSession]:
return request.app.state.session_factory
async def get_db_session(
session_factory: async_sessionmaker[AsyncSession] = Depends(get_session_factory),
) -> AsyncIterator[AsyncSession]:
async with session_factory() as session:
yield session
```
Route usage:
```python
from fastapi import APIRouter, Depends
from sqlalchemy.ext.asyncio import AsyncSession
router = APIRouter()
@router.post("/items")
async def create_item(session: AsyncSession = Depends(get_db_session)) -> dict:
async with session.begin():
# write operations here
...
return {"status": "ok"}
```
---
## Configuration Guidance
Typical session factory setup:
```python
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
session_factory = async_sessionmaker(
engine,
class_=AsyncSession,
expire_on_commit=False,
)
```
Notes:
- `expire_on_commit=False` is commonly preferred in asyncio applications to reduce accidental post-commit reload behavior.
- `AsyncSession.refresh()` is preferred over broad expiration patterns when state refresh is needed.
---
## Concurrency Rules
- One session per concurrent task.
- If work fans out into parallel tasks, each task receives its own AsyncSession.
- Pass sessions explicitly to service functions; avoid mutable global session state.
---
## Anti-Patterns
- A singleton/global AsyncSession reused across requests.
- Sharing one AsyncSession across parallel tasks.
- Hidden session creation in lower repository helpers with no caller control.
- Mixing commit/rollback ownership across layers without a declared boundary.
---
## Operational Checks
- Exactly one `async_sessionmaker` is registered in app lifecycle.
- Request handlers receive sessions from one canonical dependency.
- No code path creates AsyncSession in module import side effects.
- Background jobs and API handlers each create task-local sessions.
---
## Testing Checks
- Dependency override exists for test session factory.
- Rollback behavior is verified for failed write units.
- Parallel-task tests verify no shared AsyncSession instances.
- Lifespan tests confirm session factory is initialized and teardown-safe.
---
## Migration Notes
- If current code uses global/shared sessions, fix scope first before refactoring query style.
- If legacy sync patterns are present, keep session boundary rules stable while migrating incrementally.
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@@ -1,17 +1,6 @@
---
name: fastapi-uv-docker
description: 'Audit and migrate an existing Python project to best practices for a cloud-native ASGI FastAPI app managed with uv and run with uvicorn in Docker. Use when: conforming a project to production standards, setting up src layout, configuring pyproject.toml, writing multi-stage Dockerfiles, wiring lifespan and settings, adding health endpoints, enforcing non-root container user, migrating from requirements.txt to uv.'
argument-hint: 'What is the current state of the project (bare Python, requirements.txt, pip, etc.)?'
x-personal-mcp:
id: fastapi-uv-docker
version: 1.0.0
tags:
- fastapi
- uv
- docker
capabilities:
- resource://skills/fastapi-uv-docker/document
depends_on: []
---
# FastAPI Project Best Practices
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@@ -0,0 +1,48 @@
---
name: mcp-details
description: "Reference hub for MCP and FastMCP source documentation links. Use when you need authoritative protocol, SDK, transport, and deployment docs without loading broad implementation guidance."
---
# MCP Details
This skill is a reference index only. It is optimized for fast retrieval of upstream documentation links for MCP and FastMCP.
## When to Use
- You need official MCP protocol and architecture docs.
- You need MCP SDK links for Python or TypeScript.
- You need FastMCP docs and source references.
- You need ecosystem links for tooling, inspection, and client configuration.
## How To Use This Skill
1. Classify the request by intent: protocol, SDK usage, FastMCP, or ecosystem integration.
2. Open only the matching reference page first.
3. Load at most one additional reference page if the request spans multiple areas.
4. Return links grouped by category, with a one-line reason for each group.
## Intent Router
1. MCP fundamentals, protocol architecture, resources, tools, prompts, transports, security: [mcp-protocol-and-spec.md](./references/mcp-protocol-and-spec.md)
2. MCP SDK and FastMCP implementation references for Python and TypeScript: [sdk-and-fastmcp.md](./references/sdk-and-fastmcp.md)
3. MCP client integration and operational tooling references: [ecosystem-and-tooling.md](./references/ecosystem-and-tooling.md)
## Load Order
1. Start with the single best-match reference page from the Intent Router.
2. If the question includes both protocol and implementation details, load [mcp-protocol-and-spec.md](./references/mcp-protocol-and-spec.md) then [sdk-and-fastmcp.md](./references/sdk-and-fastmcp.md).
3. Load [ecosystem-and-tooling.md](./references/ecosystem-and-tooling.md) only when the request includes client setup, inspector usage, or deployment/operations context.
## Load Budget
1. Single-focus request: 1 reference page.
2. Mixed protocol and implementation request: 2 reference pages.
3. Broad audit or migration planning request: up to 3 reference pages.
## Output Contract
When this skill is applied, return:
1. Which reference files were consulted.
2. The discovery path used (intent classification and load order).
3. Curated source-document links grouped by topic.
4. Any notable gaps or ambiguities in the currently indexed links.
@@ -0,0 +1,29 @@
# Ecosystem and Tooling
Use this page for MCP client setup, operational tools, and integration references.
## VS Code and Copilot MCP Integration
!!! info "VS Code MCP docs"
- [VS Code MCP servers overview](https://code.visualstudio.com/docs/agent-customization/mcp-servers)
- [VS Code MCP configuration reference](https://code.visualstudio.com/docs/agents/reference/mcp-configuration)
- [VS Code Copilot customization overview](https://code.visualstudio.com/docs/copilot/customization/overview)
## Debugging and Inspection
!!! info "Inspector and diagnostics"
- [MCP inspector repository](https://github.com/modelcontextprotocol/inspector)
- [MCP protocol repository issues](https://github.com/modelcontextprotocol/spec/issues)
- [Python logging configuration docs](https://docs.python.org/3/library/logging.config.html)
## Runtime and API Framework References
!!! info "Runtime references"
- [FastAPI documentation](https://fastapi.tiangolo.com/)
- [Uvicorn settings](https://www.uvicorn.org/settings/)
- [AnyIO documentation](https://anyio.readthedocs.io/en/stable/)
## Notes
- Use these links when tasks include IDE wiring, MCP server runtime setup, or production operations.
- Keep protocol and SDK references separate to avoid overloading implementation prompts.
@@ -0,0 +1,32 @@
# MCP Protocol and Specification
Use this page for authoritative links about MCP concepts, protocol shape, and official specification assets.
## Official Documentation
!!! info "MCP docs"
- [MCP introduction](https://modelcontextprotocol.io/docs/getting-started/intro)
- [Architecture overview](https://modelcontextprotocol.io/docs/learn/architecture)
- [Server concepts](https://modelcontextprotocol.io/docs/learn/server-concepts)
- [Client concepts](https://modelcontextprotocol.io/docs/learn/client-concepts)
- [Security overview](https://modelcontextprotocol.io/docs/learn/security-overview)
## Protocol and Schema Sources
!!! info "Specification repositories"
- [MCP specification repository](https://github.com/modelcontextprotocol/spec)
- [Specification schema directory](https://github.com/modelcontextprotocol/spec/tree/main/schema)
- [Specification issues and proposals](https://github.com/modelcontextprotocol/spec/issues)
## Core Capability References
!!! info "Capability details"
- [Resources concept docs](https://modelcontextprotocol.io/docs/learn/server-concepts#resources)
- [Tools concept docs](https://modelcontextprotocol.io/docs/learn/server-concepts#tools)
- [Prompt objects concept docs](https://modelcontextprotocol.io/docs/learn/server-concepts#prompts)
- [Sampling concept docs](https://modelcontextprotocol.io/docs/learn/client-concepts)
## Notes
- Prefer these links when the user asks about protocol correctness, transport semantics, capability naming, or compatibility.
- For implementation-level examples, use [sdk-and-fastmcp.md](./sdk-and-fastmcp.md).
@@ -0,0 +1,31 @@
# SDK and FastMCP
Use this page for implementation-oriented links across MCP SDKs and FastMCP.
## MCP SDKs
!!! info "SDK sources"
- [Python SDK repository](https://github.com/modelcontextprotocol/python-sdk)
- [TypeScript SDK repository](https://github.com/modelcontextprotocol/typescript-sdk)
- [Python SDK documentation](https://modelcontextprotocol.github.io/python-sdk/)
## FastMCP
!!! info "FastMCP sources"
- [FastMCP project documentation](https://gofastmcp.com/)
- [FastMCP GitHub repository](https://github.com/jlowin/fastmcp)
- [FastMCP examples directory](https://github.com/jlowin/fastmcp/tree/main/examples)
- [FastMCP PyPI package](https://pypi.org/project/fastmcp/)
## Server Implementation Patterns
!!! info "Implementation references"
- [MCP server concepts](https://modelcontextprotocol.io/docs/learn/server-concepts)
- [MCP architecture patterns](https://modelcontextprotocol.io/docs/learn/architecture)
- [Python packaging and resources](https://docs.python.org/3/library/importlib.resources.html)
## Notes
- Prefer official SDK repositories for API shape and compatibility checks.
- Use FastMCP references for rapid server scaffolding and implementation examples.
- For protocol-first questions, start from [mcp-protocol-and-spec.md](./mcp-protocol-and-spec.md).
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@@ -1,171 +0,0 @@
---
name: new-skill
description: Provide a practical checklist and baseline template for creating a new docs-first MCP skill in this repository.
argument-hint: What skill are you creating, and what problem should it solve?
x-personal-mcp:
id: new-skill
version: 1.0.0
tags:
- fastmcp
- bootstrap
- scaffolding
- skills
- mcp
capabilities:
- resource://skills/new-skill/document
depends_on: []
references: {}
---
# New Skill Bootstrap
Use this skill to bootstrap a new skill in the docs-first architecture. Try to use the `/create-skill` where possible to structure the output, but place it alongside the other skills in this repo.
## Inputs
1. New skill id (lowercase kebab-case)
2. One-sentence capability statement (what it does and when to use it)
3. Optional list of references to include under `references/`
## Source of Truth and Required References
1. Use this file as the baseline template for new skill authoring.
2. Read and follow these docs before implementing a new skill:
- [docs/architecture.md](../../architecture.md)
- [docs/content.md](../../content.md)
- [docs/frontmatter.md](../../frontmatter.md)
- [docs/mcp_layout.md](../../mcp_layout.md)
- [docs/uris.md](../../uris.md)
## Canonical Skill Shape
Create one skill directory under `docs/skills/`:
```text
docs/
skills/
<skill-id>/
SKILL.md
references/
... (optional markdown files, nested folders allowed)
```
Rules:
1. `SKILL.md` is required.
2. All skill-specific supporting docs live under `references/`.
3. Skill directories are ownership boundaries; no cross-skill writes.
4. `skill-id` is lowercase kebab-case and should remain stable.
### Framing
Phrasing and language in the skills should reflect the intent of providing preferences and reference documentation, rather than being for a migration or transition. When a particular resource is brought in, it should focus the general way something is done.
## SKILL.md Frontmatter Contract
`SKILL.md` frontmatter is authoritative for skill metadata.
Required top-level fields:
1. `name`
2. `description`
3. `x-personal-mcp`
Required `x-personal-mcp` fields:
1. `id`
2. `version`
3. `capabilities`
Optional `x-personal-mcp` fields:
1. `tags`
2. `depends_on`
3. `references`
Canonical frontmatter template:
```yaml
---
name: <skill-id>
description: <what this skill does and when to use it>
x-personal-mcp:
id: <skill-id>
version: 1.0.0
tags: []
capabilities:
- resource://skills/<skill-id>/document
depends_on: []
# Optional: only for nested references or metadata overrides.
references:
<ref-id>:
path: references/<file>.md
mime_type: text/markdown
title: <optional short title>
---
```
Reference manifest rules:
1. `ref-id` is lowercase kebab-case.
2. `path` is skill-relative and must stay under `references/`.
3. Top-level `references/*.md` files are auto-discovered, and `ref-id` is derived from a normalized filename stem.
4. Nested `references/**` markdown files must be declared explicitly.
5. Reference paths are markdown files.
## URI Surface
Canonical resource URIs for a skill:
1. `resource://skills/<skill_id>/document`
2. `resource://skills/<skill_id>/references/<ref_id>`
Canonical discovery URIs:
1. `resource://catalog/skills_index`
2. `resource://catalog/skills/{skill_id}`
Compatibility rule:
1. Keep URI families unversioned by default.
2. For breaking changes, update clients to the canonical replacement URIs directly.
## Scope
1. Create docs under docs/skills/<skill-id>/.
2. Define SKILL frontmatter with Anthropic and x-personal-mcp fields.
3. Treat top-level `references/*.md` as auto-discovered references with `ref-id` generated from filename.
4. Declare `x-personal-mcp.references` only when you need overrides or nested `references/**` entries.
5. Validate the docs build and MCP resource reads.
## Authoring Checklist
1. Create docs/skills/<skill-id>/SKILL.md.
2. Add docs/skills/<skill-id>/references/ files as needed.
3. Keep skill id and directory name aligned.
4. Keep frontmatter name equal to x-personal-mcp.id.
5. Include resource://skills/<skill-id>/document in capabilities.
6. For each top-level `references/<name>.md`, expect `resource://skills/<skill-id>/references/<name>` (normalized to lowercase kebab-case).
7. Add explicit `x-personal-mcp.references` entries only for nested paths or metadata overrides.
## Required Outcomes
1. Create `docs/skills/<skill-id>/SKILL.md` with valid frontmatter and a practical skill body.
2. Create and populate `docs/skills/<skill-id>/references/` with any needed markdown references.
3. Ensure frontmatter follows repository contract, including `x-personal-mcp` fields and canonical capabilities.
4. Keep URI and reference mapping consistent with repository conventions.
5. Reconcile all updates with repository implementation and avoid introducing parallel metadata systems.
## Validation
1. uv run zensical build
2. uv run pytest -q
## Output Contract
Return:
1. Files created or updated
2. Validation results
3. Follow-up suggestions for improving the skill
@@ -1,145 +0,0 @@
---
name: nicegui-ui-customization
description: 'Design and implement production NiceGUI UIs with reusable components, Tailwind-first styling, event-driven interactions, and troubleshooting for uploads, state, and static assets. Use when building or refactoring NiceGUI pages and interaction flows.'
argument-hint: 'What UI outcome should this workflow produce?'
x-personal-mcp:
id: nicegui-ui-customization
version: 1.0.0
tags:
- nicegui
- ui
- customization
- frontend
capabilities:
- resource://skills/nicegui-ui-customization/document
depends_on: []
---
# NiceGUI UI Customization Workflow
Create, style, and ship production NiceGUI UI flows with a repeatable process. The workflow keeps structure in Python, favors Tailwind and Quasar APIs for styling, and uses event-driven interaction patterns over ad-hoc polling.
## When To Use
- Building a new NiceGUI page or dashboard
- Refactoring a page into reusable components
- Adding file upload, form submission, live status, or background-job UX
- Troubleshooting race conditions, stale assets, or inconsistent state updates
## Target Outcome
Deliver a responsive, accessible UI flow that:
- keeps clear boundaries between page adapters, reusable components, and services
- uses Tailwind-first styling with minimal custom CSS
- updates UI through events and bindings
- has validation, user feedback, and failure handling
- passes a production-readiness check at the end
## Progressive Loading References
Load these references only when needed:
- Architecture and styling rules: [architecture and styling](./references/architecture-and-styling.md)
- Event and state interaction patterns: [interaction patterns](./references/interaction-patterns.md)
- Troubleshooting and release gates: [troubleshooting and quality gates](./references/troubleshooting-and-quality-gates.md)
## Procedure
### 1. Define the UI Slice
- Capture the user-visible outcome for this task in one sentence.
- Identify route-level page modules to touch.
- Identify service operations needed by the UI.
Completion check:
- You can name the target page, component candidates, and service calls before coding.
### 2. Choose Component Extraction Strategy
Decision point:
- If a layout pattern appears in 2 or more pages, extract it to `ui/components/`.
- If a pattern is page-specific, keep it in the page module.
Completion check:
- Reused UI patterns are encapsulated as callable components.
### 3. Build Responsive Layout First
- Use Tailwind utility classes for structure and spacing.
- Use responsive breakpoints (`sm:`, `md:`, `lg:`).
- Reserve `.style()` for dynamic values that cannot be expressed with classes.
Completion check:
- Layout works at mobile and desktop widths without custom CSS overrides.
### 4. Add Reactive State And Events
- Use bindable dataclasses for local page state.
- Prefer event handlers (`on_click`, `on_upload`, etc.) over periodic polling.
- Trigger explicit refreshes with `@ui.refreshable` where needed.
Decision point by interaction type:
- File upload: validate size/type, delegate storage to a service, notify success/failure.
- Form submit: bind inputs to dataclass fields, validate in service layer, clear state on success.
- Real-time status: use SSE or WebSocket for push updates.
- Long jobs: run in background task, update status endpoint or stream.
Completion check:
- Every user action has explicit positive and negative feedback via `ui.notify()`.
### 5. Apply Styling Strategy
Preferred order:
1. Tailwind utility classes
2. Quasar props
3. Reusable styled component functions
Only if absolutely necessary:
- Load minimal custom CSS once at startup in `bootstrap.py`.
- Keep custom CSS tokenized (variables) and documented.
Completion check:
- Styling is mostly class/props-driven and not dependent on scattered ad-hoc CSS.
### 6. Harden Against Common Failures
- Prevent duplicate submissions by disabling controls during in-flight operations.
- Avoid overlapping timers for the same state target.
- Serialize dependent updates (`await` service call before mutation/render).
- Verify static mount paths and cache behavior for changed assets.
Completion check:
- Race conditions and stale asset symptoms are addressed with explicit safeguards.
### 7. Final Production Readiness Review
Pass all checks:
- Structure: pages, components, services follow one-way dependency flow.
- Responsiveness: tested at small and large viewport widths.
- Accessibility: labels, button text, and action visibility are clear.
- Reliability: validation and exception paths produce user-facing notifications.
- Maintainability: repeated UI patterns are extracted; business logic stays in services.
If any check fails, return to the relevant step and iterate.
## Completion Contract
This workflow is complete when:
- the page flow meets the target outcome
- architecture boundaries are preserved
- chosen interaction pattern is implemented with explicit success and failure feedback
- troubleshooting checks pass
- production-readiness gate passes
@@ -1,77 +0,0 @@
# Architecture and Styling Reference
## Project Boundaries
Use this dependency direction:
- pages import components and services
- components contain presentation logic only
- services contain business logic and do not import UI
- static assets are mounted and loaded once at bootstrap
Suggested module split:
```text
src/app/
ui/pages/
ui/components/
ui/static/
services/
api/
bootstrap.py
```
## Component Extraction Rules
Extract to ui/components when a pattern appears in two or more pages.
Keep in-page if the layout is specific to a single route.
```python
def card_section(title: str, content: str) -> ui.card:
with ui.card().classes("w-full max-w-md") as card:
ui.label(title).classes("text-lg font-bold")
ui.label(content).classes("text-gray-600")
return card
```
## Tailwind-First Layout Pattern
Use Tailwind utility classes for structure and spacing.
Use breakpoint classes for responsive behavior.
Use .style() only for values that must be computed dynamically.
```python
with ui.column().classes("w-full"):
with ui.row().classes("w-full gap-4 flex-wrap sm:flex-nowrap"):
ui.card().classes("flex-1 min-w-64")
ui.card().classes("flex-1 min-w-64")
```
## Styling Decision Order
1. Tailwind utility classes
2. Quasar props
3. Reusable styled component functions
4. Minimal custom CSS loaded once at bootstrap (only when needed)
```python
from fastapi.staticfiles import StaticFiles
app.mount("/static", StaticFiles(directory="src/app/static"), name="static")
ui.add_css(open("src/app/static/css/base.css").read())
```
## Static Asset Rules
- Keep custom CSS small and tokenized with variables.
- Avoid per-page CSS injection.
- Verify static mount paths and reverse proxy rewrites.
## Links
!!! info "Primary sources"
- [NiceGUI elements](https://nicegui.io/documentation/element)
- [NiceGUI binding properties](https://nicegui.io/documentation/section_binding_properties)
- [Tailwind utility-first styling](https://tailwindcss.com/docs/utility-first)
- [Quasar components](https://quasar.dev/vue-components)
+93 -163
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@@ -1,207 +1,137 @@
---
name: nicegui
description: 'Design and scaffold a production-ready NiceGUI + FastAPI application architecture. Use for multi-page app planning, package boundaries, optional DB/LangGraph/docs integration, and implementation checklists.'
argument-hint: 'What should this app include (pages, DB, AI, docs, constraints)?'
x-personal-mcp:
id: nicegui
version: 1.0.0
tags:
- nicegui
- fastapi
- ui
- architecture
capabilities:
- resource://skills/nicegui/document
depends_on: []
description: 'Reference hub for NiceGUI and FastAPI application structure, typed configuration, ASGI and Uvicorn startup, UI composition, styling, bindable state, interactions, troubleshooting, testing, and source documentation. Use when planning, implementing, reviewing, deploying, or debugging NiceGUI applications; load only the references relevant to the task.'
---
# NiceGUI
# NiceGUI Reference
Design a production-minded NiceGUI + FastAPI architecture with clear boundaries, optional extensions, and a concrete implementation checklist.
Use this skill as a progressive reference for NiceGUI applications built with FastAPI. Start with the routing map, load only the material needed for the current question, and reconcile it with the target project's NiceGUI version and established conventions.
## When to Use
- You need a reusable architecture plan before implementing a NiceGUI app.
- You want FastAPI app-factory structure and lifespan wiring.
- You need optional guidance for database, LangGraph workflows, or mounted static docs.
- You want output that is concise, structured, and implementation-ready.
- Planning or reviewing NiceGUI application structure and FastAPI composition.
- Building or refactoring pages, components, layouts, and static assets.
- Modeling UI state with bindings or bindable dataclasses.
- Implementing forms, uploads, refreshes, live updates, or background work.
- Diagnosing UI state, concurrency, navigation, or asset problems.
- Verifying framework behavior against primary documentation.
## Inputs to Collect
## How to Use This Skill
Collect these inputs up front. If not provided, make safe defaults and state assumptions.
1. Classify the request using the discovery map below.
2. Load the smallest relevant reference, or at most two references for a mixed concern.
3. Inspect the target repository before applying guidance; preserve its sound local patterns.
4. Check the pinned NiceGUI and integration versions before relying on version-specific APIs.
5. Validate the changed behavior with focused tests and, for UI work, relevant viewport checks.
- Product scope and primary user journeys.
- Required pages and route map.
- Whether persistent data is required.
- Whether AI orchestration (multi-step, streaming, approvals) is required.
- Whether generated docs should be mounted in-app.
- Runtime/deployment constraints (single service vs split services, environment requirements).
## Progressive Discovery Map
## Outcome
### Application Architecture
Produce:
Load [application architecture](./references/architecture.md) for:
- A concise architecture explanation.
- How core services, UI pages, and UI components fit together.
- Explicit decision on DB ownership or involvement.
- Explicit decision on AI workflow (or no AI).
- A checklist implementation plan organized by package and domain.
- FastAPI app factories and lifespan ownership
- package boundaries and dependency direction
- page registration and health routes
- optional persistence, LangGraph, or mounted documentation
- async responsiveness and baseline tests
## Procedure
### FastAPI And Uvicorn Startup
1. Frame the baseline architecture.
2. Choose optional extensions (DB, AI, docs) using decision points below.
3. Map modules, dependencies, and key boundaries.
4. Define async behavior and UI responsiveness expectations.
5. Define key functions/classes and configuration surfaces.
6. Produce phased checklist with rollout or migration notes when relevant.
7. Run completion checks before returning.
Load [FastAPI and Uvicorn startup](./references/fastapi-uvicorn-startup.md) for:
### 1) Baseline architecture
- choosing between `ui.run()` and `ui.run_with()`
- understanding the parent FastAPI app and NiceGUI's internal app
- composing ASGI lifespan and mounted routes
- loading one typed settings snapshot for server and application configuration
- serving an app instance or factory with Uvicorn
- exposing programmatic startup through `[project.scripts]`
- reload, worker, and process-local state constraints
Use a src-layout with FastAPI as the ASGI app and NiceGUI registered via composition.
### Components And Styling
- App factory pattern: `create_app()`.
- Lifespan for startup and shutdown resource management.
- `api/` for HTTP handlers, `services/` for business logic.
- `ui/pages/` for page modules, `ui/components/` for shared UI.
- Health endpoint on FastAPI side: `/healthz`.
Load [architecture and styling](./references/architecture-and-styling.md) for:
Recommended base shape:
- page, component, and service boundaries
- component extraction decisions
- Quasar props, Tailwind utilities, and custom CSS boundaries
- responsive layout and static asset conventions
- Tailwind and Quasar breakpoint scales, container queries, and responsive testing
- uniformly scaling dialogs on mobile
- preserving Quasar field proportions
- keeping detached `QSelect` menus anchored
- sizing scrollable dialog cards under CSS `zoom`
- validating zoomed controls with Playwright or a browser
```text
.
├─ pyproject.toml
├─ .env.example
├─ README.md
├─ src/
│ └─ app/
│ ├─ __init__.py
│ ├─ main.py
│ ├─ bootstrap.py
│ ├─ config.py
│ ├─ logging.py
│ ├─ api/
│ │ ├─ __init__.py
│ │ └─ health.py
│ ├─ services/
│ │ ├─ __init__.py
│ │ └─ example_service.py
│ └─ ui/
│ ├─ __init__.py
│ ├─ components/
│ │ ├─ __init__.py
│ │ └─ nav.py
│ └─ pages/
│ ├─ __init__.py
│ ├─ home.py
│ ├─ dashboard.py
│ └─ about.py
└─ tests/
├─ test_health.py
└─ test_pages_registration.py
```
### Bindable State
### 2) Decision points
Load [bindable dataclasses](./references/binding-dataclasses.md) for:
#### Database needed?
- typed local UI state
- propagation and refresh behavior
- nested structures and strict bindings
- mutable defaults, performance, and version notes
- If no: keep `services/` pure and skip persistence layers.
- If yes: add `db/` package with engine/session/model/repository layering.
- Prefer one process-level engine and request-scoped sessions via `yield`.
- Prefer Alembic migrations for schema changes.
### Interaction Patterns
#### AI workflow needed?
Load [interaction patterns](./references/interaction-patterns.md) for:
- If no: keep `services/` focused on app logic only.
- If yes: add `ai/` package (state, nodes, graph, runtime, contracts).
- Keep graph internals out of `ui/pages/` and API handlers.
- Use stable thread/session IDs for resumable flows.
- uploads and form submission
- explicit refreshes
- server-sent events and WebSockets
- background work and duplicate-submission guards
#### Mounted docs needed?
### Troubleshooting And Quality
- If no: skip docs mounting.
- If yes: mount generated static site under configurable route (default `/docs`).
- Keep docs mounting in composition layer, not page modules.
Load [troubleshooting and quality gates](./references/troubleshooting-and-quality-gates.md) for:
### 3) Page and component registration
- upload failures and UI race conditions
- stale assets and navigation drift
- responsiveness, accessibility, reliability, and maintainability checks
- Require at minimum page modules for `/`, `/dashboard`, `/about`.
- Prefer explicit registration pattern:
- `ui/pages/__init__.py` exports `register_pages()`.
- Each page module exports `register_page()`.
- Shared shell components (header/nav/drawer) live in `ui/components/`.
### Primary Sources
### 4) Dependency direction rules
Load [source documentation](./references/source-documentation.md) when:
Prefer:
- behavior is version-sensitive or uncertain
- an integration recommendation needs verification
- upstream NiceGUI, FastAPI, Tailwind, Quasar, SQLAlchemy, Pydantic, or LangGraph documentation is required
- `main/bootstrap` -> `config/logging` + `api` + `ui/pages` + `services`
- `api` -> `services`
- `ui/pages` -> `ui/components` + `services`
- `services` -> helpers/clients (and `db/` when enabled)
## Common Discovery Paths
Avoid reverse imports from services into API or UI modules.
### New Application Or Architecture Review
### 5) Async and UI responsiveness rules
1. Load [application architecture](./references/architecture.md).
2. Add [FastAPI and Uvicorn startup](./references/fastapi-uvicorn-startup.md) when FastAPI owns the application or startup must be exposed as a project command.
3. Add [architecture and styling](./references/architecture-and-styling.md) only when page and component design is in scope.
- Prefer `async def` for page handlers, service methods, and integrations when the call path includes I/O.
- Use non-blocking clients/libraries where possible so long-running I/O does not freeze UI updates.
- Do not run blocking calls (`time.sleep`, blocking HTTP/database clients) in UI event handlers.
- For heavy CPU work, offload to worker/background execution and keep the UI loop free.
- Show progress states for long actions (disable action button, show spinner/progress text, re-enable on completion).
- Stream or chunk incremental results to the UI when workflows are multi-step or long-running.
- Keep cancellation and timeout behavior explicit for user-triggered long tasks.
- Ensure exceptions from async tasks are surfaced with user-friendly feedback and logged for diagnostics.
### Page Or Component Work
### 6) Testing minimums
1. Load [architecture and styling](./references/architecture-and-styling.md).
2. Add [interaction patterns](./references/interaction-patterns.md) or [bindable dataclasses](./references/binding-dataclasses.md) according to the page behavior.
- Test FastAPI health route behavior.
- Test page registration wiring.
- If DB enabled: session lifecycle and rollback behavior tests.
- If AI enabled: graph happy path and interrupt/resume coverage.
- If docs enabled: mounted docs route returns index page.
- For async flows: test long-running actions preserve UI responsiveness (loading state, completion state, and error state).
### Debugging Or Production Review
### 7) Styling architecture
1. Start with [troubleshooting and quality gates](./references/troubleshooting-and-quality-gates.md).
2. Follow the symptom to one detailed reference.
3. Confirm uncertain behavior in [source documentation](./references/source-documentation.md).
- Keep structure and layout in Python modules using NiceGUI class composition.
- Keep visual polish in shared CSS files, loaded once at startup.
- Prefer semantic reusable classes over ad hoc per-page styling.
## General Defaults
## Completion Checks
- Keep composition, transport, services, pages, and components directionally separated.
- Keep business logic out of UI components and event handlers.
- Avoid blocking I/O and CPU-heavy work in the UI event loop.
- Prefer event-driven updates and explicit refreshes over unrelated polling.
- Prefer Tailwind utilities, then Quasar props, then reusable component helpers; use minimal shared CSS when those are insufficient.
- Provide loading, success, and failure states for user-triggered work.
- Treat version-specific guidance as a prompt to verify the project's dependency version.
- Uses app factory and FastAPI lifespan.
- Pages are modularized (not single-file UI).
- Health endpoint exists on FastAPI side.
- Dependency direction is clean and one-way.
- Async-first guidance is applied where I/O exists, with explicit non-blocking UX states.
- Optional DB/AI/docs decisions are explicit and reflected in structure.
- Output includes architecture summary and package-organized checklist.
## Reference Use Contract
## Output Contract
When applying this skill:
Return:
- Concise high-level architecture.
- How core services, pages, and shared components fit.
- DB involvement and ownership stance.
- AI workflow stance and runtime flow.
- Checklist plan by package and domain:
- key functions/classes
- settings/config surfaces
- rollout/migration notes (when relevant)
## Guardrails
- Do not collapse all pages into one file.
- Do not use globals or implicit global side effects.
- Do not block UI event handlers with synchronous I/O or long CPU tasks.
- Always define loading/progress/error states for long user-triggered actions.
- Keep code minimal but production-minded.
- Prefer clarity and maintainability over clever abstractions.
## References
- Architecture and integration details: [NiceGUI architecture reference](./references/architecture.md)
- Source documentation links: [NiceGUI source documentation](./references/source-documentation.md)
- return only guidance relevant to the current task
- distinguish repository facts from reference recommendations
- cite the appropriate source reference for framework-level claims
- state assumptions when application requirements are missing
- report the focused checks used to validate implementation changes
@@ -0,0 +1,289 @@
# NiceGUI Page Layout And Styling
Use this reference to structure NiceGUI pages, choose component boundaries, apply responsive layout, and introduce custom CSS without fighting Quasar's internal geometry.
## Ownership And Dependency Boundaries
Keep dependencies flowing in one direction:
- pages import components and services
- components contain presentation logic only
- services contain business logic and do not import UI
- bootstrap code mounts static assets and loads shared CSS once
Suggested module split:
```text
src/my_app/
ui/
pages/
components/
static/
services/
api/
```
Page modules should compose a route from reusable presentation and service calls. They should not own domain rules, persistence, or long-running synchronous work.
## Page Composition
Build the outer layout before styling individual controls:
1. Define the page shell and width constraints.
2. Establish responsive rows, columns, gaps, and wrapping.
3. Add semantic sections and repeated components.
4. Configure Quasar component appearance with props.
5. Add custom CSS only for behavior that props and utilities cannot express safely.
```python
with ui.column().classes("w-full max-w-6xl mx-auto gap-6 px-4"):
page_header(title="Inventory")
with ui.row().classes("w-full gap-4 flex-wrap lg:flex-nowrap items-start"):
filters_panel().classes("w-full lg:w-72 shrink-0")
item_grid().classes("w-full flex-1 min-w-0")
```
Use stable width, minimum-width, and flex constraints so labels, icons, validation messages, and loaded content do not shift the surrounding layout.
## Component Extraction
Extract a presentation pattern to `ui/components/` when it appears on two or more pages or when it owns a meaningful interaction boundary. Keep one-off route layout in the page module.
```python
def card_section(title: str, content: str) -> ui.card:
with ui.card().classes("w-full max-w-md") as card:
ui.label(title).classes("text-lg font-bold")
ui.label(content).classes("text-gray-600")
return card
```
Reusable components should accept data and event callbacks rather than import page state or business services implicitly.
## Styling Decision Order
NiceGUI wraps Quasar components. Choose the styling mechanism according to what it owns:
1. Use Quasar props for component appearance, density, labels, and popup behavior.
2. Use NiceGUI `.classes()` and Tailwind utilities for width, spacing, alignment, and responsive layout.
3. Use reusable component functions for repeated visual patterns.
4. Use `.style()` for genuinely dynamic inline values.
5. Use minimal shared CSS only when props and utilities are insufficient.
Common Quasar props include:
- `outlined`
- `dense`
- `stack-label`
- `popup-content-class`
- `input-class`
- `input-style`
Avoid overriding internal selectors such as:
- `.q-field__label`
- `.q-field__native`
- `.q-field__control`
- `.q-field__input`
Quasar coordinates field height, padding, labels, values, icons, and floating-label transforms. Changing only one internal part tends to cause clipping or overlap.
## Responsive Layout
Support these layouts only:
- mobile: a single-column layout with wrapping toolbars and full-width controls
- landscape desktop: $1920 \times 1080$ with side-by-side panels where they improve scanning
- portrait desktop: $1080 \times 1920 with stacked panels or a narrow fixed sidebar
Build the mobile layout first, then add one desktop breakpoint when a row or grid needs more space. Prefer flex wrapping and fluid grids before adding another breakpoint. Use Tailwind classes for page layout and Quasar props for component behavior.
```python
with ui.row().classes('w-full flex-wrap gap-4 lg:flex-nowrap items-start'):
filters_panel().classes('w-full lg:w-72 shrink-0')
item_grid().classes('w-full flex-1 min-w-0')
```
Use `min-w-0` for flexible children, `flex-wrap` for toolbars, and `max-w-* mx-auto` to keep portrait layouts readable. Do not add device-specific component trees, container queries, or custom breakpoints unless a supported layout demonstrates a concrete failure.
## Static Assets And Shared CSS
- Mount static assets from the composition layer.
- Load shared CSS once rather than injecting it from individual pages.
- Keep custom CSS tokenized with variables and scoped to application classes.
- Avoid broad rules against Quasar internals.
- Verify mount paths, reverse-proxy rewrites, and cache behavior.
```python
from pathlib import Path
from fastapi.staticfiles import StaticFiles
STATIC_DIR = Path(__file__).parent / "ui" / "static"
app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
ui.add_css((STATIC_DIR / "css" / "base.css").read_text(encoding="utf-8"))
```
## Responsive Dialog Pattern
Use whole-card scaling when a form dialog must become uniformly larger on mobile while preserving Quasar's internal proportions. Keep detached select menus unscaled and make the card itself scrollable.
### Use Normal Field Density
Normal Quasar fields are approximately `56px` high, while dense fields are approximately `40px` high. Remove `dense` when larger controls are needed.
```python
ui.input("Name").props("outlined")
ui.number("Quantity").props("outlined")
ui.select(...).props(
"outlined popup-content-class=app-item-detail-menu"
)
ui.textarea("Description").props("outlined autogrow")
```
Add a scoped class to the dialog card:
```python
ui.card().classes("app-detail-card app-item-detail-card")
```
### Scale The Complete Card
```css
:root {
--item-dialog-scale: 1;
--item-dialog-max-height: calc(100dvh - 3rem);
}
.app-item-detail-card {
width: min(50rem, 50vw);
max-height: var(--item-dialog-max-height);
overflow-y: auto;
overscroll-behavior: contain;
zoom: var(--item-dialog-scale);
}
/* Restore Quasar's baseline if a global rule overrides it. */
.app-item-detail-card .q-field,
.app-item-detail-menu {
font-size: 14px;
}
@media (max-width: 599px) {
:root {
--item-dialog-scale: 1.2;
/* 75dvh becomes 90dvh after 1.2x zoom. */
--item-dialog-max-height: 75dvh;
}
.app-item-detail-card {
width: 80vw;
}
.app-item-detail-menu {
font-size: 16.8px;
}
}
```
The main mobile tuning knob is:
```css
--item-dialog-scale: 1.2;
```
### Keep Detached Popups Unscaled
Do not apply `zoom` or `transform: scale()` to a `QSelect` popup menu. Quasar renders menus outside the dialog and positions them from the unscaled anchor geometry. Scaling the menu container afterward separates it from its field.
Avoid:
```css
.app-item-detail-card,
.app-item-detail-menu {
zoom: 1.2;
}
```
Use:
```css
.app-item-detail-card {
zoom: 1.2;
}
.app-item-detail-menu {
font-size: 16.8px;
}
```
Use `popup-content-class=app-item-detail-menu` to target the detached menu and enlarge its text without changing its coordinate system.
### Account For Zoom When Scrolling
The card's pre-zoom maximum height must account for the scale:
\[
\begin{aligned}
h_{\mathrm{pre}} &= \frac{h_{\mathrm{visible}}}{s} \\
\text{where } s &= \text{the zoom scale}
\end{aligned}
\]
For a desired visual height of `90dvh` at \(1.2\times\):
\[
\frac{90\,\mathrm{dvh}}{1.2} = 75\,\mathrm{dvh}
\]
Therefore:
```css
--item-dialog-max-height: 75dvh;
```
Apply scrolling to the card itself:
```css
.app-item-detail-card {
max-height: var(--item-dialog-max-height);
overflow-y: auto;
overscroll-behavior: contain;
}
```
This keeps the dimmed page stationary while the form scrolls.
### Match The Quasar Breakpoint
Quasar's extra-small breakpoint ends at `599.98px`. A mobile-only rule can use:
```css
@media (max-width: 599px) {
/* Mobile rules. */
}
```
Confirm custom breakpoint values against the target application's Quasar configuration.
## Validation Checklist
Check each completed page at these three viewports:
1. A representative mobile viewport, such as $390 \times 844$.
2. Landscape desktop at $1920 \times 1080$.
3. Portrait desktop at $1080 \times 1920$.
Confirm that page sections do not overlap, toolbars wrap on mobile, desktop panels use the available space without becoming excessively wide, and dialogs remain visible and scroll to their final field.
## Sources
!!! info "Primary sources"
- [NiceGUI element styling and props](https://nicegui.io/documentation/element)
- [NiceGUI binding properties](https://nicegui.io/documentation/section_binding_properties)
- [Quasar components](https://quasar.dev/vue-components)
- [Quasar field](https://quasar.dev/vue-components/field/)
- [Quasar select](https://quasar.dev/vue-components/select/)
- [Tailwind responsive design](https://tailwindcss.com/docs/responsive-design)
- [MDN `zoom`](https://developer.mozilla.org/en-US/docs/Web/CSS/zoom)
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# NiceGUI Architecture Reference
# NiceGUI Application Architecture
This reference expands the workflow in the main skill file and is loaded only when needed.
Load this reference for application composition, package boundaries, and optional subsystem decisions.
## Baseline package boundaries
## Baseline Package Boundaries
- `main.py`: process entrypoint only.
- `bootstrap.py`: app composition, router wiring, page registration, lifespan orchestration.
- `config.py`: typed settings and env parsing.
- `main.py`: process entry point and app factory exposure.
- `bootstrap.py`: app composition, router wiring, page registration, and lifespan orchestration.
- `config.py`: typed settings and environment parsing.
- `logging.py`: centralized logging setup.
- `api/`: HTTP transport layer; delegates to services.
- `services/`: business/use-case logic.
- `api/`: HTTP transport that delegates to services.
- `services/`: business and use-case logic.
- `ui/pages/`: route-level NiceGUI pages.
- `ui/components/`: shared UI building blocks.
- `ui/components/`: shared presentation building blocks.
## Required baseline behavior
Recommended base shape:
```text
.
├─ pyproject.toml
├─ .env.example
├─ src/
│ └─ app/
│ ├─ __init__.py
│ ├─ main.py
│ ├─ bootstrap.py
│ ├─ config.py
│ ├─ logging.py
│ ├─ api/
│ │ ├─ __init__.py
│ │ └─ health.py
│ ├─ services/
│ │ ├─ __init__.py
│ │ └─ example_service.py
│ └─ ui/
│ ├─ __init__.py
│ ├─ components/
│ │ ├─ __init__.py
│ │ └─ nav.py
│ └─ pages/
│ ├─ __init__.py
│ ├─ home.py
│ ├─ dashboard.py
│ └─ about.py
└─ tests/
├─ test_health.py
└─ test_pages_registration.py
```
## Required Baseline Behavior
- FastAPI is the base ASGI app.
- NiceGUI pages are modular and registered from page modules.
- Minimum pages: `/`, `/dashboard`, `/about`.
- FastAPI health route: `/healthz`.
- Lifespan handles startup/shutdown resources.
- No global side effects at import time.
- `create_app()` composes routes, resources, and NiceGUI.
- Lifespan owns startup and shutdown resources.
- NiceGUI pages are modular and explicitly registered.
- FastAPI exposes a health route such as `/healthz`.
- Imports do not trigger runtime global side effects.
## Optional extension: Database
For the ownership relationship between a caller-created FastAPI app, `nicegui.app`, `ui.run_with()`, Uvicorn, and a packaged startup command, load [FastAPI and Uvicorn startup](./fastapi-uvicorn-startup.md).
Use only if persistence is required.
## Dependency Direction
Suggested additions:
Prefer:
- `main/bootstrap` -> `config/logging` + `api` + `ui/pages` + `services`
- `api` -> `services`
- `ui/pages` -> `ui/components` + `services`
- `services` -> helpers, clients, and `db/` when enabled
Avoid imports from services back into API or UI modules.
## Optional Persistence
Use only when the product requires durable data.
```text
src/app/db/
@@ -37,19 +82,15 @@ src/app/db/
└─ repositories/
```
Guidelines:
- Create one engine and sessionmaker per process.
- Provide request- or operation-scoped sessions with `yield`.
- Keep transaction boundaries explicit in service or repository flows.
- Never share sessions across concurrent tasks.
- Use Alembic as the schema migration source of truth.
- One engine and one sessionmaker per process.
- Request-scoped session dependency using `yield`.
- Explicit transaction boundaries in service/repository flows.
- Avoid shared sessions across concurrent tasks.
- Use Alembic as schema source of truth.
## Optional LangGraph AI
## Optional extension: LangGraph AI
Use only for multi-step AI orchestration or human-in-the-loop workflows.
Suggested additions:
Use only for multi-step orchestration, resumable work, streaming, or human approval.
```text
src/app/ai/
@@ -60,33 +101,37 @@ src/app/ai/
└─ contracts.py
```
Guidelines:
- Keep graph internals outside API/UI modules.
- Invoke graph through `services/ai_service.py`.
- Use stable thread/session IDs for resumable sessions.
- Keep graph internals outside API and UI modules.
- Invoke graphs through a service such as `services/ai_service.py`.
- Use stable thread or session IDs for resumable flows.
- Keep interrupt payloads JSON-serializable.
## Optional extension: Mounted static docs
## Optional Mounted Docs
Use only when generated docs should be served in-app.
Use only when generated docs must be served by the application.
Suggested settings:
- `docs_enabled`
- `docs_mount_path`
- `docs_site_dir`
- `docs_require_build` (optional)
- `docs_require_build`
Guidelines:
Mount docs in the composition layer, normalize the mount path, avoid route conflicts, and define behavior for missing build artifacts.
- Mount docs in composition layer (`bootstrap.py`).
- Normalize mount path and avoid route conflicts.
- Warn on missing build artifacts unless strict mode is enabled.
## Async And Responsiveness
## Suggested output quality criteria
- Use `async def` where a handler or service path performs I/O.
- Prefer non-blocking clients and libraries.
- Offload CPU-heavy work to worker or background execution.
- Define progress, cancellation, timeout, completion, and error states for long actions.
- Stream or chunk results when workflows are long-running or multi-step.
- Clear architecture summary with assumptions.
- Explicit decisions for DB, AI, and docs.
- Package-scoped implementation checklist.
- Minimal test plan aligned to enabled features.
## Testing Minimums
- Test the FastAPI health route.
- Test page registration wiring.
- If persistence is enabled, test session lifecycle and rollback behavior.
- If AI is enabled, test happy paths and interrupt/resume behavior.
- If docs are enabled, test the mounted index route.
- For long actions, test loading, completion, and error states.
@@ -0,0 +1,100 @@
# Binding Dataclasses Deep Dive
Use this reference to model NiceGUI state with bindable dataclasses and avoid common propagation and performance pitfalls.
## Primary Sources
- NiceGUI binding docs: [binding properties](https://www.nicegui.io/documentation/section_binding_properties)
- Python dataclass docs: [dataclasses module](https://docs.python.org/3/library/dataclasses.html)
- Data class design rationale: [PEP 557](https://peps.python.org/pep-0557/)
## Bindable Dataclass Behavior
`@binding.bindable_dataclass` extends standard dataclasses by turning fields into bindable properties, allowing UI bindings to propagate when a field is assigned.
```python
from nicegui import binding, ui
@binding.bindable_dataclass
class Profile:
name: str = "Ada"
age: int = 37
profile = Profile()
ui.input("Name").bind_value(profile, "name")
ui.number("Age", min=0).bind_value(profile, "age")
ui.label().bind_text_from(profile, "name", backward=lambda name: f"User: {name}")
```
## Propagation And Performance
NiceGUI distinguishes between two link types:
- Bindable properties propagate efficiently when values are assigned.
- Active links are checked in a refresh loop.
Prefer bindable dataclasses for frequently updated form state. Keep binding transforms pure and inexpensive. If an application has many active links, tune `binding_refresh_interval` in `ui.run(...)` only after measuring the impact.
## Dataclass Modeling Rules
- Use `field(default_factory=...)` for mutable defaults.
- Avoid `frozen=True` for models edited by UI controls.
- Use `slots=True` only after confirming compatibility with inheritance and extension needs.
- Keep UI-editable fields explicit and typed.
```python
from dataclasses import field
from nicegui import binding
@binding.bindable_dataclass
class Filters:
query: str = ""
tags: list[str] = field(default_factory=list)
```
## Nested Structures
NiceGUI supports tuple paths for nested data structures.
```python
from nicegui import ui
data = {"user": {"name": "Ada"}}
ui.input("Name").bind_value(data, ("user", "name"))
ui.label().bind_text_from(data, ("user", "name"))
```
Keep nested dataclass updates explicit and predictable at the field level.
## Strictness And Refactor Safety
- Object attributes are checked by default.
- Dictionary keys are not checked by default.
- Use `strict=True` when missing dictionary keys should produce warnings.
```python
from nicegui import app, ui
ui.input().bind_value(app.storage.user, "display_name", strict=True)
```
## Common Pitfalls
- In-place mutation may not produce immediate UI synchronization. Assign the updated value back to the bound field.
- Heavy binding transforms can degrade refresh performance. Move expensive work to event handlers or services.
- State shared across unrelated pages or users can leak data. Scope models to the appropriate page, client, or user context.
## Version Checks
- `bindable_dataclass` was added in NiceGUI 2.11.0.
- Depth-first binding propagation was documented in NiceGUI 2.16.0.
- Binding `strict` behavior was documented in NiceGUI 3.0.0.
- Tuple paths for nested properties were documented in NiceGUI 3.10.0.
Verify these behaviors against the NiceGUI version pinned by the target project.
@@ -0,0 +1,315 @@
# FastAPI And Uvicorn Startup
Use this reference when FastAPI owns the application and NiceGUI is one part of it. The central distinction is between **composing an ASGI application** and **starting an ASGI server**:
- [`ui.run_with()`](https://github.com/zauberzeug/nicegui/blob/main/nicegui/ui_run_with.py) composes NiceGUI with a caller-owned FastAPI application. It does not start Uvicorn.
- [`uvicorn.run()`](https://www.uvicorn.org/#running-programmatically) starts the server and tells it which ASGI application to serve.
## Ownership Model
```mermaid
flowchart TD
E["Project script: my-app"] --> M["main()"]
M --> S["get_settings()"]
M --> U["uvicorn.run()"]
U --> F["create_app()"]
F --> S
F --> P["Parent FastAPI app"]
P --> A["API routes and middleware"]
P -->|"mount_path=/gui"| N["NiceGUI App"]
U -->|"ASGI requests and lifespan"| P
```
The objects have separate responsibilities:
| Object | Owner | Responsibility |
| --- | --- | --- |
| Parent `FastAPI` instance | Application code | Root ASGI app, API routes, middleware, lifespan, and mounted applications |
| `Settings` instance | Application code | Immutable, process-local configuration snapshot shared by startup and composition |
| `nicegui.app` | NiceGUI | A process-local [`App`](https://github.com/zauberzeug/nicegui/blob/main/nicegui/app/app.py) instance that subclasses `FastAPI` |
| `ui.run_with(parent_app)` | NiceGUI integration | Configures NiceGUI, mounts `nicegui.app` into `parent_app`, and integrates lifecycle handling |
| Uvicorn | Server process | Imports or receives the root ASGI app, opens sockets, drives lifespan, and serves requests |
Uvicorn must serve the **parent FastAPI app** when using `ui.run_with()`. Passing `nicegui.app` to `ui.run_with()` is rejected because it would mount NiceGUI into itself and recurse on unmatched routes.
## Choose One Startup Mode
### Let NiceGUI Own Startup
Use `ui.run()` when NiceGUI is the main application. Add ordinary FastAPI routes to the exported `nicegui.app` object:
```python
from nicegui import app, ui
@app.get('/healthz')
def health() -> dict[str, str]:
return {'status': 'ok'}
@ui.page('/')
def home() -> None:
ui.label('Home')
ui.run()
```
In this mode, NiceGUI configures and starts its own [Uvicorn-derived server](https://github.com/zauberzeug/nicegui/blob/main/nicegui/server.py). Do not also call `uvicorn.run()`.
### Let FastAPI Own The Application
Use `ui.run_with()` when an existing FastAPI application owns middleware, API routers, OpenAPI configuration, lifespan resources, or deployment startup. The [official NiceGUI FastAPI example](https://github.com/zauberzeug/nicegui/blob/main/examples/fastapi/main.py) follows this model.
`mount_path` controls where the NiceGUI application appears externally. A NiceGUI page declared as `/` is reachable at `/gui/` when mounted at `/gui`, while parent routes such as `/healthz` remain at the root. A dedicated UI prefix usually makes ownership and route conflicts clearer than mounting both applications at `/`.
## Canonical Factory Layout
Keep application composition importable and server startup explicit:
```text
.
├─ pyproject.toml
└─ src/
└─ my_app/
├─ __init__.py
├─ config.py
└─ main.py
```
```python title="src/my_app/config.py"
from functools import cache
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field, SecretStr
from pydantic_settings import BaseSettings, SettingsConfigDict
class ServerSettings(BaseModel):
model_config = ConfigDict(frozen=True)
host: str = '0.0.0.0'
port: int = 8000
log_level: Literal['critical', 'error', 'warning', 'info', 'debug', 'trace'] = (
'info'
)
reload: bool = False
class GuiSettings(BaseModel):
model_config = ConfigDict(frozen=True)
mount_path: str = '/gui'
storage_secret: SecretStr | None = None
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_prefix='MY_APP_',
env_nested_delimiter='__',
env_file='.env',
env_file_encoding='utf-8',
frozen=True,
)
server: ServerSettings = Field(default_factory=ServerSettings)
gui: GuiSettings = Field(default_factory=GuiSettings)
@cache
def get_settings() -> Settings:
return Settings()
```
`ServerSettings` and `GuiSettings` inherit from `BaseModel` because they share one application owner, source policy, and process lifecycle. The root `BaseSettings` reads the sources once and validates one atomic snapshot. Environment variables use names such as `MY_APP_SERVER__PORT`, `MY_APP_SERVER__RELOAD`, `MY_APP_GUI__MOUNT_PATH`, and `MY_APP_GUI__STORAGE_SECRET`.
The argument-free [`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache) provider is appropriate here because both the project entry point and Uvicorn's zero-argument factory need process-lifetime access. Each reload or worker process gets its own settings instance. Do not add override arguments to `get_settings()`; inject a `Settings` instance directly into `create_app()` in tests or alternate composition roots. See the [Pydantic settings implementation guide](../../pydantic-settings/SKILL.md) for source precedence, independent settings boundaries, cache clearing, and runtime reload guidance.
```python title="src/my_app/main.py"
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
import uvicorn
from fastapi import FastAPI
from nicegui import ui
from my_app.config import Settings, get_settings
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None]:
app.state.ready = True
try:
yield
finally:
app.state.ready = False
def register_pages() -> None:
@ui.page('/')
def dashboard() -> None:
ui.label('Dashboard')
def create_app(settings: Settings | None = None) -> FastAPI:
settings = settings or get_settings()
app = FastAPI(lifespan=lifespan)
app.state.settings = settings
@app.get('/healthz')
def health() -> dict[str, str]:
return {'status': 'ok'}
register_pages()
ui.run_with(
app,
mount_path=settings.gui.mount_path,
storage_secret=(
settings.gui.storage_secret.get_secret_value()
if settings.gui.storage_secret is not None
else None
),
)
return app
def main() -> None:
settings = get_settings()
uvicorn.run(
'my_app.main:create_app',
factory=True,
host=settings.server.host,
port=settings.server.port,
log_level=settings.server.log_level,
reload=settings.server.reload,
)
if __name__ == '__main__':
main()
```
The `storage_secret` is optional unless the application uses `ui.storage.user` or `ui.storage.browser`. `SecretStr` prevents accidental plaintext display in logs and model representations, while `get_secret_value()` unwraps it only at the NiceGUI integration boundary. Supply production secrets through environment variables or a supported settings secret source rather than committing them.
The example passes an [import string and `factory=True`](https://www.uvicorn.org/settings/#application) to Uvicorn. Uvicorn imports `my_app.main`, calls the zero-argument `create_app` factory, and serves the returned parent FastAPI app. Import strings are also required when Uvicorn creates reload or worker subprocesses; passing `create_app()` directly only supports the simple single-process case.
NiceGUI keeps framework state in its process-local app singleton. Treat `create_app()` as a once-per-worker factory. Calling it repeatedly in one interpreter can register the same pages and lifecycle handlers more than once; tests that create multiple apps must isolate or reset NiceGUI state.
## Lifespan Ordering
The [ASGI lifespan protocol](https://asgi.readthedocs.io/en/latest/specs/lifespan.html) is driven by the server. Uvicorn sends startup before accepting requests and sends shutdown while terminating the process. Lifespan runs once per event loop, including once in each worker process.
Current NiceGUI source integrates with the parent application by:
1. Capturing the parent FastAPI lifespan context.
2. Mounting NiceGUI's internal app on the parent.
3. Replacing the parent lifespan with a wrapper.
4. Starting NiceGUI before entering the original parent lifespan.
5. Exiting the original parent lifespan before shutting down NiceGUI.
This exact ordering comes from the current [`ui.run_with` implementation](https://github.com/zauberzeug/nicegui/blob/main/nicegui/ui_run_with.py) and is version-sensitive. Check the pinned NiceGUI version before making one startup handler depend on another framework's internal ordering.
Create database pools, HTTP clients, and similar resources in the parent [FastAPI lifespan](https://fastapi.tiangolo.com/advanced/events/), then close them after `yield`. Do not create event-loop-bound resources at import time or assume that globals are shared between workers.
## Expose The Server As A Project Script
Map a command name to the no-argument startup function:
```toml title="pyproject.toml"
[project]
name = "my-app"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
"fastapi",
"nicegui",
"pydantic-settings",
"uvicorn[standard]",
]
[project.scripts]
my-app = "my_app.main:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/my_app"]
```
Run the installed command through uv:
```bash
uv run my-app
```
The uv [project entry-point documentation](https://docs.astral.sh/uv/concepts/projects/config/#entry-points) requires a build system so uv installs the project and generates its command. The `[project.scripts]` target follows the [PyPA entry-point specification](https://packaging.python.org/en/latest/specifications/entry-points/#use-for-scripts): its generated wrapper imports `main`, calls it without arguments, and uses the return value as the process exit status. Returning `None` means successful completion.
The settings model now owns host, port, logging, reload, mount path, and storage-secret configuration. Add an explicit CLI settings source or another CLI parser only when the project command needs user-supplied arguments; the entry-point callable itself still receives no arguments.
## Development Reload
Because `main()` supplies an import string, it can enable Uvicorn reload for local development:
```dotenv title=".env"
MY_APP_SERVER__HOST=127.0.0.1
MY_APP_SERVER__RELOAD=true
```
The cached settings object is a process-start snapshot. Changing an environment variable or dotenv file does not mutate a running instance; restart the process, or let the development reloader create a new worker when a watched file changes. Keep reload disabled in production. Uvicorn documents [`reload` and `workers` as mutually exclusive](https://www.uvicorn.org/settings/#production), and each worker would have independent settings, NiceGUI state, lifespan resources, and WebSocket connections. Use one worker by default unless the application has explicitly validated session affinity and externalized every stateful dependency needed across processes.
## Anti-Patterns
| Anti-pattern | Why it fails | Preferred approach |
| --- | --- | --- |
| `ui.run_with(nicegui.app)` | Mounts NiceGUI into itself | Pass a separately created `FastAPI()` instance |
| Calling both `ui.run()` and `ui.run_with()` | Gives two paths responsibility for startup | Choose one ownership model |
| `uvicorn.run(create_app(), reload=True)` | Reload subprocesses cannot import the app object | Use an import string with `factory=True` |
| Calling `uvicorn.run()` at module import time | Importing the module starts a blocking server and breaks subprocess startup | Call it from `main()` |
| Top-level `ui.label(...)` with `ui.run_with()` | Script-mode elements are discarded by this integration | Register UI in `@ui.page` functions or a root callable |
| Multiple workers by default | Process-local UI state and WebSockets are not automatically shared | Start with one worker and validate a distributed design explicitly |
| Reconstructing `Settings()` throughout the app | Re-reads sources and obscures the active configuration lifecycle | Inject the startup snapshot or use the argument-free provider at framework boundaries |
| Adding kwargs to cached `get_settings()` | Retains one hidden process-lifetime instance per argument combination | Construct explicit `Settings(...)` overrides and inject them |
## Verification
Use `TestClient` as a context manager so the parent ASGI lifespan runs:
```python
from fastapi.testclient import TestClient
from my_app.config import GuiSettings, Settings
from my_app.main import create_app
def test_application_routes() -> None:
settings = Settings(
gui=GuiSettings(storage_secret='test-storage-secret'),
)
with TestClient(create_app(settings)) as client:
assert client.get('/healthz').json() == {'status': 'ok'}
assert client.get('/gui/').status_code == 200
```
Also verify:
- startup resources exist while the client context is active and are released afterward
- the mounted UI returns HTML and parent API failures retain FastAPI's JSON responses
- `uv run my-app` starts the server and responds on both the API and UI paths
- shutdown signals complete without orphaned background tasks
## Primary Sources
- [NiceGUI pages, routing, and FastAPI integration](https://www.nicegui.io/documentation/section_pages_routing)
- [NiceGUI `ui.run_with` implementation](https://github.com/zauberzeug/nicegui/blob/main/nicegui/ui_run_with.py)
- [NiceGUI FastAPI example](https://github.com/zauberzeug/nicegui/blob/main/examples/fastapi/main.py)
- [FastAPI lifespan events](https://fastapi.tiangolo.com/advanced/events/)
- [ASGI lifespan protocol](https://asgi.readthedocs.io/en/latest/specs/lifespan.html)
- [Uvicorn settings](https://www.uvicorn.org/settings/)
- [Uvicorn programmatic startup](https://www.uvicorn.org/#running-programmatically)
- [Pydantic settings management](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/)
- [`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache)
- [uv project entry points](https://docs.astral.sh/uv/concepts/projects/config/#entry-points)
- [PyPA entry points specification](https://packaging.python.org/en/latest/specifications/entry-points/)
@@ -1,6 +1,16 @@
# Source Documentation
Use these links for framework-specific details.
Use these links to verify framework-specific behavior before relying on version-sensitive or integration-specific guidance.
## NiceGUI
!!! info "NiceGUI sources"
- [Pages, routing, and FastAPI integration](https://www.nicegui.io/documentation/section_pages_routing)
- [`ui.run_with` implementation](https://github.com/zauberzeug/nicegui/blob/main/nicegui/ui_run_with.py)
- [FastAPI integration example](https://github.com/zauberzeug/nicegui/blob/main/examples/fastapi/main.py)
- [Binding properties and bindable dataclasses](https://www.nicegui.io/documentation/section_binding_properties)
- [Action events](https://www.nicegui.io/documentation/section_action_events)
- [Security best practices](https://www.nicegui.io/documentation/section_security)
## FastAPI
@@ -8,33 +18,53 @@ Use these links for framework-specific details.
- [Lifespan events](https://fastapi.tiangolo.com/advanced/events/)
- [Settings and environment variables](https://fastapi.tiangolo.com/advanced/settings/)
- [Dependencies with yield](https://fastapi.tiangolo.com/tutorial/dependencies/dependencies-with-yield/)
- [SQL databases tutorial](https://fastapi.tiangolo.com/tutorial/sql-databases/)
- [Server-sent events](https://fastapi.tiangolo.com/advanced/server-sent-events/)
- [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/)
## SQLAlchemy and Alembic
## ASGI And Uvicorn
!!! info "Server and lifespan sources"
- [ASGI lifespan protocol](https://asgi.readthedocs.io/en/latest/specs/lifespan.html)
- [Uvicorn settings](https://www.uvicorn.org/settings/)
- [Uvicorn programmatic startup](https://www.uvicorn.org/#running-programmatically)
- [Uvicorn deployment](https://www.uvicorn.org/deployment/)
## uv And Project Scripts
!!! info "Packaging and command sources"
- [uv project entry points](https://docs.astral.sh/uv/concepts/projects/config/#entry-points)
- [uv project packaging](https://docs.astral.sh/uv/concepts/projects/config/#project-packaging)
- [PyPA entry points specification](https://packaging.python.org/en/latest/specifications/entry-points/)
## Styling
!!! info "Styling sources"
- [Tailwind utility-first styling](https://tailwindcss.com/docs/utility-first)
- [Tailwind responsive design and container queries](https://tailwindcss.com/docs/responsive-design)
- [Quasar components](https://quasar.dev/vue-components)
- [Quasar Screen plugin documentation source](https://github.com/quasarframework/quasar/blob/dev/docs/src/pages/options/screen-plugin.md)
- [CSS media queries](https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_media_queries/Using_media_queries)
- [CSS container queries](https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_containment/Container_queries)
## Persistence
!!! info "Persistence sources"
- [SQLAlchemy engine configuration and pooling](https://docs.sqlalchemy.org/en/20/core/engines.html)
- [SQLAlchemy session lifecycle basics](https://docs.sqlalchemy.org/en/20/orm/session_basics.html)
- [SQLAlchemy session lifecycle](https://docs.sqlalchemy.org/en/20/orm/session_basics.html)
- [Alembic tutorial](https://alembic.sqlalchemy.org/en/latest/tutorial.html)
## Pydantic
## Configuration And Dataclasses
!!! info "Pydantic source"
- [Pydantic settings management](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/)
## NiceGUI
!!! info "NiceGUI sources"
- [Pages, routing, and FastAPI integration](https://www.nicegui.io/documentation/section_pages_routing)
- [Security best practices](https://www.nicegui.io/documentation/section_security)
!!! info "Python and Pydantic sources"
- [Pydantic settings management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/)
- [Python dataclasses](https://docs.python.org/3/library/dataclasses.html)
- [PEP 557: Data Classes](https://peps.python.org/pep-0557/)
## LangGraph
!!! info "LangGraph sources"
- [Overview](https://docs.langchain.com/oss/python/langgraph/overview)
- [Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart)
- [Workflows and agents](https://docs.langchain.com/oss/python/langgraph/workflows-agents)
- [Persistence](https://docs.langchain.com/oss/python/langgraph/persistence)
- [Memory concepts](https://docs.langchain.com/oss/python/concepts/memory)
- [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming)
- [Interrupts and human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts)
+442
View File
@@ -0,0 +1,442 @@
---
name: pydantic-settings
description: "Practical guide for implementing typed application configuration with pydantic-settings. Use when designing BaseSettings models, choosing nested or independent settings boundaries, managing settings lifecycles, configuring dotenv or secrets, and customizing source priority safely."
---
# Pydantic Settings Implementation Guide
Use this skill to implement robust, typed application configuration with `pydantic-settings` in production Python services.
## When to Use
- You need a single typed configuration model for app settings.
- You are migrating from ad-hoc `os.getenv(...)` calls.
- You need predictable precedence across init args, env vars, dotenv files, and secrets.
- You need nested settings models and reliable parsing behavior.
- You need to choose between one nested application settings object and independently owned settings objects.
- You need a deliberate construction, caching, or reload lifecycle.
- You need to customize settings sources or source order safely.
## Procedure
### 1. Baseline Model
Create a single settings model for the service boundary:
```python
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class DatabaseSettings(BaseModel):
host: str = "localhost"
port: int = 5432
user: str
password: str
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_prefix="APP_",
env_file=".env",
env_file_encoding="utf-8",
extra="ignore",
frozen=True,
)
debug: bool = False
log_level: str = "info"
database: DatabaseSettings
api_key: str = Field(validation_alias="MY_API_KEY")
```
Quality gate:
1. Required fields fail fast when missing.
2. Defaults are intentional and safe.
### 2. Pick Env Naming Rules
1. Choose one prefix and apply it consistently.
2. Use aliases only for compatibility or external contracts.
3. Document whether env names are case-sensitive.
Quality gate:
1. Team can derive env variable names without guessing.
2. Legacy names are supported only where needed.
### 3. Decide Nested Parsing
For nested models via env vars, configure delimiters intentionally:
```python
model_config = SettingsConfigDict(
env_prefix="APP_",
env_nested_delimiter="__",
env_nested_max_split=1,
)
```
Typical vars:
1. `APP_DATABASE={"host": "db", "port": 5432, "user": "svc", "password": "pw"}`
2. `APP_DATABASE__HOST=db.internal`
Quality gate:
1. Nested overrides behave as expected.
2. Delimiter choice does not collide with field names.
### 4. Confirm Source Priority
Default priority (higher first):
1. CLI args (if enabled)
2. init kwargs
3. env vars
4. dotenv
5. secrets dir
6. defaults
Only customize when required:
```python
from pydantic_settings import PydanticBaseSettingsSource
@classmethod
def settings_customise_sources(
cls,
settings_cls: type[BaseSettings],
init_settings: PydanticBaseSettingsSource,
env_settings: PydanticBaseSettingsSource,
dotenv_settings: PydanticBaseSettingsSource,
file_secret_settings: PydanticBaseSettingsSource,
) -> tuple[PydanticBaseSettingsSource, ...]:
return (init_settings, env_settings, dotenv_settings, file_secret_settings)
```
Quality gate:
1. Priority order is explicit in code.
2. Tests verify conflict resolution.
### 5. Add Secrets Strategy
1. In local development, dotenv is acceptable for non-production values.
2. In deployed environments, prefer env vars or secret managers.
3. For file-mounted secrets, use `secrets_dir`.
Example:
```python
model_config = SettingsConfigDict(
env_prefix="APP_",
env_file=".env",
secrets_dir="/run/secrets",
)
```
Quality gate:
1. No secret literals in repository code.
2. Missing secrets behavior is understood per environment.
### 6. Choose Nested Or Independent Settings Boundaries
Prefer one root `BaseSettings` object with nested `BaseModel` sections when the configuration belongs to one application lifecycle:
```python
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class DatabaseSettings(BaseModel):
host: str = "localhost"
port: int = 5432
class ObservabilitySettings(BaseModel):
log_level: str = "INFO"
json_logs: bool = True
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_prefix="APP_",
env_nested_delimiter="__",
frozen=True,
)
database: DatabaseSettings = Field(default_factory=DatabaseSettings)
observability: ObservabilitySettings = Field(
default_factory=ObservabilitySettings
)
```
This produces names such as `APP_DATABASE__HOST` and gives the application one validated, atomic configuration snapshot. Nested sections should normally inherit from `BaseModel`, not `BaseSettings`; otherwise each nested settings model can collect sources independently and produce surprising results.
Use independent `BaseSettings` classes when the objects have genuinely independent ownership:
1. Different packages or deployable components own the schemas.
2. Each object needs its own env prefix or source policy.
3. A component is optional or loaded lazily.
4. Components need different reload lifecycles.
5. The same component must run outside the application.
Construct independent objects explicitly at the composition root and inject each dependency. Do not nest one `BaseSettings` class inside another merely to reuse its fields. Extract a shared `BaseModel` schema when models need common structure.
### Alternative Database Backends
When one application can run against one of several database backends, model the selected backend as a [discriminated union](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions). Pydantic validates only the variant selected by `driver`, so required PostgreSQL values do not make a SQLite configuration fail, and vice versa.
```python
from typing import Annotated, Literal
from pydantic import BaseModel, Field, SecretStr
from pydantic_settings import BaseSettings, SettingsConfigDict
class SqliteSettings(BaseModel):
driver: Literal["sqlite"] = "sqlite"
path: str = "app.db"
class PostgresSettings(BaseModel):
driver: Literal["postgres"] = "postgres"
host: str
port: int = 5432
database: str
user: str
password: SecretStr
DatabaseSettings = Annotated[
SqliteSettings | PostgresSettings,
Field(discriminator="driver"),
]
class Settings(BaseSettings):
model_config = SettingsConfigDict(
env_prefix="APP_",
env_nested_delimiter="__",
env_file=".env",
extra="ignore",
frozen=True,
)
database: DatabaseSettings
```
Choose one configuration. A SQLite deployment requires no PostgreSQL variables:
```dotenv
APP_DATABASE__DRIVER=sqlite
APP_DATABASE__PATH=./data/app.db
```
A PostgreSQL deployment requires only the PostgreSQL branch:
```dotenv
APP_DATABASE__DRIVER=postgres
APP_DATABASE__HOST=db.internal
APP_DATABASE__PORT=5432
APP_DATABASE__DATABASE=app
APP_DATABASE__USER=app_user
APP_DATABASE__PASSWORD=provided-by-the-runtime
```
After settings validation, select an async SQLAlchemy driver URL. This is a pure configuration step; create the engine, session factory, and sessions in their own lifecycle-managed providers:
```python
from functools import cache
from sqlalchemy import URL
from sqlalchemy.ext.asyncio import AsyncEngine
from sqlalchemy.ext.asyncio import create_async_engine
def get_database_url(settings: Settings) -> str:
match settings.database:
case SqliteSettings(path=path):
url = URL.create(
drivername="sqlite+aiosqlite",
database=path,
)
case PostgresSettings() as database:
url = URL.create(
drivername="postgresql+asyncpg",
host=database.host,
port=database.port,
database=database.database,
username=database.user,
password=database.password.get_secret_value(),
)
return url.render_as_string(hide_password=False)
@cache
def get_engine(database_url: str) -> AsyncEngine:
return create_async_engine(database_url, pool_pre_ping=True)
async def dispose_engine(database_url: str) -> None:
engine = get_engine(database_url)
try:
await engine.dispose()
finally:
get_engine.cache_clear()
async def refresh_engine(database_url: str) -> AsyncEngine:
await dispose_engine(database_url)
return get_engine(database_url)
```
At the composition boundary, resolve the URL once with `get_database_url(settings)` and use it to retrieve the cached engine. In FastAPI, expose that engine through lifespan and build one `async_sessionmaker` from it; each request or unit of work then creates its own `AsyncSession`. Do not call `aiosqlite.connect()` or `asyncpg.create_pool()` directly: `aiosqlite` and `asyncpg` are selected as SQLAlchemy drivers by the URL, while SQLAlchemy owns pooling, disposal, and session integration.
The nested variants remain `BaseModel` classes. `Settings` is the only `BaseSettings` model and therefore the only object that reads environment variables, dotenv files, or secrets. This keeps one source policy and validated configuration snapshot while keeping the engine, session factory, and sessions in their distinct lifecycles. See the [SQLAlchemy asyncio extension](https://docs.sqlalchemy.org/en/21/orm/extensions/asyncio.html), the [engine lifecycle guidance](../async-fastapi-sqlmodel/references/engine.md), and the [session lifecycle guidance](../async-fastapi-sqlmodel/references/session.md).
Quality gate:
1. Nested sections share one source policy and lifecycle.
2. Independent settings have distinct owners, prefixes, or lifecycles.
3. The application does not repeatedly scan the same sources through accidental nested `BaseSettings` construction.
4. Each backend configuration validates without values required only by another backend.
5. One cached `AsyncEngine` exists per configured driver URL, while each request or unit of work receives a new `AsyncSession`.
### 7. Own The Settings Lifecycle
For most applications, construct settings once at the composition root and pass the validated object to services:
```python
def main() -> None:
settings = Settings()
application = Application(settings=settings)
application.run()
```
This makes ownership, startup failure, and test overrides explicit. Treat the object as a snapshot: environment variables and files changing later do not update an existing instance. Prefer `frozen=True` for shared settings so consumers cannot silently mutate process-wide configuration.
Use [`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache) only when process-lifetime singleton access is intentional and explicit injection is awkward, such as a framework dependency provider:
```python
from functools import cache
@cache
def get_settings() -> Settings:
return Settings()
```
Keep the cached factory argument-free. Passing override kwargs creates one cached instance per argument combination, retains those values for the process lifetime, and obscures which configuration is active. In tests, instantiate `Settings(...)` directly or override the dependency; when a test must exercise the cached getter, isolate environment changes with `get_settings.cache_clear()` before and after the assertion.
`cache` is process-local. Every worker process gets its own instance, and concurrent first calls can construct more than one instance before the cache is populated. Settings construction must therefore be side-effect free; create engines, clients, and sessions in their own lifecycle-managed providers.
Quality gate:
1. Settings are created once per intended application or worker lifecycle.
2. Cached factories are argument-free and side-effect free.
3. Tests do not leak cached settings or environment changes.
4. Resource construction is separate from configuration parsing.
### 8. Reload Deliberately
Static service configuration should normally require a process restart. If runtime reload is a real requirement, construct a fresh settings instance and atomically replace the owned reference. Do not call `__init__()` on a shared instance: readers can observe mutation in progress, and resources derived from old values may remain alive.
Settings sources are synchronous. In an async application, construction or reload that reads dotenv, secrets, JSON, TOML, or YAML files should run in a worker thread:
```python
import asyncio
async def load_settings() -> Settings:
return await asyncio.to_thread(Settings)
```
Clearing `get_settings` is sufficient for controlled tests or single-threaded administration, but it is not an atomic live-reload protocol. Concurrent applications should own the current reference behind an application-specific lock or lifecycle manager, swap in a fully validated replacement, and then rebuild dependent resources.
Quality gate:
1. Reload creates and validates a replacement before publication.
2. Readers cannot observe a partially mutated object.
3. Dependent resources are recreated after the settings reference changes.
4. File-backed source reads do not block an async event loop.
### 9. Add Focused Lifecycle Tests
Do not add tests that re-validate baseline `pydantic-settings` functionality unless custom behavior is layered on top. Test the application-owned behavior instead:
1. Repeated cached getter calls return the same instance.
2. Cache clearing after an environment change returns a newly validated instance.
3. Explicitly injected settings bypass global cached state.
4. Reload swaps the settings snapshot and rebuilds dependent resources, when reload is supported.
Suggested invocation:
1. `uv run pytest -q`
## Completion Checks
1. Settings ownership matches the application or component lifecycle.
2. Source precedence is documented and tested.
3. Env naming conventions and aliases are explicit and stable.
4. Nested parsing behavior is tested when custom parsing behavior is added.
5. Secrets and dotenv usage are environment-appropriate and do not leak sensitive defaults.
6. Validation errors are actionable and fail fast for required values.
7. Cached factories are argument-free, process-local, and cleared deliberately in tests.
8. Nested models share one source policy; independent settings have an explicit ownership reason.
9. Runtime reload, if supported, replaces a validated snapshot and rebuilds dependent resources.
## Output Contract
When this skill is applied, return:
1. Which references were consulted.
2. The chosen source-precedence model and why.
3. The exact parsing and alias decisions made.
4. Any deferred choices and their risk.
5. The validation commands or tests run to confirm behavior.
Use these upstream docs when implementing or reviewing `pydantic-settings` behavior.
## Source Docs
### Primary
- [Settings Management](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/)
- [pydantic-settings package repository](https://github.com/pydantic/pydantic-settings)
### Core Concepts
- [Field aliases](https://pydantic.dev/docs/validation/latest/concepts/fields/#field-aliases)
- [Alias choices](https://pydantic.dev/docs/validation/latest/concepts/alias#aliaspath-and-aliaschoices)
- [Validation default behavior](https://pydantic.dev/docs/validation/latest/concepts/fields#validate-default-values)
- [ImportString type](https://pydantic.dev/docs/validation/latest/api/pydantic/types/#pydantic.types.ImportString)
### Priority And Sources
- [Field value priority](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#field-value-priority)
- [Customise settings sources](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#customise-settings-sources)
- [Other settings source types](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#other-settings-source)
### Environment And Parsing
- [Environment variable names and prefix behavior](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#environment-variable-names)
- [Case sensitivity behavior](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#case-sensitivity)
- [Parsing environment variable values](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#parsing-environment-variable-values)
- [Nested model default partial updates](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#nested-model-default-partial-updates)
### Lifecycle And Reloading
- [In-place reloading](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#in-place-reloading)
- [Async environments](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#async-environments)
- [`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache)
### Dotenv And Secrets
- [Dotenv support](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#dotenv-env-support)
- [Secrets](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#secrets)
- [Nested secrets](https://pydantic.dev/docs/validation/latest/concepts/pydantic_settings/#nested-secrets)
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---
name: pytest-scaffolding
description: "Scaffold a maintainable, hierarchical pytest suite with fast defaults and clear escalation paths for FastAPI and SQLAlchemy tests. Use when creating or reorganizing tests, defining fixture/marker boundaries, or making test strategy progressively discoverable."
argument-hint: "Target scope plus stack details (pure Python, FastAPI, SQLAlchemy sync, SQLAlchemy async, or mixed)"
x-personal-mcp:
id: pytest-scaffolding
version: 1.0.0
tags:
- pytest
- testing
- python
capabilities:
- resource://skills/pytest-scaffolding/document
depends_on: []
---
# Pytest Scaffolding
Create test scaffolding that stays fast for daily work and scales safely as dependencies increase.
This skill is optimized for progressive discoverability:
1. Start with the shortest path in this file.
2. Load exactly one deeper reference only when a decision requires it.
3. Continue only as far as needed for the current task.
Repository defaults:
- `uv run pytest` is the canonical invocation.
- pytest settings live in `pyproject.toml` under `[tool.pytest.ini_options]`.
- strict marker checking is expected (`--strict-markers`).
## Discovery Ladder
### Level 0: Scope And Stack Triage (always)
Collect:
1. Target scope (repo, package, module).
2. Stack shape (pure Python, FastAPI, SQLAlchemy sync, SQLAlchemy async, or mixed).
3. Speed target (what must stay instant).
4. CI gate policy (which marker groups block merge).
If any are missing, ask concise clarifying questions before scaffolding.
### Level 1: Core pytest scaffold (default)
Use this for all stacks first:
1. Mirror `src/` into `tests/` with one starter file per core module.
2. Classify test intent by cost:
- `unit`: no DB/network/filesystem side effects.
- `integration`: framework, DB, or multi-layer contracts.
- `smoke`: thin critical-path checks.
3. Scaffold each new module with:
- one happy-path test,
- one failure/edge test,
- TODO anchors for deeper assertions.
4. Keep fixtures layered:
- global lightweight fixtures in `tests/conftest.py`,
- domain fixtures in subtree `conftest.py` only when needed.
5. Register markers early: `unit`, `integration`, `smoke`, `slow`, `external`.
6. Validate in order:
- `uv run pytest --collect-only -q`
- `uv run pytest -m unit -q`
- `uv run pytest -q` when dependencies are available.
Load next reference only if needed:
- Baseline details and rationale: [pytest-docs.md](./references/pytest-docs.md)
### Level 2: FastAPI branch (only for HTTP/dependency/lifespan concerns)
Escalate here when testing API routes, dependency injection boundaries, or app lifespan behavior.
Apply these defaults:
1. Prefer `TestClient` with sync `def` tests for route behavior.
2. Use `AsyncClient` + `@pytest.mark.anyio` only when test logic must await other async work.
3. Prefer `app.dependency_overrides` over patching internals.
4. Reset dependency overrides in teardown after every test/fixture.
5. For startup/shutdown semantics:
- use `TestClient` as context manager, or
- use `LifespanManager` with async client.
Marker intent in FastAPI-heavy suites:
- `unit`: service logic without HTTP/DB.
- `integration`: route + DI + DB contract checks.
- `smoke`: one request per critical user path.
Reference: [fastapi-testing.md](./references/fastapi-testing.md)
### Level 3: SQLAlchemy branch (only for DB transaction/session design)
Escalate here when session lifecycle, transaction isolation, or async ORM behavior matters.
Apply these defaults:
1. Create engine once per test session.
2. Open connection + outer transaction per test.
3. Bind session with `join_transaction_mode="create_savepoint"`.
4. Allow code under test to call `commit()` safely; rollback outer transaction at test end.
5. Keep unit tests DB-free; DB tests belong under `integration`.
Async additions:
- use async fixtures and `@pytest.mark.anyio`.
- set `expire_on_commit=False` for `AsyncSession`.
- avoid implicit lazy IO; use eager loading (`selectinload`) or explicit refresh.
SQLite in-memory with threaded test clients:
- use `StaticPool` when required by thread/connection sharing.
Reference: [sqlalchemy-testing.md](./references/sqlalchemy-testing.md)
## Branching Logic Summary
- If pure logic can be faked cleanly, keep in `unit`.
- If framework/DB contract is the behavior under test, use `integration`.
- If external service credentials/network is required, gate behind `external`.
- If suite slows down, split by marker before broadening fixture scope.
- If async relationship access raises `MissingGreenlet`, switch to eager loading strategy.
## Completion Checks
A scaffold pass is complete when all are true:
1. Core source areas map to clear test modules.
2. Fast path (`-m unit`) is deterministic and quick.
3. Integration and external paths are isolated by fixtures and markers.
4. No unregistered-marker failures occur.
5. Structure is understandable without extra oral context.
6. Clear TODO extension points exist for deeper assertions.
## Output Contract
When this skill is applied, return:
1. Proposed test tree diff.
2. Marker and fixture plan.
3. Exact fast-path and full-path commands.
4. Which reference level was loaded and why.
5. Risks or open questions before expanding assertions.
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---
name: pytesting
description: "Reference hub for pytest suite structure, naming, markers, and stack-specific testing patterns. Optimized for progressive discovery so naming and hierarchy guidance are loaded first when shaping or reorganizing tests."
---
# Pytesting
This skill is a collection of preferences and links to source documentation for building and maintaining pytest suites.
Use it to quickly find the right guidance for:
1. Baseline pytest structure and marker strategy.
2. Naming conventions and test hierarchy organization.
3. FastAPI route, dependency override, and lifespan testing patterns.
4. SQLAlchemy transaction and session testing patterns.
5. AsyncIO loop-scope, fixture-lifecycle, and cancellation-safe testing patterns.
Repository defaults:
- `uv run pytest` is the canonical invocation.
- pytest settings live in `pyproject.toml` under `[tool.pytest.ini_options]`.
- strict marker checking is expected (`--strict-markers`).
## Progressive Discovery Start
Use this load order by default so guidance stays targeted and naming conventions are pulled in early:
1. Classify intent first: naming and organization, baseline pytest mechanics, FastAPI testing, SQLAlchemy testing, or mixed.
2. For create/restructure/rename tasks, load [naming-and-organization.md](./references/naming-and-organization.md) first.
3. Load [pytest-docs.md](./references/pytest-docs.md) next for fixture and marker defaults.
4. Load at most one stack-specific reference unless the request is explicitly mixed stack.
5. If confidence is low after two references, ask one clarifying question before loading more.
Load budget defaults:
1. Single-stack task: 1 to 2 references.
2. Mixed-stack task: up to 3 references.
3. Avoid loading all references unless the user explicitly asks for a broad audit.
## Intent Router
Open only the reference that matches the immediate task.
1. Naming, file layout, discovery prefixes, class/function naming: [naming-and-organization.md](./references/naming-and-organization.md)
2. Fixture layering, marker policy, collect-only and fast-path commands: [pytest-docs.md](./references/pytest-docs.md)
3. Route tests, dependency overrides, lifespan handling: [fastapi-testing.md](./references/fastapi-testing.md)
4. Session and transaction fixtures, async ORM behavior: [sqlalchemy-testing.md](./references/sqlalchemy-testing.md)
5. Async test mode selection, event loop scope, cancel-scope teardown issues: [asyncio-testing.md](./references/asyncio-testing.md)
## Naming Pull-In Triggers
Always consult [naming-and-organization.md](./references/naming-and-organization.md) before recommending structure when any of these are true:
1. New tests are being added.
2. Existing tests are being reorganized or renamed.
3. The request mentions conventions, readability, hierarchy, or discoverability.
4. The task introduces parametrization where case naming affects failure readability.
## Guiding Principles
These principles are abstract, but are the highest priority to follow.
- Much of testing is very well-trodden. In general, tests should follow whatever conventions there are.
- Tests will be run very frequently, so it's important that they run quickly and deterministically.
- When tests fail, it should be easy to determine what failed and fix it.
- Always be on guard against tests that are tautological. Every test should provide specific value by capturing something about the intent of the program.
## Pytest Best Practices
These are stable defaults regardless of stack:
1. Apply pytest naming and hierarchy conventions first so discovery and ownership stay predictable; see [naming-and-organization.md](./references/naming-and-organization.md).
2. Mirror `src/` into `tests/` so ownership and coverage are obvious.
3. Keep fixtures explicit and layered (`tests/conftest.py` globally, subtree `conftest.py` for domain-specific fixtures).
4. Register markers up front (`unit`, `integration`, `smoke`, `slow`, `external`) and keep strict marker checks enabled.
5. Separate fast feedback (`-m unit`) from broader integration/external lanes.
6. Validate structure early with collection checks before expanding assertions.
7. Keep test scope tight and count intentional; add tests only when each case protects a distinct behavior.
8. Start with the single core-intent behavior path, then add edge cases based on real risk.
9. Prefer parametrized tests for behavior variants instead of cloning near-identical test functions.
10. Reject low-signal assertions (for example `assert True` patterns) and avoid tests that only assert a mock was called.
11. Prefer behavior-first tests that exercise real code paths and concrete inputs over patching internals.
12. Use monkeypatching, mocks, and fakes extremely sparingly, only when no practical real-input alternative exists, and only after explicit user confirmation.
## Universal Test Double Policy (Repo-Local Placement)
To avoid over-using monkeypatching, mocks, fakes, etc, apply this policy whenever a test change introduces one of them:
1. Attempt a real-input, real-object test design first.
2. If that approach is impractical, explain why and request user confirmation before adding monkeypatching, mocks, or fakes.
3. Keep any approved test double narrowly scoped and document the exact boundary it replaces.
4. Do not treat call-only verification as sufficient; pair any test double with assertions on observable behavior or outputs.
5. Revisit approved test doubles when implementation seams improve so they can be removed.
## Stack-Specific Guidance
- For FastAPI, prefer dependency overrides and clear lifecycle handling; see [fastapi-testing.md](./references/fastapi-testing.md).
- For SQLAlchemy, prefer transaction-safe session fixtures and explicit async loading strategy; see [sqlalchemy-testing.md](./references/sqlalchemy-testing.md).
- For async fixtures, loop-scope selection, and cancellation-safe teardown, see [asyncio-testing.md](./references/asyncio-testing.md).
- For naming and tree organization, use the conventions in [naming-and-organization.md](./references/naming-and-organization.md).
## Source Documentation Entry Points
Primary upstream docs are curated in each reference page. Start with:
1. Pytest good practices: [pytest docs](https://docs.pytest.org/en/stable/explanation/goodpractices.html)
2. Pytest fixtures: [fixture how-to](https://docs.pytest.org/en/stable/how-to/fixtures.html)
3. Pytest markers: [marker examples](https://docs.pytest.org/en/stable/example/markers.html)
4. FastAPI testing: [FastAPI testing tutorial](https://fastapi.tiangolo.com/tutorial/testing/)
5. SQLAlchemy transaction testing: [SQLAlchemy external transaction pattern](https://docs.sqlalchemy.org/en/20/orm/session_transaction.html#joining-a-session-into-an-external-transaction-such-as-for-test-suites)
6. Pytest monkeypatch usage and limits: [monkeypatch how-to](https://docs.pytest.org/en/stable/how-to/monkeypatch.html)
7. pytest-asyncio configuration: [pytest-asyncio config](https://pytest-asyncio.readthedocs.io/en/stable/reference/configuration.html)
8. AnyIO cancellation semantics: [AnyIO cancellation and timeouts](https://anyio.readthedocs.io/en/stable/cancellation.html)
## Quick Validation Commands
Use these commands to check structure and execution lanes:
1. `uv run pytest --collect-only -q`
2. `uv run pytest -m unit -q`
3. `uv run pytest -m "not external" -q`
4. `uv run pytest -q`
## Output Contract
When this skill is applied, return:
1. Which references were consulted.
2. The discovery path used (intent classification, load order, and why).
3. Recommended structure, naming, fixture, and marker decisions.
4. Concrete naming outcomes: file/module naming pattern, class usage decision, and any parametrization `ids` conventions.
5. Exact validation commands.
6. Relevant source-doc links for any non-trivial recommendation.
7. Risks, assumptions, or open questions.
8. Explicit confirmation status if monkeypatching, mocks, or fakes were requested or used.
@@ -0,0 +1,108 @@
# AsyncIO Testing Patterns (Pytest, FastAPI, AnyIO)
!!! info "Primary sources"
- [pytest-asyncio configuration](https://pytest-asyncio.readthedocs.io/en/stable/reference/configuration.html)
- [pytest-asyncio concepts](https://pytest-asyncio.readthedocs.io/en/stable/concepts.html)
- [pytest-asyncio fixture loop scope how-to](https://pytest-asyncio.readthedocs.io/en/stable/how-to-guides/change_fixture_loop.html)
- [pytest-asyncio default fixture loop scope how-to](https://pytest-asyncio.readthedocs.io/en/stable/how-to-guides/change_default_fixture_loop.html)
- [AnyIO cancellation and cancel-scope safety](https://anyio.readthedocs.io/en/stable/cancellation.html)
- [FastAPI async tests](https://fastapi.tiangolo.com/advanced/async-tests/)
## Agent Quick Path
Use this reference when tests involve asynchronous fixtures, HTTP clients, task groups, or teardown failures.
1. Confirm async plugin mode in pytest config (`asyncio_mode`).
2. Keep async fixture loop scope predictable, defaulting to `function` unless there is a measured need to broaden it.
3. Prefer one async testing model per lane (pytest-asyncio or AnyIO-style markers), and keep it consistent.
4. Keep async fixtures small and isolate stateful resources to the narrowest useful scope.
5. If teardown errors mention cancel scopes or task groups, validate that setup and teardown run in the same task context.
## Baseline Configuration
Recommended defaults for most projects using `pytest-asyncio`:
```toml
[tool.pytest.ini_options]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
```
Why:
- [Strict mode](https://pytest-asyncio.readthedocs.io/en/stable/concepts.html#test-discovery-modes) is safer for multi-plugin environments, but [auto mode](https://pytest-asyncio.readthedocs.io/en/stable/concepts.html#test-discovery-modes) is often simpler when the suite is primarily asyncio-based.
- [Function loop scope](https://pytest-asyncio.readthedocs.io/en/stable/reference/configuration.html#asyncio-default-fixture-loop-scope) minimizes cross-test coupling and avoids many lifecycle surprises.
If a fixture or test needs broader loop sharing, make it explicit instead of changing suite-wide defaults:
```python
import pytest
import pytest_asyncio
@pytest_asyncio.fixture(loop_scope="module")
async def shared_resource():
...
@pytest.mark.asyncio(loop_scope="module")
async def test_uses_shared_loop(shared_resource):
...
```
## FastAPI Endpoint Test Patterns
Use [FastAPI's async testing guidance](https://fastapi.tiangolo.com/advanced/async-tests/) as the default:
1. Use `httpx.AsyncClient` with `ASGITransport` for async endpoint tests.
2. Mark async tests with one consistent marker style for the suite.
3. If app lifespan hooks matter, add [LifespanManager](https://fastapi.tiangolo.com/advanced/async-tests/#httpx) support because `AsyncClient` alone does not trigger lifespan events.
Example:
```python
import pytest
from httpx import ASGITransport, AsyncClient
@pytest.mark.asyncio
async def test_healthz(app):
async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
response = await client.get("/healthz")
assert response.status_code == 200
```
## Fixture Design For Async Reliability
Apply these patterns first:
1. Keep async fixtures narrow (`function` scope by default).
2. Keep one responsibility per fixture when possible.
3. Prefer yield fixtures and pair each setup step with teardown in the same fixture.
4. Avoid mixing many independent event-loop lifecycles in one fixture chain.
When using transports that manage internal task groups (for example, streaming clients), avoid patterns that risk splitting lifecycle across different task contexts.
## Troubleshooting Cancel-Scope Teardown Failures
When you see errors like `Attempted to exit cancel scope in a different task than it was entered in`, treat it as an async lifecycle-ownership issue first.
Checklist:
1. Verify fixture and test loop scopes are compatible and explicit.
2. Confirm async resource setup and teardown are owned by the same fixture context.
3. Reduce fixture scope (`module` or `session` -> `function`) to test for loop/task ownership drift.
4. Ensure the suite uses one primary async plugin model for the failing lane.
5. Re-run with focused selection and skip reasons to isolate first failing fixture:
- `uv run --group test python -m pytest -m smoke tests/web -q -rs`
Relevant references:
- [Avoiding cancel scope stack corruption](https://anyio.readthedocs.io/en/stable/cancellation.html#avoiding-cancel-scope-stack-corruption)
- [pytest-asyncio configuration](https://pytest-asyncio.readthedocs.io/en/stable/reference/configuration.html)
- [pytest fixture teardown behavior](https://docs.pytest.org/en/stable/how-to/fixtures.html#teardown-cleanup-aka-fixture-finalization)
## Commands Worth Remembering
- `uv run --group test python -m pytest --collect-only -q`
- `uv run --group test python -m pytest -m smoke tests/web -q -rs`
- `uv run --group test python -m pytest -m integration -q`
- `uv run --group test python -m pytest -q`
@@ -0,0 +1,118 @@
# Pytest Naming Conventions and Test Organization
!!! info "Primary sources"
- [Good integration practices](https://docs.pytest.org/en/stable/explanation/goodpractices.html)
- [Changing standard (Python) test discovery](https://docs.pytest.org/en/stable/example/pythoncollection.html)
- [How to use fixtures](https://docs.pytest.org/en/stable/how-to/fixtures.html)
- [How to parametrize fixtures and test functions](https://docs.pytest.org/en/stable/how-to/parametrize.html)
- [Marker examples](https://docs.pytest.org/en/stable/example/markers.html)
## Agent Quick Path
Use this when creating or reorganizing test modules so naming and hierarchy stay predictable.
1. Mirror the product domain structure in `tests/` so ownership is obvious.
2. Encode broad context in module and class names (`test_*.py`, `Test*`).
3. Keep leaf test names short and behavior-focused (`test_*`).
4. Use `class Test<Subject>:` only for grouping related scenarios.
5. Place fixtures in the nearest `conftest.py` needed by scope.
6. Separate expensive tests with markers first, directories second.
## Naming Conventions
### File and directory naming
- Use lowercase snake_case for test file names: `test_user_service.py`.
- Keep directories domain-oriented and stable over time: `tests/orders/`, `tests/billing/`.
- Prefer descriptive test names over internal ticket numbers or implementation details.
### Test function naming
- Prefer hierarchical naming: put broad context in folder/module/class, and keep the function name focused on the final assertion.
- Keep pytest discovery prefixes intact:
- modules start with `test_`
- classes start with `Test`
- functions start with `test_`
- Start with user-visible behavior or contract, not private helper names.
Recommended pattern:
- module: `test_<subject>.py`
- class: `Test<Operation>` or `Test<Scenario>`
- function: `test_<expected_outcome>`
Examples:
- Flat (still valid): `test_create_order_rejects_invalid_currency`
- Class-context: `TestOrder -> TestCreate -> test_rejects_invalid_currency`
- Module-context: `test_order.py -> TestCreate -> test_rejects_invalid_currency`
- Module + class context can similarly shorten:
- `test_token.py -> TestRefresh -> test_rotates_session_id`
- `test_user_list.py -> TestListUsers -> test_returns_empty_for_new_tenant`
### Test class naming
- Use `class Test<SubjectOrScenario>:` for scenario grouping and context reduction.
- Keep class names noun-focused (`TestOrderService`) rather than action-focused.
- Avoid xUnit style setup inheritance when fixtures can express dependencies directly.
## Hierarchy and Organization Patterns
Two patterns work well; choose one and apply it consistently.
### Pattern A: Source-mirror hierarchy (default for product code ownership)
```text
src/
app/
orders/service.py
billing/invoice.py
tests/
app/
orders/test_service.py
billing/test_invoice.py
```
Use this when teams own modules by source path and want direct test-to-source mapping.
### Pattern B: Cost-lane hierarchy (default for CI policy clarity)
```text
tests/
unit/
orders/test_service.py
integration/
api/test_orders.py
persistence/test_order_repository.py
smoke/
test_health.py
```
Use this when CI gating is based on cost lanes and marker filtering.
### Hybrid rule (recommended)
- Keep a source-mirror tree for local ownership.
- Add markers (`unit`, `integration`, `smoke`, `external`) for runtime policy.
- Avoid duplicating both trees unless the repository already requires it.
## Fixture Placement Strategy
- Put universal lightweight fixtures in `tests/conftest.py`.
- Put domain fixtures in subtree `conftest.py` files close to where they are used.
- Keep fixtures composable and explicit; avoid large fixture "god objects".
- Use `yield` fixtures for teardown so cleanup is always paired with setup.
## Parametrize and ID Naming
- Use `pytest.mark.parametrize` for behavior matrices instead of copy/paste tests.
- Provide explicit `ids=` labels when case names are not obvious.
- Keep IDs business-meaningful (`"expired-token"`, `"zero-balance"`) so failures are readable.
## Collection and Structure Checks
Use these checks after introducing new test files or renaming modules:
- `uv run pytest --collect-only -q`
- `uv run pytest -m unit -q`
- `uv run pytest -m "not external" -q`
If collection surprises appear, verify file names, marker registration, and directory placement first.
## Common Anti-Patterns
- Mixed naming styles (`testFoo.py`, `test_foo.py`, `foo_test.py`) in one repository.
- Deep fixture chains that hide setup behavior.
- Test names that encode implementation details instead of behavior.
- Moving slow tests into `unit` directories without marker updates.
- Sharing mutable module-level state across tests.
@@ -19,6 +19,7 @@ Use this file when you need fast pytest scaffolding defaults without framework-s
Load other references only when needed:
- FastAPI routes/dependency injection/lifespan: `fastapi-testing.md`
- SQLAlchemy sessions/transactions/DB fixtures: `sqlalchemy-testing.md`
- Naming conventions and test hierarchy: `naming-and-organization.md`
## Practical Guidance For This Skill
- Use src-aligned test layout and keep test discovery conventional.
@@ -1,117 +0,0 @@
---
name: python-logging-dictconfig
description: 'Set up idiomatic Python logging with logging.config.dictConfig. Use when creating or refactoring logging setup, standardizing handlers/formatters, and enforcing centralized config.'
argument-hint: 'Target context (single script, package, FastAPI app, or CLI) and desired log destinations'
x-personal-mcp:
id: python-logging-dictconfig
version: 1.0.0
tags:
- logging
- python
- observability
capabilities:
- resource://skills/python-logging-dictconfig/document
depends_on: []
---
# Idiomatic Python Logging with dictConfig
Use this skill to produce a minimal, centralized logging setup using `logging.config.dictConfig`.
Load references only when needed:
- Python logging overview and hierarchy: [Python logging references](./references/python-logging-docs.md)
## When to Use
- A project configures logging ad hoc with `basicConfig` across multiple modules.
- You need one canonical logging configuration for app startup.
- You need consistent formatting and levels across console/file handlers.
- You want library modules to use named loggers without configuring logging themselves.
## Inputs To Collect
1. Runtime type: script, library, web app, worker, CLI.
2. Destinations: stdout only, file only, or both.
3. Desired default level: `INFO`, `DEBUG`, etc.
4. Whether third-party loggers should be tuned (for example `uvicorn`, `sqlalchemy`).
If missing, assume:
- stdout handler
- human-readable formatter
- root level `INFO`
- `disable_existing_loggers: False`
## Procedure
1. Define a single `LOGGING` dictionary in one startup-oriented module (for example `logging_config.py`).
2. Include `version: 1` and set `disable_existing_loggers: False` unless there is a specific reason to silence existing loggers.
3. Define formatters first, then handlers, then logger routing (`root` and optional named `loggers`).
4. Use `logging.config.dictConfig(LOGGING)` exactly once during application startup.
5. In all modules, get loggers via `logger = logging.getLogger(__name__)` and never call `basicConfig`.
6. Keep libraries configuration-free: libraries should emit logs, applications decide routing.
7. Verify behavior with a quick smoke check at multiple levels (`DEBUG`, `INFO`, `WARNING`, `ERROR`).
## Minimal Baseline Templates
### Configuration
!!! warning "Don't use the name `logging.py` because it will conflict
```python title="logging_config.py"
import logging.config
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"basic": {
"format": "%(asctime)s.%(msecs)03d [%(levelname)s] %(message)s",
"datefmt": "%Y-%m-%d %H:%M:%S",
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "basic",
"stream": "ext://sys.stdout",
}
},
"root": {
"level": "INFO",
"handlers": ["console"],
},
}
def configure_logging() -> None:
logging.config.dictConfig(LOGGING)
```
```python title="app.py"
# app startup
from .logging_config import configure_logging
configure_logging()
```
### Usage
The preferred way of instantiating loggers is at the top of modules like this:
```python
import logging
logger = logging.getLogger(__name__)
```
## Completion Checks
1. `dictConfig` is called once at startup, not per module.
2. No `basicConfig` calls remain.
3. Modules use `getLogger(__name__)`.
4. Logs appear at expected level and destination.
5. Third-party logger noise is intentionally configured or left at defaults.
6. No module named `logging.py` in the project.
## Branching Guidance
- If structured logs are required: switch formatter output to JSON while keeping `dictConfig` topology unchanged.
- If both console and file output are needed: add a file handler and attach it to `root`.
- If a specific framework logger is too noisy: add a named logger override under `loggers`.
+246
View File
@@ -0,0 +1,246 @@
---
name: python-logging
description: 'Design, review, or refactor Python logging. Use when choosing logger names, levels, handlers, library/application boundaries, basicConfig, dictConfig, structured logs, or operational logging defaults.'
---
# Python Logging
Use this skill to produce idiomatic Python logging guidance or a small logging setup for an application, library, CLI, worker, or web service.
Load references only when needed:
[Python logging references](./references/python-logging-docs.md)
: Python logging overview, library guidance, handlers, and dictConfig schema
[JSON file logging pattern](./references/json-file-logging.md)
: Queue-backed rotating JSON file pattern for local machine-readable logs
[Network logging minimal example](./references/network-logging-minimal-example.md)
: Minimal network logging example with a receiver and queue-backed client
[HTTPX logging handler example](./references/httpx-logging-handler-example.md)
: HTTP JSON logging example with `httpx` and a queue-backed client
## When to Use
- A project mixes `print`, root logger calls, scattered `basicConfig`, or ad hoc handlers.
- You need to choose logging levels, destinations, formatter fields, or logger names.
- You need a clear boundary between library logging and application logging configuration.
- You need a centralized logging setup, including a `logging.config.dictConfig` section.
- You are tuning framework or third-party loggers such as `uvicorn`, `sqlalchemy`, or HTTP clients.
## Inputs To Collect
1. Runtime type: script, library, CLI, web app, worker, service, or notebook.
2. Audience: humans in a terminal, operators in files, machines in JSON, or test assertions.
3. Destinations: stdout/stderr, file, rotating file, queue, syslog, external collector, or none for libraries.
4. Default level and verbosity controls: `INFO`, `DEBUG`, CLI flag, environment variable, or config file.
5. Operational constraints: async event loop, multiprocessing, container logs, sensitive data, or high-volume paths.
If missing, assume:
- application code, not a reusable library
- stdout console logging
- human-readable formatter
- root level `INFO`
- no file logging unless requested
## Procedure
1. Classify the project boundary first: application code configures logging; library code emits logs and avoids configuring handlers.
2. In modules, create loggers with `logger = logging.getLogger(__name__)` so logger names follow the package hierarchy.
3. Use level semantics consistently: `DEBUG` for diagnosis, `INFO` for normal milestones, `WARNING` for notable recoverable conditions, `ERROR` for failed operations, and `CRITICAL` for process-threatening failures.
4. Prefer parameterized logging calls such as `logger.info("Processed %s items", count)` so message formatting is deferred until the record is emitted.
5. Configure handlers and formatters once during application startup.
6. Keep third-party logger overrides explicit and narrow. Tune noisy loggers by name instead of muting broad logger hierarchies.
7. Smoke-check output at expected levels and destinations, including one suppressed `DEBUG` message and one exception path if errors are logged.
## Best Practices
- Do not name a module `logging.py`; it shadows the standard library package.
- Do not call `basicConfig` or attach handlers in every module.
- Do not log to the root logger from libraries. Use named loggers and, only if needed, attach `logging.NullHandler()` to the library's top-level logger.
- Do not create loggers per request, user, file, or connection. Use contextual fields, adapters, or filters instead.
- Use `logger.exception(...)` only inside an exception handler when the traceback is useful.
- For async or high-throughput code, avoid slow network or file handlers on the hot path; consider `QueueHandler` and a listener.
- Avoid custom levels unless there is a strong interoperability reason.
## Examples
### `logging.basicConfig`
```python title="Bare minimum"
import logging
logging.basicConfig(level=logging.INFO, format="%(message)s")
logging.info("Hello, world!")
```
```python title="With a little formatting"
import logging
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logging.info("Hello, world!")
```
### `logging.config.dictConfig`
```python title="Minimal dictConfig example"
import logging
import logging.config
logging.config.dictConfig(
{
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"console": {
"format": "%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s",
"datefmt": "%Y-%m-%dT%H:%M:%S",
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "console",
}
},
"root": {"level": "INFO", "handlers": ["console"]},
}
)
logging.info("Hello world")
```
### Config Composition
Example function suitable for merging dicts for `dictConfig`
```python title="composed config"
from collections.abc import Iterable
from collections.abc import Mapping
from collections.abc import Sequence
from copy import copy
from functools import reduce
BASE = ...
CONSOLE = ...
JSON_FILE = ...
RICH = ...
def merge(a: Mapping, b: Mapping) -> Mapping:
"""Recursively merge config dicts"""
a = dict(a)
for k, v in b.items():
match a.get(k), v:
case Mapping() as inner, Mapping():
a[k] = merge(inner, v)
case Sequence() as inner, Iterable():
new = list(copy(inner))
a[k] = new + [sub_v for sub_v in v if sub_v not in new]
case _:
a[k] = v
return a
def configure_logging(
base_config: dict | None = None,
*,
enable_console: bool = True,
enable_file: bool = True,
enable_rich: bool = True,
) -> dict:
"""Configure logging using the merged configuration."""
configs = [base_config or BASE]
if enable_console:
configs.append(CONSOLE)
if enable_file:
configs.append(JSON_FILE)
if enable_rich:
configs.append(RICH)
final_config = dict(reduce(merge, configs))
logging.config.dictConfig(final_config)
return final_config
```
## Using dictConfig
Use `logging.config.dictConfig` when configuration should be centralized, data-driven, or richer than `basicConfig`.
1. Define one `LOGGING` dictionary in a startup-oriented module such as `logging_config.py`.
2. Include `version: 1` and usually set `disable_existing_loggers: False` so existing named loggers are not silently disabled.
3. Define formatters, then handlers, then logger routing with `root` and optional named `loggers`.
4. Call `logging.config.dictConfig(LOGGING)` once during application startup.
5. Keep application logging calls unchanged when adding new destinations or formats.
## Application Usage
Concrete examples of how logging should be configured and used.
!!! warning "It's important to avoid the obvious name of `logging.py` to avoid weird clashes with IDEs and python internals."
=== "dictConfig"
```python title="logging_config.py"
import logging.config
LOGGING = ...
def configure_logging() -> None:
logging.config.dictConfig(LOGGING)
```
=== "basicConfig"
```python title="logging_config.py"
import logging.config
LOGGING = ...
def configure_logging() -> None:
logging.basicConfig(**LOGGING)
```
```python title="app.py"
import logging
logger = logging.getLogger(__name__)
def run(count: int) -> None:
logger.info("Processing %s items", count)
```
```python title="main.py"
from app import run
from logging_config import configure_logging
configure_logging()
run(5)
```
## Branching Guidance
- If the code is a tiny script: use `basicConfig` once near the entry point and module loggers elsewhere.
- If the code is a library: remove handlers and configuration calls; document logger names and optionally add `NullHandler` at the package root.
- If structured logs are required: keep the same logger and handler topology, but switch formatter output to JSON or a structured formatter.
- If console and file output are needed: add one file or rotating-file handler and attach it centrally. For a queue-backed JSON file setup, use the [JSON file logging pattern](./references/json-file-logging.md).
- If multiple processes write to one file: use a queue/listener or process-safe collection path rather than opening the same file independently in each process.
- If logs must cross a network: send records to a receiver or collector from a queue-backed handler, keep the receiver responsible for final destinations, and avoid exposing unauthenticated logging ports.
- If a framework logger is noisy: add a named logger override with a level and leave unrelated logger propagation alone.
## Completion Checks
1. Modules use `logging.getLogger(__name__)`.
2. Application startup configures logging once.
3. Libraries do not configure application handlers.
4. Levels match the severity semantics in this skill.
5. Logs include enough context to identify source, severity, and event without leaking secrets.
6. Expected destinations receive messages and suppressed levels stay quiet.
7. No source file or package is named `logging.py`.
@@ -0,0 +1,131 @@
# HTTPX Logging Handler Example
Use this reference when an application should emit JSON logs to an HTTP collector while keeping startup logging configuration declarative.
This page follows the top-level skill pattern:
- define one `LOGGING` dictionary
- apply it once with `logging.config.dictConfig(LOGGING)`
- keep modules focused on logger calls
Source docs to keep nearby:
- [HTTPX clients](https://www.python-httpx.org/advanced/clients/)
- [HTTPX timeouts](https://www.python-httpx.org/advanced/timeouts/)
- [`logging.config.dictConfig`](https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig)
## Minimal Topology
```text
application code -> named logger -> HttpxJsonLogHandler -> HTTP collector
```
## Reusable Handler Type
Keep transport behavior in one handler class and wire it declaratively through `dictConfig`.
```python title="httpx_json_handler.py"
import logging
import httpx
class HttpxJsonLogHandler(logging.Handler):
def __init__(self, collector_url: str, timeout_seconds: float = 2.0, token: str | None = None) -> None:
super().__init__()
headers = {"content-type": "application/json"}
if token is not None:
headers["authorization"] = f"Bearer {token}"
timeout = httpx.Timeout(timeout_seconds)
self.client = httpx.Client(base_url=collector_url, headers=headers, timeout=timeout)
def emit(self, record: logging.LogRecord) -> None:
payload = {
"name": record.name,
"levelname": record.levelname,
"levelno": record.levelno,
"pathname": record.pathname,
"lineno": record.lineno,
"funcName": record.funcName,
"created": record.created,
"message": record.getMessage(),
}
try:
response = self.client.post("/logs", json=payload)
response.raise_for_status()
except httpx.HTTPError:
self.handleError(record)
def close(self) -> None:
self.client.close()
super().close()
```
## Application Logging Configuration (Declarative)
```python title="logging_config.py"
import logging.config
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"handlers": {
"httpx": {
"class": "httpx_json_handler.HttpxJsonLogHandler",
"collector_url": "http://127.0.0.1:9021",
"timeout_seconds": 2.0,
"token": None,
},
"console": {
"class": "logging.StreamHandler",
"level": "INFO",
"stream": "ext://sys.stdout",
},
},
"root": {
"level": "INFO",
"handlers": ["httpx", "console"],
},
}
def configure_logging() -> None:
logging.config.dictConfig(LOGGING)
```
```python title="feature.py"
import logging
logger = logging.getLogger(__name__)
def sync_customer(customer_id: str) -> None:
logger.info("Syncing customer %s", customer_id)
```
```python title="main.py"
from feature import sync_customer
from logging_config import configure_logging
configure_logging()
sync_customer("C-101")
```
## Collector-Side Configuration (Declarative)
Whether you use an internal HTTP endpoint or a managed collector, keep receiver-side formatting and routing declared on the receiver side, not in application modules.
## Why This Pattern
- Logging wiring is declared once and applied once.
- Runtime behavior changes by editing config fields, not scattered root mutations.
- Feature modules stay independent from transport details.
- HTTP connection details remain encapsulated in one handler type.
## Review Checklist
1. Is there one `LOGGING` dict for the application process?
2. Is `dictConfig` called once at startup?
3. Are module loggers created via `logging.getLogger(__name__)`?
4. Are HTTP endpoint, timeout, and auth token inputs declared in handler config?
5. Are final routing/retention decisions handled by the collector side?
@@ -0,0 +1,176 @@
# JSON File Logging Pattern (Queue + Rotation)
Use this reference when you need machine-readable JSON logs written to rotating files without blocking caller threads.
This page captures the pattern used in the logging notebook example: configure a queue-backed root logger, route queued records to a rotating JSON file handler, and explicitly start and stop the `QueueListener` around workload execution.
## Pattern Overview
Use this topology:
```text
application code -> named logger/root logger -> QueueHandler -> QueueListener -> RotatingFileHandler(JSON)
```
Why this shape:
- `QueueHandler` keeps file I/O off the main execution path. See [Dealing with handlers that block](https://docs.python.org/3/howto/logging-cookbook.html#dealing-with-handlers-that-block).
- `RotatingFileHandler` bounds disk usage and preserves recent history in backups. See [RotatingFileHandler](https://docs.python.org/3/library/logging.handlers.html#rotatingfilehandler).
- A JSON formatter makes logs easy to parse for automation and analytics. See [python-json-logger](https://nhairs.github.io/python-json-logger/latest/).
## Configuration Example
```python title="logging_config.py"
import logging
import logging.config
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"console": {
"format": "%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s",
"datefmt": "%Y-%m-%d %H:%M:%S",
},
"json": {
"()": "pythonjsonlogger.json.JsonFormatter",
"format": "pathname,lineno,taskName,created,name,levelname,message,args",
"style": ",",
"rename_fields": {"levelname": "level"},
},
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "console",
"level": "INFO",
},
"queue": {
"class": "logging.handlers.QueueHandler",
"handlers": ["file"],
},
"file": {
"class": "logging.handlers.RotatingFileHandler",
"filename": "app.log",
"maxBytes": 1024**2 * 5,
"backupCount": 5,
"formatter": "json",
},
},
"root": {"level": "DEBUG", "handlers": ["queue", "console"]},
}
logging.config.dictConfig(LOGGING)
```
Notes:
- Queue/listener configuration through `dictConfig` is documented in [Configuring QueueHandler and QueueListener](https://docs.python.org/3/library/logging.config.html#configuring-queuehandler-and-queuelistener).
- `disable_existing_loggers: False` is usually safer unless you intentionally want to disable existing non-root loggers.
## Listener Lifecycle Pattern
When using queue-backed logging, treat listener startup and shutdown as explicit lifecycle responsibilities.
```python title="listener_lifecycle.py"
import logging
import warnings
from collections.abc import Callable
from contextlib import contextmanager
from functools import cache
from logging.handlers import QueueHandler, QueueListener
@cache
def get_listener(queue_handler_name: str) -> QueueListener | None:
match logging.getHandlerByName(queue_handler_name):
case QueueHandler(listener=QueueListener() as listener):
return listener
def _listener_action(queue_handler_name: str, action: Callable[[QueueListener], None]):
match get_listener(queue_handler_name):
case QueueListener() as listener:
action(listener)
return listener
def start_listener(queue_handler_name: str) -> None:
listener = _listener_action(queue_handler_name, lambda listener: listener.start())
if listener is None:
warnings.warn(f"{queue_handler_name} is not set up correctly", stacklevel=2)
return
def stop_listener(queue_handler_name: str) -> None:
_listener_action(queue_handler_name, lambda listener: listener.stop())
@contextmanager
def listener_lifespan(queue_handler_name: str):
start_listener(queue_handler_name)
try:
yield
finally:
stop_listener(queue_handler_name)
with listener_lifespan("queue"):
logging.info("Started")
for _ in range(10**6):
logging.debug("Hello world")
logging.info("Done")
logging.info("Console only")
```
This demonstrates deterministic listener startup/shutdown around the active workload
Docs for APIs used above:
- [`logging.getHandlerByName`](https://docs.python.org/3/library/logging.html#logging.getHandlerByName)
- [`QueueHandler`](https://docs.python.org/3/library/logging.handlers.html#queuehandler)
- [`QueueListener`](https://docs.python.org/3/library/logging.handlers.html#queuelistener)
- [`contextlib.contextmanager`](https://docs.python.org/3/library/contextlib.html#contextlib.contextmanager)
- [`functools.cache`](https://docs.python.org/3/library/functools.html#functools.cache)
## Reading JSON Logs Back
For quick validation, read recent lines and deserialize JSON:
```python title="inspect_logs.py"
import json
from collections import deque
from pathlib import Path
def read_last_n_lines(file: str | Path, *, n: int):
with Path(file).open("r") as f:
return deque(f, maxlen=n)
lines = read_last_n_lines("app.log", n=5)
records = list(map(json.loads, lines))
```
For rotated logs, enumerate files by basename and sort by modification time before reading.
## Practical Checks
Before calling this done:
1. Confirm listener startup and shutdown run for the workload lifecycle.
2. Confirm `app.log` receives JSON lines, not plain text.
3. Confirm rotation occurs at the expected size and backup count.
4. Confirm console output still appears at the desired level.
5. Confirm exceptions and key context fields are preserved in JSON output.
## Source Links
- [Logging Cookbook](https://docs.python.org/3/howto/logging-cookbook.html)
- [logging.config reference](https://docs.python.org/3/library/logging.config.html)
- [Configuring QueueHandler and QueueListener](https://docs.python.org/3/library/logging.config.html#configuring-queuehandler-and-queuelistener)
- [logging handlers reference](https://docs.python.org/3/library/logging.handlers.html)
- [LogRecord attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes)
- [python-json-logger docs](https://nhairs.github.io/python-json-logger/latest/)
@@ -0,0 +1,199 @@
# Network Logging Minimal Example
Use this reference when an application should send logs over TCP to a local receiver and you want a complete, working baseline.
This page shows how the pieces fit together end to end:
- application code logs with named loggers
- startup applies one declarative `LOGGING` config
- `SocketHandler` sends records to a receiver
- receiver uses `socketserver` and local logging config for final routing
Source docs to keep nearby:
- [Sending and receiving logging events across a network](https://docs.python.org/3/howto/logging-cookbook.html#sending-and-receiving-logging-events-across-a-network)
- [`SocketHandler`](https://docs.python.org/3/library/logging.handlers.html#sockethandler)
- [`socketserver`](https://docs.python.org/3/library/socketserver.html)
- [`logging.makeLogRecord`](https://docs.python.org/3/library/logging.html#logging.makeLogRecord)
## Minimal Topology
```text
app module -> logger -> SocketHandler -> TCP receiver -> local handlers
```
## 1) Client Logging Config (Declarative)
```python title="logging_config.py"
import logging.config
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"console": {
"format": "%(asctime)s %(levelname)s %(name)s %(message)s",
"datefmt": "%Y-%m-%d %H:%M:%S",
}
},
"handlers": {
"network": {
"class": "logging.handlers.SocketHandler",
"host": "127.0.0.1",
"port": 9020,
},
"console": {
"class": "logging.StreamHandler",
"formatter": "console",
"level": "INFO",
"stream": "ext://sys.stdout",
},
},
"root": {
"level": "INFO",
"handlers": ["network", "console"],
},
}
def configure_logging() -> None:
logging.config.dictConfig(LOGGING)
```
```python title="feature.py"
import logging
logger = logging.getLogger(__name__)
def process_order(order_id: str) -> None:
logger.info("Processing order %s", order_id)
```
```python title="main.py"
from feature import process_order
from logging_config import configure_logging
def main() -> None:
configure_logging()
process_order("A-42")
if __name__ == "__main__":
main()
```
## 2) Receiver Logging Config (Declarative)
```python title="receiver_logging_config.py"
import logging.config
LOGGING = {
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"console": {
"format": "%(asctime)s %(levelname)s %(name)s %(message)s",
"datefmt": "%Y-%m-%d %H:%M:%S",
}
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "console",
"stream": "ext://sys.stdout",
}
},
"root": {"level": "INFO", "handlers": ["console"]},
}
def configure_receiver_logging() -> None:
logging.config.dictConfig(LOGGING)
```
## 3) Cookbook Receiver (`socketserver`) Implementation
This receiver follows the same structure as the Python logging cookbook example.
`SocketHandler` sends:
- a 4-byte big-endian length prefix
- a pickle payload containing a `LogRecord` dictionary
```python title="log_receiver.py"
import logging
import pickle
import socketserver
import struct
from receiver_logging_config import configure_receiver_logging
class LogRecordStreamHandler(socketserver.StreamRequestHandler):
def handle(self) -> None:
while True:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
payload_len = struct.unpack(">L", chunk)[0]
payload = self.connection.recv(payload_len)
while len(payload) < payload_len:
payload = payload + self.connection.recv(payload_len - len(payload))
try:
record_dict = pickle.loads(payload)
record = logging.makeLogRecord(record_dict)
except Exception:
logging.getLogger(__name__).exception("Dropped malformed log record")
continue
self.handle_log_record(record)
def handle_log_record(self, record: logging.LogRecord) -> None:
logger = logging.getLogger(record.name)
if logger.isEnabledFor(record.levelno):
logger.handle(record)
class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
allow_reuse_address = True
def main() -> None:
configure_receiver_logging()
with LogRecordSocketReceiver(("127.0.0.1", 9020), LogRecordStreamHandler) as server:
logging.getLogger(__name__).info("Receiver listening on 127.0.0.1:9020")
server.serve_forever()
if __name__ == "__main__":
main()
```
## 4) How It Fits Together In Practice
1. Start `log_receiver.py`.
2. Start `main.py` from the client app.
3. Client logs go to console and TCP.
4. Receiver reconstructs records and emits them through its own handlers.
This split keeps app emission and receiver routing independent while still being fully runnable.
## Important Security Note
`SocketHandler` uses pickle serialization. Treat this as trusted-network-only transport.
- Bind receiver to localhost or a trusted private network.
- Do not expose this receiver to untrusted clients.
- For hostile boundaries, use JSON/TLS with authenticated ingestion instead of raw pickle.
## Review Checklist
1. Is there one `LOGGING` dict per process role (client and receiver)?
2. Is `dictConfig` called once at each process startup?
3. Does the receiver decode length-prefixed payloads correctly?
4. Do modules only use `logging.getLogger(__name__)`?
5. Is the receiver endpoint protected by trust boundaries?
@@ -1,6 +1,6 @@
# Python Logging References
# Python Logging Source References
Use these official Python docs when applying this skill.
Use these official Python docs when applying the Python logging skill.
## Core Documentation
@@ -10,13 +10,15 @@ Use these official Python docs when applying this skill.
- [logging API reference](https://docs.python.org/3/library/logging.html)
- [logging.config reference](https://docs.python.org/3/library/logging.config.html)
## dictConfig-Specific
## Configuration And dictConfig
!!! info "dictConfig references"
- [Dictionary schema details](https://docs.python.org/3/library/logging.config.html#logging-config-dictschema) for `version`, formatters, handlers, loggers, and root.
- [`logging.config.dictConfig`](https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig) function reference.
## Practical Notes
- Prefer app-level centralized config with one startup call to `dictConfig`.
- In modules, use `logging.getLogger(__name__)`.
- Avoid calling `basicConfig` in libraries or scattered modules.
- Prefer module loggers created with `logging.getLogger(__name__)`.
- Let applications configure handlers and formatters; libraries should emit logs without taking over routing.
- Use `basicConfig` for simple scripts and `dictConfig` for centralized application configuration.
- Explicitly set `disable_existing_loggers: False` in `dictConfig` unless disabling existing non-root loggers is intentional.
- Use queue-based handlers when slow handlers would block async, threaded, or high-volume code paths.
+94
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@@ -0,0 +1,94 @@
---
name: python-typing
description: "Reference-first skill for reviewing and modernizing Python typing to the newest supported best practices. Use when auditing annotations, replacing legacy typing syntax, and enforcing latest-syntax-first conventions."
---
# Modern Python Typing Review Reference
Use this skill to enforce a latest-syntax-first typing standard grounded in current Python language guidance.
Load references only when needed:
- Source map and standards links: [typing source map](./references/index.md)
- Practical review workflow and quality gates: [typing review workflow](./references/review-workflow.md)
- Astral ty adoption and operation guidance: [Astral ty usage reference](./references/astral-ty.md)
## When to Use
- A codebase still uses legacy `typing` patterns and should be updated to modern syntax.
- You need a repeatable process for type-focused code review across a package or module.
- You want references to official Python docs and PEPs attached to recommendations.
- You need to decide whether a modern feature is allowed under the project Python version.
## How To Use This Skill
1. Confirm the effective Python baseline from project config (for example `pyproject.toml` and lint target version).
2. Scan target files for legacy patterns and prioritize newest canonical syntax first.
3. Apply modern typing upgrades aggressively, keeping runtime behavior stable unless explicitly requested otherwise.
4. Validate with project lint and diagnostics.
5. Report what changed and list only hard-blocker deferrals (for example incompatible Python baseline).
## Intent Router
- Baseline and compatibility checks: [typing source map](./references/index.md)
- Exact modernization sequence and branching logic: [typing review workflow](./references/review-workflow.md)
- Integrating or tuning Astral ty: [Astral ty usage reference](./references/astral-ty.md)
- Need official rationale for a specific feature: [typing source map](./references/index.md)
## Load Order
1. Start with [typing source map](./references/index.md) for authoritative links.
2. Load [typing review workflow](./references/review-workflow.md) to execute the review.
3. Load [Astral ty usage reference](./references/astral-ty.md) when the workflow includes `ty` setup, configuration, migration, or editor integration.
4. Return to source links for any feature-level recommendation included in the final output.
## Load Budget
1. Default: load one reference (`index.md`) for lightweight guidance.
2. Standard review: load two references (`index.md` and `review-workflow.md`).
3. Add `astral-ty.md` only when `ty` is in scope.
4. Do not load additional docs unless a project-specific edge case requires it.
## Decision Baseline
Use these defaults unless a hard compatibility constraint prevents them:
1. Prefer built-in generics (`list[str]`, `dict[str, int]`) over `typing.List` and `typing.Dict`.
2. Prefer `X | Y` over `typing.Optional[X]` or `typing.Union[X, Y]`.
3. Prefer PEP 695 generics (`class Box[T]`, `def fn[T](...)`) for Python 3.12+ codebases and use them by default.
4. Prefer `typing.Self` for fluent instance/class method return typing.
5. Use `typing.Literal` when a finite value set is the real contract.
6. Remove legacy typing aliases and module-level `TypeVar` declarations when PEP 695 can replace them.
7. Keep runtime behavior unchanged unless the task explicitly requests behavior refactors.
8. Treat `typing.cast(...)` as a last resort, not a default fix for type-checker complaints.
9. Before adding a cast, prefer real narrowing (`isinstance`, `TypeIs`/`TypeGuard`), explicit control-flow checks, or small annotation refactors that preserve behavior.
10. Reject casts whose only purpose is to silence the checker without a clear runtime invariant.
11. For closed variant sets (`Literal`/`Enum`/tagged unions), prefer structural pattern matching with exhaustiveness checks (`assert_never`) for deterministic narrowing.
## Cast Discipline
Use this policy whenever a modernization pass encounters a potential cast:
1. Confirm whether the checker can be satisfied with stronger narrowing first (for example `isinstance` or assertion-based narrowing).
2. If a cast is still necessary, keep it narrowly scoped to the exact expression rather than widening an entire variable flow.
3. Document the invariant that makes the cast valid in human terms, not just "type checker requires this".
4. Prefer fixing imprecise annotations at the source over stacking repeated casts downstream.
5. If multiple casts appear in one code path, treat that as a design smell and propose a structural typing fix.
## Completion Checks
1. Modern syntax aligns with the project Python baseline.
2. Linting and diagnostics are clean for edited files.
3. Public APIs are unchanged unless explicitly requested.
4. Feature-level recommendations include source links.
5. Any deferral is backed by a specific hard constraint (for example Python version floor).
6. New casts, if any, are minimal, justified by an explicit invariant, and not used as checker-silencing shortcuts.
## Output Contract
Return:
1. Files reviewed and files changed.
2. Applied typing upgrades with brief rationale.
3. Deferred upgrades only when blocked by explicit hard constraints.
4. Validation results (lint/tests/diagnostics).
5. References consulted and discovery path used.
@@ -0,0 +1,59 @@
# Astral ty Usage Reference
Use this page when you want to run or adopt [ty](https://docs.astral.sh/ty/), Astral's Python type checker and language server, in a typing-focused workflow.
## Quick Start
- Run a one-off check without installing globally: `uvx ty check`
- Run checks in the current project: `ty check`
- Explore behavior quickly in the [ty playground](https://play.ty.dev/)
Primary docs:
- [Getting started](https://docs.astral.sh/ty/#getting-started)
- [Installation](https://docs.astral.sh/ty/installation/)
- [Type checking](https://docs.astral.sh/ty/type-checking/)
- [CLI reference](https://docs.astral.sh/ty/reference/cli/)
## Editor Integration
Use ty as a language server in supported editors.
- [Editor integration overview](https://docs.astral.sh/ty/editors/)
- [VS Code setup](https://docs.astral.sh/ty/editors/#vs-code)
- [Language server capabilities](https://docs.astral.sh/ty/features/language-server/)
- [Editor settings reference](https://docs.astral.sh/ty/reference/editor-settings/)
## Configuration Surface
Start from project defaults, then add targeted overrides only where needed.
- [Configuration guide](https://docs.astral.sh/ty/configuration/)
- [Configuration reference](https://docs.astral.sh/ty/reference/configuration/)
- [Python version handling](https://docs.astral.sh/ty/python-version/)
- [Module discovery](https://docs.astral.sh/ty/modules/)
- [File exclusions](https://docs.astral.sh/ty/exclusions/)
## Rule And Suppression Controls
Use this set when tuning signal-to-noise in large or partially typed codebases.
- [Rules overview](https://docs.astral.sh/ty/rules/)
- [Rules reference](https://docs.astral.sh/ty/reference/rules/)
- [Suppression comments and directives](https://docs.astral.sh/ty/suppression/)
- [Diagnostics feature docs](https://docs.astral.sh/ty/features/diagnostics/)
## Migration Notes
For teams moving from existing type checkers, use Astral's migration guidance first.
- [Coming from mypy or pyright](https://docs.astral.sh/ty/coming-from-mypy-or-pyright/)
- [Typing FAQ](https://docs.astral.sh/ty/reference/typing-faq)
## Suggested Review Flow With ty
1. Confirm Python baseline and project targets.
2. Run `uvx ty check` for an initial signal pass.
3. Configure version/module/discovery settings as needed.
4. Triage diagnostics and tune rules or suppressions deliberately.
5. Re-run checks and keep modernization changes behavior-preserving unless explicitly requested.
@@ -0,0 +1,40 @@
# Python Typing Source Map
Use this page as the canonical source index when making typing modernization recommendations.
## Core Language and Library Docs
- [Typing module documentation](https://docs.python.org/3/library/typing.html)
- [Typing specification (typing.python.org)](https://typing.python.org/)
- [Built-in types and generic aliases](https://docs.python.org/3/library/stdtypes.html)
- [Python language reference: `match` statement](https://docs.python.org/3/reference/compound_stmts.html#the-match-statement)
- [PEP 634: Structural Pattern Matching specification](https://peps.python.org/pep-0634/)
- [typing.cast reference (runtime no-op)](https://docs.python.org/3/library/typing.html#typing.cast)
- [Typing spec directives for `cast()`](https://typing.python.org/en/latest/spec/directives.html#cast)
- [Mypy type narrowing and casts guidance](https://mypy.readthedocs.io/en/stable/type_narrowing.html#casts)
- [Typing guide: exhaustiveness and `assert_never`](https://typing.python.org/en/latest/guides/unreachable.html#assert-never-and-exhaustiveness-checking)
- [Mypy: `Literal`/`Enum` exhaustiveness with `match`](https://mypy.readthedocs.io/en/stable/literal_types.html#exhaustiveness-checking)
## Tooling References
- [Astral ty documentation](https://docs.astral.sh/ty/)
- [Astral ty usage reference (this skill)](./astral-ty.md)
## Modernization PEPs
- [PEP 585: Type Hinting Generics In Standard Collections](https://peps.python.org/pep-0585/)
- [PEP 604: Allow writing union types as `X | Y`](https://peps.python.org/pep-0604/)
- [PEP 673: Self Type](https://peps.python.org/pep-0673/)
- [PEP 695: Type Parameter Syntax](https://peps.python.org/pep-0695/)
## Advanced Typing PEPs (Load on Demand)
- [PEP 612: Parameter Specification Variables](https://peps.python.org/pep-0612/)
- [PEP 646: Variadic Generics](https://peps.python.org/pep-0646/)
- [PEP 647: User-Defined Type Guards](https://peps.python.org/pep-0647/)
- [PEP 655: Required and NotRequired for TypedDict](https://peps.python.org/pep-0655/)
- [PEP 742: Narrowing types with TypeIs](https://peps.python.org/pep-0742/)
## Version Gate Reminder
Before recommending syntax upgrades, verify the project's supported Python range and lint target so recommendations match runtime constraints.
@@ -0,0 +1,85 @@
# Typing Review Workflow
This workflow is distilled from practical typing modernization passes and is designed for latest-syntax-first upgrades.
## Step-by-Step Process
1. Identify the Python baseline from project config (`requires-python`, lint target version, toolchain constraints).
2. Scan target files for legacy typing patterns and repeated opportunities.
3. Apply highest-value modern syntax updates first:
- `typing.List`/`typing.Dict` -> built-in generics.
- `Optional[T]`/`Union[A, B]` -> `T | None` / `A | B`.
4. Upgrade generic declarations to PEP 695 syntax where baseline allows:
- `TypeVar` module globals -> local type parameters in classes/functions.
5. Tighten domain contracts where clear:
- replace unconstrained `str` with `Literal[...]` for finite known values.
- use `Self` for fluent APIs.
6. Keep edits minimal and avoid behavior changes unless requested.
7. Validate with lint and editor diagnostics.
8. Report applied changes, hard-blocker deferrals, and sources consulted.
## Decision Points and Branching
- If Python baseline is below 3.12:
- use the newest syntax available under that baseline, and document exactly what blocked PEP 695.
- If a legacy annotation is public API and downstream tooling compatibility is unknown:
- still modernize syntax unless there is a confirmed breakage risk with a named downstream constraint.
- If replacing `TypeVar` with PEP 695 affects readability debates only:
- still prefer PEP 695; readability preference alone is not a blocker.
- If a stricter type (for example `Literal`) may reject existing runtime inputs:
- apply only when the input contract is already finite; otherwise defer with a contract-change note.
## Deterministic Narrowing with `match`
Use structural pattern matching when the domain is a closed set (for example tagged unions, enum dispatch, or finite literal variants).
1. Prefer `match` over long `if`/`elif` ladders when each branch represents a distinct variant.
2. For tagged unions, match the discriminant and extract payload fields in the same case.
3. Add a default `case _:` branch with `assert_never(...)` to enforce exhaustiveness in static analysis.
4. Keep patterns explicit and side-effect-light; avoid relying on bindings from failed matches.
Example with a tagged `TypedDict` union:
```python
from typing import Literal, TypedDict, assert_never
class NewJobEvent(TypedDict):
tag: Literal["new-job"]
job_name: str
class CancelJobEvent(TypedDict):
tag: Literal["cancel-job"]
job_id: int
type Event = NewJobEvent | CancelJobEvent
def route(event: Event) -> str:
match event:
case {"tag": "new-job", "job_name": job_name}:
return f"enqueue:{job_name}"
case {"tag": "cancel-job", "job_id": job_id}:
return f"cancel:{job_id}"
case _:
assert_never(event)
```
This pattern makes narrowing deterministic per branch and surfaces missing variants as type-checker errors during review.
## Quality Criteria
1. All edits are syntax-valid for the target Python versions.
2. Lint and diagnostics pass for edited files.
3. Runtime behavior is unchanged for modernization-only tasks.
4. Recommendations cite authoritative sources.
5. Output clearly separates "changed now" from hard-blocked follow-up items.
## Suggested Validation Commands
- `uv run ruff check <paths>`
- `uv run pytest -q` (or targeted tests where available)
Use repository-preferred test invocation conventions when they differ.
@@ -1,19 +1,6 @@
---
name: ruff-linting-formating
description: "Reference-first Ruff skill for repository preferences, baseline defaults, and source links. Use to pick consistent Ruff conventions and integration references, not to run migration playbooks."
argument-hint: "Which Ruff preferences or integrations are you deciding (rules, formatting, pre-commit, GitHub Actions)?"
x-personal-mcp:
id: ruff-linting-formating
version: 1.0.0
tags:
- ruff
- linting
- formatting
- python
- ci
capabilities:
- resource://skills/ruff-linting-formating/document
depends_on: []
---
# Ruff Preferences and References
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@@ -1,21 +1,6 @@
---
name: vscode-configuration
description: 'Create and troubleshoot VS Code workspace configuration for Python projects, with focused patterns for launch.json debugpy/FastAPI debugging and tasks.json task automation.'
argument-hint: 'What do you need: debug setup, FastAPI debug run profile, tasks.json automation, or all of them?'
x-personal-mcp:
id: vscode-configuration
version: 1.0.0
tags:
- vscode
- launch-json
- tasks-json
- debugpy
- fastapi
- python
- skills
capabilities:
- resource://skills/vscode-configuration/document
depends_on: []
---
# VS Code Configuration
@@ -32,7 +32,7 @@ Use [`.vscode/tasks.json`](https://code.visualstudio.com/docs/editor/tasks) to d
"label": "App: Run",
"type": "shell",
"command": "uv",
"args": ["run", "uvicorn", "personal_mcp.main:app", "--host", "127.0.0.1", "--port", "8000", "--reload"],
"args": ["run", "uvicorn", "personal_mcp.main:create_app", "--factory", "--host", "127.0.0.1", "--port", "8000", "--reload"],
"options": {
"cwd": "${workspaceFolder}"
},
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@@ -1,25 +1,6 @@
---
name: zensical-docs
description: 'Reference skill for Zensical documentation mechanics. Use for quick lookup of docs structure, feature options, and source links. Prefer inline Markdown links to source docs and avoid bare URLs because this content is rendered as human docs and MCP resources.'
argument-hint: 'What are you documenting, who is the audience, and what Zensical features are in scope?'
x-personal-mcp:
id: zensical-docs
version: 1.0.0
tags:
- zensical
- mkdocs
- mkdocs-material
- mkdocstrings
- docs
- documentation
- information-architecture
- skills
- bootstrap
- discovery
- authoring
capabilities:
- resource://skills/zensical-docs/document
depends_on: []
---
# Zensical Documentation Authoring
+98
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@@ -0,0 +1,98 @@
---
icon: lucide/flask-conical
---
# Testing
This page describes the current test layout and execution model for this repository.
Primary guidance sources:
- [Pytest scaffolding skill](./skills/pytesting/SKILL.md)
- [Pytest docs reference](./skills/pytesting/references/pytest-docs.md)
- [FastAPI + uv + Docker skill](./skills/fastapi-uv-docker/SKILL.md)
## Goals
1. Keep local feedback fast with deterministic tests.
2. Mirror source modules with focused test groups.
3. Keep endpoint and MCP surface checks explicit.
4. Make marker usage strict and intentional.
## Current Test Layout
Current tree:
```text
tests/
__init__.py
conftest.py
registry/
test_read.py
ingest/
conftest.py
test_current_docs.py
test_document.py
test_prompt.py
models/
test_document_validation.py
test_prompt_validation.py
test_registry_payload_models.py
skills/
test_provider.py
web/
conftest.py
test_endpoint_connections.py
test_mcp_prompts.py
test_mcp_skills.py
```
Source-to-test alignment today:
- `src/personal_mcp/registry/ingest/` -> `tests/registry/ingest/`
- `src/personal_mcp/registry/models/` -> `tests/registry/models/`
- `src/personal_mcp/skills/provider.py` -> `tests/skills/test_provider.py`
- `src/personal_mcp/web/` and MCP HTTP surface -> `tests/web/`
## Markers And Strictness
Configured markers in `pyproject.toml`:
- `unit`: fast deterministic tests with no external dependencies
- `integration`: framework or component integration tests
- `smoke`: thin critical-path checks
Pytest runs with `--strict-markers`, so any unregistered marker fails the test run.
## Fixture Layering
Fixture placement follows test scope:
1. `tests/conftest.py` for cross-suite defaults.
2. `tests/registry/ingest/conftest.py` for ingest-specific setup.
3. `tests/web/conftest.py` for web and endpoint client setup.
Prefer adding fixtures at the narrowest scope that serves more than one test.
## Command Baseline
Canonical invocation:
```bash
uv run pytest
```
Useful filtered runs:
```bash
uv run pytest --collect-only -q
uv run pytest -m unit -q
uv run pytest -m integration -q
uv run pytest -m smoke -q
```
## Adding New Tests
When adding coverage:
1. Place tests under the nearest existing module subtree (`registry/`, `skills/`, or `web/`).
2. Mirror the source path where practical.
3. Reuse existing `conftest.py` files before adding new fixture layers.
4. Add markers only when they convey execution intent, and register new markers in `pyproject.toml` first.
This keeps the suite aligned with the current architecture while preserving a fast local test loop.
-118
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@@ -1,118 +0,0 @@
---
icon: lucide/link
---
# URI Contract
This page defines the canonical resource URI contract, template parameter rules, and compatibility policy.
## Canonical URI Surface
The public, preferred URIs are:
1. `resource://catalog/skills_index`
2. `resource://catalog/skills/{skill_id}`
3. `resource://skills/{skill_id}/document`
4. `resource://skills/{skill_id}/references/{ref_id}`
5. `resource://docs/{path*}`
Contract intent:
1. Catalog URIs are discovery surfaces.
2. Skill URIs are the primary per-skill guidance surfaces.
3. The docs wildcard URI is a direct authored-markdown access surface under `docs/`.
## URI Semantics
### `resource://catalog/skills_index`
1. Returns a compact list of skill records for discovery.
2. Contains one entry per `skill_id`.
3. Includes enough metadata for client-side selection, at minimum `id`, `name`, `description`, `tags`, and `capabilities`.
### `resource://catalog/skills/{skill_id}`
1. Returns one normalized record for `skill_id`.
2. Includes the canonical document URI and declared reference ids.
3. Returns not found when `skill_id` does not exist.
### `resource://skills/{skill_id}/document`
1. Returns the canonical `SKILL.md` authored content for that skill.
2. `skill_id` must satisfy the stable skill id rules from the content contract.
### `resource://skills/{skill_id}/references/{ref_id}`
1. Returns one reference document declared in the skill frontmatter references manifest.
2. `ref_id` is the stable public handle for that reference document.
### `resource://docs/{path*}`
1. Returns authored markdown at a normalized relative path under `docs/`.
2. Supports nested paths via RFC6570 wildcard expansion.
3. Typical examples include `index.md`, `usage.md`, `skills/<skill-id>/SKILL.md`, and `skills/<skill-id>/references/<file>.md`.
## Template Parameter And Validation Rules
### `skill_id`
1. Lowercase kebab-case.
2. Must satisfy the stable skill id rules from the content contract.
### `ref_id`
1. Lowercase kebab-case.
2. Must be declared in the skill's references manifest.
### `path*`
1. Relative POSIX path only.
2. No leading slash.
3. No `..` traversal segments.
4. Resolves only inside `docs/`.
5. Markdown-only in the end state, meaning `.md` files.
## URI Versioning Policy
Default rule:
1. Keep URIs unversioned by default.
2. Allow URI and payload updates when they improve clarity or implementation simplicity.
Breaking-change rule:
1. Breaking changes use direct replacement of the canonical URI family.
2. No compatibility aliases or dual URI families are maintained.
FastMCP version metadata usage:
1. Resource `version` metadata may be used for implementation and version discovery.
2. URI readability and maintainability remain the primary contract.
## Reference Id Compatibility Policy
`ref_id` is the public identifier for a reference document, separate from file path.
Rules:
1. Prefer keeping `ref_id` stable when practical.
2. File paths may change without URI churn as long as the mapped `ref_id` still resolves.
3. If a reference is renamed, introduce a new `ref_id` and treat the old one as retired.
4. Avoid reusing retired `ref_id` values for unrelated content.
## Invariants
This contract guarantees:
1. One canonical URI pattern per core capability surface.
2. Fast, low-friction URI evolution through direct replacement of canonical URIs.
3. A single canonical catalog URI family with no alias maintenance overhead.
4. Reference mappings can evolve with minimal churn.
## Non-Goals
This contract does not define:
1. Implementation-specific transform wiring details, such as `VersionFilter`, mounts, or provider composition.
2. Migration script mechanics for auto-generating aliases.
3. Authorization policy design for URI-level access control.
+83 -220
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@@ -6,260 +6,123 @@ icon: lucide/workflow
## Purpose
This page explains practical usage mechanics for the GitHub Copilot extension in VS Code when `personal-mcp` is configured as an MCP server:
This page describes how clients discover and load `personal-mcp` skills published by the [FastMCP Skills Provider](https://gofastmcp.com/servers/providers/skills).
1. explicit `/` command flows when you want deterministic control
2. guided skill loading when relevance can be inferred
Skills are MCP resources. The client remains responsible for selecting guidance, loading only useful supporting material, and applying it to the current workspace.
The goal is to show how Copilot behaves as a client and how to shape that behavior.
## Published Skill Surface
## Mental Model
Each directory beneath `docs/skills/` publishes:
In Copilot Chat, there are two distinct mechanisms:
1. `skill://<name>/SKILL.md` for primary instructions
2. `skill://<name>/_manifest` for file discovery and integrity metadata
3. `skill://<name>/{path*}` for supporting files
1. `/` commands are user-invoked orchestration shortcuts.
2. MCP resources are server-published knowledge units that can be attached as read-only context, while MCP tools provide an execution path for discovery and retrieval.
The server uses `supporting_files="template"`. Main files and manifests appear in `resources/list`; supporting files stay behind per-skill wildcard templates so the resource list remains compact.
In this repository, skill guidance is exposed as MCP resources, not as server-owned prompt execution. Copilot remains the orchestrator.
The manifest contains every skill-relative path, byte size, and SHA256 hash. References do not have synthetic ids or a separate catalog record.
## Background Mechanics
Prompts remain available through prompt catalog resources, prompt document resources, and MCP prompt objects.
### What the server publishes
## Discovery Workflow
`personal-mcp` registers resources from the validated docs registry and exposes catalog discovery resources:
Use this bounded sequence:
1. `resource://catalog/skills_index`
2. `resource://catalog/skills/{skill_id}`
1. List resources or call FastMCP `list_skills()`.
2. Compare skill names and descriptions.
3. Read one selected `skill://<name>/SKILL.md`.
4. Read `skill://<name>/_manifest` only when supporting material may be useful.
5. Fetch the minimum supporting paths needed for the task.
6. Reconcile the guidance with the actual repository code before making changes.
Each skill publishes a canonical Markdown document resource:
Do not load every skill or every supporting file up front.
1. `resource://skills/<skill-id>/document`
2. `resource://skills/<skill-id>/references/<ref-id>`
## FastMCP Client Utilities
The document payload is loaded from `docs/skills/<skill-id>/SKILL.md` and returned with metadata.
FastMCP provides native utilities in `fastmcp.utilities.skills`:
### What Copilot does as the client
1. `list_skills(client)` discovers main skill resources.
2. `get_skill_manifest(client, name)` parses a generated manifest.
3. `download_skill(client, name, target_dir)` downloads one skill.
4. `sync_skills(client, target_dir)` downloads all advertised skills.
When connected to MCP, Copilot can do the following at runtime:
These utilities operate directly on the native `skill://` contract and require no repository-specific adapter.
1. interpret the current chat request
2. use attached MCP resources that you provide through the chat UI
3. invoke MCP tools when the task and tool descriptions make that relevant
4. summarize relevant sections into working context
5. apply guidance while generating edits or recommendations
## Tool-Only Clients
This behavior is shaped by the active chat surface, prompt or instruction guidance, and available MCP tools.
For reliable progressive discovery, use one of these sequences:
1. explicit resource path: attach a catalog resource first, then attach only selected skill documents
2. tool path: call catalog tools first, then load only selected skill documents
### What `/` commands do
`/` commands in VS Code are client-side prompt entry points (for example in prompt files). They do not replace MCP resources. In Copilot, they typically:
1. enforce a known sequence
2. collect missing inputs
3. call discovery/read steps in a predictable order
Think of `/` commands as orchestration shortcuts on top of MCP resources.
### What automatic loading means here
In this project, "automatic loading" should be read as a preference you express through instructions and prompts, not as a guaranteed VS Code feature that auto-attaches MCP resources.
In practice, there are two reliable ways to make skill content available in chat:
1. explicit resource attachment through `Add Context > MCP Resources` or `MCP: Browse Resources`
2. MCP tool invocation using `list_resources`/`read_resource` (ResourcesAsTools), with thin catalog tools as parity fallback
Instruction quality and metadata quality still matter, because they influence whether Copilot recognizes that the MCP server is relevant and chooses the tool path well.
## Operating Pattern
Use both modes intentionally in Copilot Chat.
### Mode A: Explicit `/` command
Use when you need predictable, repeatable behavior across teammates.
Good fits:
1. onboarding workflows
2. compliance-sensitive tasks
3. repetitive scaffolding
### Mode B: Guided skill loading
Use when requests are varied and you want lower friction during normal chat.
Good fits:
1. ad hoc implementation questions
2. mixed-topic debugging
3. architecture tradeoff discussions
### Mode C: Fallback flow
Start with guided loading in chat; escalate to a `/` command when:
1. confidence is low
2. multiple skills conflict
3. the user wants strict repeatability
## Suggested Resolution Flow
```mermaid
flowchart TD
A[User request in Copilot Chat] --> B{Deterministic workflow needed?}
B -- Yes --> C[/Run slash command/]
C --> D[Copilot fetches known catalog and skill resources]
B -- No --> E[Copilot uses attached resources or catalog tools]
E --> F{Confident skill match?}
F -- Yes --> G[Copilot fetches skill documents]
F -- No --> H[Ask clarifying question or suggest slash command]
D --> I[Apply guidance to task]
G --> I
H --> I
```
## Authoring Requirements For Reliable Matching
For resource selection or tool-based matching to work well, each skill should have:
1. precise `description`
2. focused `tags`
3. explicit `capabilities`
4. stable `id` and slug naming
Weak metadata reduces Copilot match quality and increases wrong context injection.
## Practical Guidelines
1. Keep `/` commands minimal and high-value.
2. Do not duplicate full methodology text inside command files.
3. Keep canonical guidance in `docs/skills/*/SKILL.md`.
4. In Copilot instructions, prefer catalog-first discovery before skill fetch.
5. Prefer small, relevant context slices over loading every skill.
6. Keep slash commands focused on deterministic orchestration, not content duplication.
If you skip the catalog/index step, behavior is less predictable and may either miss relevant skills or pull too much context.
## Optional Tool Search Mode
When tool catalogs grow, FastMCP search transforms can reduce tool-list noise for tool-only clients.
Runtime switches:
1. `PERSONAL_MCP_TOOL_SEARCH=none|regex|bm25` (default `none`)
2. `PERSONAL_MCP_TOOL_SEARCH_MAX_RESULTS=<positive int>` (default `5`)
Behavior:
1. `regex` uses deterministic regex matching for targeted queries.
2. `bm25` uses ranked natural-language matching.
3. `list_resources` and `read_resource` stay visible so resource-backed fallback remains primary.
## Copilot Instruction Pattern
If you want Copilot to use `personal-mcp` skill content more reliably, the instruction file should describe three things clearly:
1. when MCP-backed skill guidance is relevant
2. which retrieval path Copilot should prefer first
3. how much skill context it should load before answering
That matters because instructions can strongly steer discovery behavior, but they do not force VS Code to auto-attach MCP resources. A good instruction tells Copilot to prefer the canonical MCP content path while remaining accurate about the fallback path.
In this repository, the right policy is:
1. start from catalog discovery
2. prefer MCP resources when the current chat surface exposes resource attachment
3. fall back to catalog tools when resource attachment is unavailable
4. keep loaded skill context bounded
Suggested instruction text:
```md
When a task may match a documented implementation pattern from `personal-mcp`:
1. Start with catalog-first discovery.
2. Prefer MCP resources when the chat surface exposes resource attachment.
3. If MCP resource attachment is unavailable, use `list_resources`/`read_resource` first, then thin catalog tools if needed.
4. Load only the most relevant skill document, or at most 2 skill documents.
5. Reconcile loaded skill guidance with the actual repository code before making changes.
Preferred resource order:
1. `resource://catalog/skills_index`
2. `resource://catalog/skills/{skill_id}`
3. `resource://skills/<skill-id>/document`
4. `resource://skills/<skill-id>/references/<ref-id>` when needed
Preferred tool fallback order:
The server installs [`ResourcesAsTools`](https://gofastmcp.com/servers/transforms/resources-as-tools), which exposes generic tools:
1. `list_resources`
2. `read_resource`
3. `search_patterns`
4. `get_pattern_by_id`
5. `get_skill_document_by_id`
Compatibility aliases for clients that use `catalog_*` naming are also available:
A tool-only client should list resources, select a `skill://<name>/SKILL.md` URI, and read it. It can then read `_manifest` and selected supporting paths through the same tool.
1. `catalog_search_patterns`
2. `catalog_get_pattern_by_id`
3. `catalog_get_skill_document_by_id`
There are no skill-specific search, detail, or document tools. This avoids maintaining a second discovery implementation.
Use canonical names first; aliases exist only to preserve interoperability when a client emits non-canonical names.
## Optional Tool Search
If confidence is low after discovery, ask one clarifying question before loading more context.
For large tool inventories, FastMCP search transforms can reduce tool-list noise:
1. `PERSONAL_MCP_TOOL_SEARCH=none|regex|bm25` defaults to `none`.
2. `PERSONAL_MCP_TOOL_SEARCH_MAX_RESULTS=<positive int>` defaults to `5`.
3. `list_resources` and `read_resource` remain visible in search modes.
These settings filter tools, not native skill resources.
## Copilot Invocation
In VS Code, skills can arrive through:
1. explicit attachment from `Add Context > MCP Resources` or `MCP: Browse Resources`
2. generic `list_resources` and `read_resource` tool calls
3. a slash-command prompt that names a specific native skill URI
Instructions can steer retrieval, but they do not guarantee automatic resource attachment in every chat surface.
A reliable prompt for a tool-only session is:
```text
Use personal-mcp list_resources to find the best matching skill://.../SKILL.md resource. Read one selected skill, inspect its _manifest only if supporting detail is needed, and reconcile the guidance with this workspace.
```
This is intentionally guidance, not a guarantee. It gives Copilot a strong policy for when to use resources and when to fall back to discovery tools, while preserving the resource-first architecture.
## Thin Shim Pattern
## Failure Modes and Recovery
Consumer repositories can bind file scopes to native skill resources with short `.github/instructions/*.instructions.md` files.
Common failure modes:
| `applyTo` scope | Companion docs | Primary skill resource |
| --- | --- | --- |
| `**/*.md` | [Authoring Guide](./authoring.md) | `skill://zensical-docs/SKILL.md` |
| `tests/**` | [Testing](./testing.md) | `skill://pytesting/SKILL.md` |
| `.vscode/**` | [VS Code Configuration](./skills/vscode-configuration/SKILL.md) | `skill://vscode-configuration/SKILL.md` |
1. No relevant skill selected.
2. Too many skills selected (context bloat).
3. Stale assumptions from old metadata.
4. Slash command bypasses normal discovery and forces the wrong skill.
Minimal shape:
Recovery sequence:
```md
---
name: <scope name>
description: Route <path scope> edits to a personal-mcp skill.
applyTo: '<glob>'
---
1. re-run catalog lookup
2. narrow by tags and intent
3. fetch only top candidates
4. if still ambiguous, ask one clarifying question
5. use explicit `/` workflow for deterministic fallback
Load `skill://<skill-name>/SKILL.md` first. Read `_manifest` and supporting files only when the task needs deeper detail. Apply the guidance to the current repository rather than treating it as generated output.
```
## Checklist
## Failure Recovery
Use this checklist when configuring GitHub Copilot in VS Code against `personal-mcp`:
When no skill is an obvious match:
1. confirm server connectivity
2. verify catalog resources are readable
3. verify at least one `resource://skills/<id>/document` can be fetched
4. add one deterministic `/` command for fallback
5. add Copilot instruction: prefer catalog-first discovery, then targeted skill fetch
6. verify context size remains bounded
7. validate behavior in Ask/Edit/Agent-style workflows with at least one task each
1. compare the available main-resource descriptions again
2. select at most two candidates
3. read their main files, not all supporting files
4. ask one clarifying question if the choice remains ambiguous
Suggested instruction policy text:
When a supporting path fails, refresh `_manifest`; file paths are the public supporting-resource identifiers.
1. Start with catalog-first discovery.
2. Prefer MCP resources when the chat surface exposes resource attachment.
3. Otherwise use tool fallback to load one or two likely skill documents.
4. Prefer `list_resources`/`read_resource` first when operating in tool-only clients.
5. If confidence is low, ask one clarifying question before loading more.
## Runtime Checklist
## Summary
The intended model is:
1. skills are canonical MCP resources
2. `/` commands are explicit Copilot control shortcuts
3. guided skill loading should be catalog-driven, bounded, and explicit about whether it is using resources or tools
Using all three together gives predictable control when needed and low-friction assistance by default in VS Code.
1. Confirm MCP connectivity.
2. Confirm at least one `skill://<name>/SKILL.md` resource is listed.
3. Read its `_manifest` and verify `SKILL.md` appears with a SHA256 hash.
4. Read one supporting file through its manifest path.
5. Confirm `list_resources` and `read_resource` are available for tool-only clients.
6. Keep loaded context bounded to the selected skill and relevant files.
+18 -2
View File
@@ -4,8 +4,9 @@ version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
"fastapi>=0.115.0",
"fastmcp>=2.10.0",
"pydantic-settings>=2.0.0",
"fastmcp>=3.4.4",
"pydantic-settings>=2",
"python-json-logger>=4",
"pyyaml>=6.0.2",
"uvicorn[standard]>=0.34.0",
"zensical>=0.0.45",
@@ -23,11 +24,26 @@ packages = ["src/personal_mcp"]
[dependency-groups]
dev = [
"ipywidgets>=8.1.8",
"pre-commit>=4.6.0",
"ruff>=0.15.18",
"ty>=0.0.51",
]
test = [
"pytest>=9.1.1",
"pytest-asyncio>=1.4.0",
"pytest-cov>=7.1.0",
]
[tool.pytest.ini_options]
addopts = ["--strict-markers"]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
markers = [
"unit: fast deterministic tests with no external dependencies",
"integration: framework or component integration tests",
"smoke: thin critical-path checks",
]
[tool.ty.src]
include = ["src", "tests"]
+63
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@@ -0,0 +1,63 @@
line-length = 120
indent-width = 4
target-version = "py313"
exclude = [
".venv",
".devenv",
".git",
".vscode",
"build",
"site",
"__pycache__",
]
[lint]
preview = true
extend-select = [
"ARG", # https://docs.astral.sh/ruff/rules/#flake8-unused-arguments-arg
"B", # https://docs.astral.sh/ruff/rules/#flake8-bugbear-b
"C4", # https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4
"DOC102", # https://docs.astral.sh/ruff/rules/docstring-extraneous-parameter/
"DOC202", # https://docs.astral.sh/ruff/rules/docstring-extraneous-returns/
"DOC403", # https://docs.astral.sh/ruff/rules/docstring-extraneous-yields/
"DOC502", # https://docs.astral.sh/ruff/rules/docstring-extraneous-exception/
"E", "W", # https://docs.astral.sh/ruff/rules/#pycodestyle-e-w
"F", # https://docs.astral.sh/ruff/rules/#pyflakes-f
"FURB", # https://docs.astral.sh/ruff/rules/#refurb-furb
"I", # https://docs.astral.sh/ruff/rules/#isort-i
"N", # https://docs.astral.sh/ruff/rules/#pep8-naming-n
"PD", # https://docs.astral.sh/ruff/rules/#pandas-vet-pd
"PTH", # https://docs.astral.sh/ruff/rules/#flake8-use-pathlib-pth
"UP", # https://docs.astral.sh/ruff/rules/#pyupgrade-up
"SIM", # https://docs.astral.sh/ruff/rules/#flake8-simplify-sim
"PLR0202", # https://docs.astral.sh/ruff/rules/no-classmethod-decorator/
"PLR0203", # https://docs.astral.sh/ruff/rules/no-staticmethod-decorator/
"PLR0206", # https://docs.astral.sh/ruff/rules/property-with-parameters/
"PLR0915", # https://docs.astral.sh/ruff/rules/too-many-statements/
"PLR1702", # https://docs.astral.sh/ruff/rules/too-many-nested-blocks/
"TRY002",
]
extend-fixable = ["ALL"]
ignore = [
"UP046",
"UP047",
]
[lint.extend-per-file-ignores]
"*.ipynb" = [
"F401", # unused imports
"F841", # unused local variable
"F821", # undefined name in exploratory notebook cells,
"LOG015", # root logger calls
]
[lint.isort]
force-single-line = true
[format]
quote-style = "double"
indent-style = "space"
skip-magic-trailing-comma = false
line-ending = "auto"
+80
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@@ -0,0 +1,80 @@
#!/usr/bin/env python3
"""Create directory and symlink for Copilot skills."""
import argparse
import subprocess
import sys
from pathlib import Path
def add_skill(markdown_path: str) -> None:
"""Add a skill by creating symlink in ~/.copilot/skills/.
Args:
markdown_path: Path to the markdown file (relative or absolute)
Raises:
FileNotFoundError: If markdown file doesn't exist
RuntimeError: If symlink creation fails
"""
md_file = Path(markdown_path)
if not md_file.exists():
raise FileNotFoundError(f"Markdown file not found: {markdown_path}")
# Get absolute path
abs_md_path = md_file.resolve()
# Extract skill name from filename (without .md extension)
skill_name = md_file.stem
# Create skill directory
skills_dir = Path.home() / ".copilot" / "skills" / skill_name
skills_dir.mkdir(parents=True, exist_ok=True)
# Create symlink
symlink_path = skills_dir / "SKILL.md"
# Remove existing symlink if it exists
if symlink_path.exists() or symlink_path.is_symlink():
symlink_path.unlink()
# Create the symlink using ln -s for compatibility
result = subprocess.run(
["ln", "-s", str(abs_md_path), str(symlink_path)],
capture_output=True,
check=False,
text=True,
)
if result.returncode != 0:
raise RuntimeError(f"Failed to create symlink: {result.stderr}")
print(f"✓ Created skill link: {symlink_path} -> {abs_md_path}")
def main() -> None:
"""Main entry point."""
parser = argparse.ArgumentParser(description="Create directory and symlink for Copilot skills")
subparsers = parser.add_subparsers(dest="command", help="Command to run")
# Add subcommand
add_parser = subparsers.add_parser("add", help="Add a skill")
add_parser.add_argument("markdown", help="Path to markdown skill file")
args = parser.parse_args()
if not args.command:
parser.print_help()
sys.exit(1)
if args.command == "add":
try:
add_skill(args.markdown)
except (FileNotFoundError, RuntimeError, OSError) as e:
print(f"✗ Error: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
+8 -10
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@@ -1,13 +1,11 @@
from personal_mcp.catalog.server import (
build_skill_detail_payload,
build_skills_index_payload,
get_pattern_by_id_payload,
search_patterns_payload,
)
from personal_mcp.catalog.server import build_prompt_detail_payload
from personal_mcp.catalog.server import build_prompts_index_payload
from personal_mcp.catalog.server import get_prompt_by_id_payload
from personal_mcp.catalog.server import search_prompts_payload
__all__ = [
"build_skill_detail_payload",
"build_skills_index_payload",
"get_pattern_by_id_payload",
"search_patterns_payload",
"build_prompt_detail_payload",
"build_prompts_index_payload",
"get_prompt_by_id_payload",
"search_prompts_payload",
]
+43 -91
View File
@@ -2,82 +2,44 @@ from __future__ import annotations
from typing import Any
from personal_mcp.skills.document_loader import DocsRegistry, SkillRecord
from personal_mcp.registry.models.registry import DocsRegistry
from personal_mcp.registry.models.registry import PromptRecord
from personal_mcp.registry.models.registry import PromptSummaryPayload
DEFAULT_LIMIT = 20
MAX_LIMIT = 100
def _pattern_payload(skill: SkillRecord) -> dict[str, Any]:
return {
"id": skill.skill_id,
"name": skill.name,
"version": skill.version,
"description": skill.description,
"tags": list(skill.tags),
"depends_on": list(skill.depends_on),
"capabilities": list(skill.capabilities),
"resources": list(skill.capabilities),
}
def _summary_payload(skill: SkillRecord) -> dict[str, Any]:
return {
"id": skill.skill_id,
"name": skill.name,
"description": skill.description,
"tags": list(skill.tags),
"capabilities": list(skill.capabilities),
"version": skill.version,
"document_uri": skill.document_uri,
"detail_uri": f"resource://catalog/skills/{skill.skill_id}",
"resources": {
"document": skill.document_uri,
"references": [
f"resource://skills/{skill.skill_id}/references/{ref_id}"
for ref_id in sorted(skill.references)
],
},
}
def _skill_matches(
skill: SkillRecord,
def _prompt_matches(
prompt: PromptRecord,
*,
query: str | None,
tag: str | None,
capability: str | None,
) -> bool:
if query:
lowered = query.strip().lower()
if lowered:
haystack = " ".join(
[
skill.skill_id,
skill.name,
skill.description,
" ".join(skill.tags),
prompt.prompt_id,
prompt.name,
prompt.description,
" ".join(prompt.tags),
" ".join(sorted(prompt.arguments)),
]
).lower()
terms = [term for term in lowered.replace("-", " ").split() if term]
if any(term not in haystack for term in terms):
return False
if tag and tag not in skill.tags:
return False
if capability and capability not in skill.capabilities:
return False
return True
return not (tag and tag not in prompt.tags)
def build_skills_index_payload(
def build_prompts_index_payload(
registry: DocsRegistry,
*,
query: str | None = None,
tag: str | None = None,
capability: str | None = None,
cursor: str | None = None,
limit: int | None = None,
) -> dict[str, Any]:
@@ -87,20 +49,14 @@ def build_skills_index_payload(
except ValueError as exc:
raise ValueError("cursor must be an integer string") from exc
ordered = [
registry.skills_by_id[skill_id] for skill_id in registry.skills_in_load_order
]
matches = [
skill
for skill in ordered
if _skill_matches(skill, query=query, tag=tag, capability=capability)
]
ordered = [registry.prompts_by_id[prompt_id] for prompt_id in registry.prompts_in_load_order]
matches = [prompt for prompt in ordered if _prompt_matches(prompt, query=query, tag=tag)]
page = matches[start : start + normalized_limit]
next_cursor = start + normalized_limit
return {
"skills": [_summary_payload(skill) for skill in page],
"prompts": [PromptSummaryPayload.from_record(prompt).model_dump() for prompt in page],
"total": len(matches),
"cursor": str(start),
"limit": normalized_limit,
@@ -108,35 +64,28 @@ def build_skills_index_payload(
}
def build_skill_detail_payload(registry: DocsRegistry, skill_id: str) -> dict[str, Any]:
if skill_id not in registry.skills_by_id:
raise KeyError(skill_id)
def build_prompt_detail_payload(registry: DocsRegistry, prompt_id: str) -> dict[str, Any]:
if prompt_id not in registry.prompts_by_id:
raise KeyError(prompt_id)
skill = registry.skills_by_id[skill_id]
prompt = registry.prompts_by_id[prompt_id]
return {
"id": skill.skill_id,
"name": skill.name,
"description": skill.description,
"version": skill.version,
"tags": list(skill.tags),
"depends_on": list(skill.depends_on),
"capabilities": list(skill.capabilities),
"id": prompt.prompt_id,
"name": prompt.name,
"description": prompt.description,
"version": prompt.version,
"tags": list(prompt.tags),
"capabilities": list(prompt.capabilities),
"resources": {
"document": skill.document_uri,
"references": {
ref_id: {
"uri": ref.uri,
"mime_type": ref.mime_type,
"title": ref.title,
"path": ref.relpath,
}
for ref_id, ref in sorted(skill.references.items())
"document": prompt.document_uri,
},
"arguments": {
arg_name: arg.model_dump(exclude_none=True) for arg_name, arg in sorted(prompt.arguments.items())
},
}
def search_patterns_payload(
def search_prompts_payload(
registry: DocsRegistry,
*,
query: str = "",
@@ -149,25 +98,28 @@ def search_patterns_payload(
requested_tags = [tag.strip() for tag in (tags or []) if tag and tag.strip()]
matches: list[SkillRecord] = []
for skill_id in registry.skills_in_load_order:
skill = registry.skills_by_id[skill_id]
if not _skill_matches(skill, query=query, tag=None, capability=None):
matches: list[PromptRecord] = []
for prompt_id in registry.prompts_in_load_order:
prompt = registry.prompts_by_id[prompt_id]
if not _prompt_matches(prompt, query=query, tag=None):
continue
if requested_tags and any(tag not in skill.tags for tag in requested_tags):
if requested_tags and any(tag not in prompt.tags for tag in requested_tags):
continue
matches.append(skill)
matches.append(prompt)
page = matches[normalized_skip : normalized_skip + normalized_limit]
return {
"patterns": [_pattern_payload(skill) for skill in page],
"prompts": [PromptSummaryPayload.from_record(prompt).model_dump() for prompt in page],
"total": len(matches),
"skip": normalized_skip,
"limit": normalized_limit,
}
def get_pattern_by_id_payload(registry: DocsRegistry, skill_id: str) -> dict[str, Any]:
if skill_id not in registry.skills_by_id:
return {"found": False, "id": skill_id}
return {"found": True, "pattern": _pattern_payload(registry.skills_by_id[skill_id])}
def get_prompt_by_id_payload(registry: DocsRegistry, prompt_id: str) -> dict[str, Any]:
if prompt_id not in registry.prompts_by_id:
return {"found": False, "id": prompt_id}
return {
"found": True,
"prompt": build_prompt_detail_payload(registry, prompt_id),
}
+43
View File
@@ -0,0 +1,43 @@
from functools import cache
from pathlib import Path
from typing import Literal
from pydantic import BaseModel
from pydantic import DirectoryPath
from pydantic import Field
from pydantic_settings import BaseSettings
from pydantic_settings import SettingsConfigDict
DEFAULT_ENV_FILE = Path(".env").resolve()
_REPO_ROOT = Path(__file__).resolve().parents[2]
class Mounts(BaseModel):
docs: str = "/docs"
mcp: str = "/mcp"
class Settings(BaseSettings):
"""Runtime settings for the HTTP MCP and docs server."""
model_config = SettingsConfigDict(
env_file=DEFAULT_ENV_FILE,
env_prefix="PERSONAL_MCP_",
extra="ignore",
)
debug: bool = False
log_level: str = "info"
mounts: Mounts = Field(default_factory=Mounts)
mcp_transport: Literal["http", "sse"] = "http"
site_dir: DirectoryPath = Field(default=_REPO_ROOT / "site")
@cache
def get_settings(**overrides) -> Settings:
return Settings(**overrides)
def refresh_settings(**overrides):
get_settings.cache_clear()
return get_settings(**overrides)
+1
View File
@@ -0,0 +1 @@
../../docs
+11 -4
View File
@@ -1,12 +1,19 @@
from personal_mcp.mcp import mcp
from personal_mcp.web.app import app
from fastapi import FastAPI
__all__ = ["app", "main", "mcp"]
from personal_mcp.mcp import create_mcp
from personal_mcp.web.app import create_app as create_fastapi
__all__ = ["create_app", "main"]
def create_app() -> FastAPI:
"""Create the HTTP application for ASGI servers using factory mode."""
return create_fastapi()
def main() -> None:
"""Run the root MCP server."""
mcp.run()
create_mcp().run()
if __name__ == "__main__":
+129 -139
View File
@@ -1,35 +1,29 @@
from __future__ import annotations
import os
import re
from inspect import Parameter
from inspect import Signature
from typing import Any
from typing import cast
from fastmcp import FastMCP
from fastmcp.server.transforms import ResourcesAsTools
from fastmcp.server.transforms.search import BM25SearchTransform, RegexSearchTransform
from fastmcp.server.transforms.search import BM25SearchTransform
from fastmcp.server.transforms.search import RegexSearchTransform
from personal_mcp.catalog.server import (
build_skill_detail_payload,
build_skills_index_payload,
get_pattern_by_id_payload,
search_patterns_payload,
)
from personal_mcp.skills.document_loader import (
DocsRegistry,
load_docs_registry,
read_docs_markdown_path,
read_skill_document,
read_skill_reference,
)
from personal_mcp.catalog.server import build_prompt_detail_payload
from personal_mcp.catalog.server import build_prompts_index_payload
from personal_mcp.catalog.server import get_prompt_by_id_payload
from personal_mcp.catalog.server import search_prompts_payload
from personal_mcp.registry.load import get_docs_registry
from personal_mcp.registry.models.registry import DocsRegistry
from personal_mcp.registry.read import read_docs_markdown_path
from personal_mcp.registry.read import read_prompt_document
from personal_mcp.skills import create_skills_provider
DOCS_ROOT = os.getenv("PERSONAL_MCP_DOCS_ROOT", "../../docs")
TOOL_SEARCH_MODE = os.getenv("PERSONAL_MCP_TOOL_SEARCH", "none").strip().lower()
TOOL_SEARCH_MAX_RESULTS = os.getenv("PERSONAL_MCP_TOOL_SEARCH_MAX_RESULTS", "5")
REGISTRY: DocsRegistry = load_docs_registry(
package_anchor="personal_mcp",
docs_root=DOCS_ROOT,
)
mcp = FastMCP("personal-mcp", on_duplicate="error")
def _parse_positive_int(value: str, *, env_name: str) -> int:
@@ -42,7 +36,7 @@ def _parse_positive_int(value: str, *, env_name: str) -> int:
return parsed
def _install_tool_fallback_transforms() -> None:
def _install_tool_fallback_transforms(mcp: FastMCP) -> None:
# Expose list_resources/read_resource for tool-only clients.
mcp.add_transform(ResourcesAsTools(mcp))
@@ -65,9 +59,7 @@ def _install_tool_fallback_transforms() -> None:
mcp.add_transform(BM25SearchTransform(**kwargs))
return
raise ValueError(
"PERSONAL_MCP_TOOL_SEARCH must be one of: none, regex, bm25"
)
raise ValueError("PERSONAL_MCP_TOOL_SEARCH must be one of: none, regex, bm25")
def _ro_annotations() -> dict[str, bool]:
@@ -77,140 +69,138 @@ def _ro_annotations() -> dict[str, bool]:
}
@mcp.resource(
"resource://catalog/skills_index",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def skills_index() -> dict[str, Any]:
return build_skills_index_payload(REGISTRY)
def _render_prompt_markdown(content: str, arguments: dict[str, Any]) -> str:
rendered = content
for key, value in arguments.items():
rendered = rendered.replace(f"{{{{{key}}}}}", str(value))
return rendered
@mcp.resource(
"resource://catalog/skills_index{?q,tag,capability,cursor,limit}",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def skills_index_query(
q: str | None = None,
tag: str | None = None,
capability: str | None = None,
cursor: str | None = None,
limit: int | None = None,
) -> dict[str, Any]:
return build_skills_index_payload(
REGISTRY,
query=q,
tag=tag,
capability=capability,
cursor=cursor,
limit=limit,
def _make_prompt_handler(content: str):
def prompt_handler(**kwargs: Any) -> str:
return _render_prompt_markdown(content, kwargs)
return prompt_handler
def _register_prompt_objects(mcp: FastMCP, registry: DocsRegistry) -> None:
for prompt_id in registry.prompts_in_load_order:
prompt = registry.prompts_by_id[prompt_id]
annotations: dict[str, Any] = {}
params: list[Parameter] = []
for arg_name, arg in sorted(prompt.arguments.items()):
annotations[arg_name] = str
default = Parameter.empty if arg.required else None
params.append(
Parameter(
arg_name,
kind=Parameter.KEYWORD_ONLY,
default=default,
annotation=str,
)
)
signature = Signature(parameters=params, return_annotation=str)
prompt_handler = _make_prompt_handler(prompt.document_content)
prompt_handler.__name__ = re.sub(r"[^a-zA-Z0-9_]", "_", prompt_id)
prompt_handler.__doc__ = prompt.description
prompt_handler.__annotations__ = annotations
cast(Any, prompt_handler).__signature__ = signature
mcp.prompt(
prompt_handler,
name=prompt_id,
description=prompt.description,
tags=set(prompt.tags),
)
@mcp.resource(
"resource://catalog/skills/{skill_id}",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def skill_detail(skill_id: str) -> dict[str, Any]:
return build_skill_detail_payload(REGISTRY, skill_id)
@mcp.resource(
"resource://skills/{skill_id}/document",
mime_type="text/markdown",
tags={"skill-doc"},
annotations=_ro_annotations(),
)
def skill_document(skill_id: str) -> dict[str, str]:
return read_skill_document(REGISTRY, skill_id)
@mcp.resource(
"resource://skills/{skill_id}/references/{ref_id}",
mime_type="text/markdown",
tags={"reference"},
annotations=_ro_annotations(),
)
def skill_reference(skill_id: str, ref_id: str) -> dict[str, str]:
return read_skill_reference(REGISTRY, skill_id=skill_id, ref_id=ref_id)
@mcp.resource(
def _register_components(mcp: FastMCP, registry: DocsRegistry) -> None:
@mcp.resource(
"resource://docs/{path*}",
mime_type="text/markdown",
tags={"docs"},
annotations=_ro_annotations(),
)
def docs_markdown(path: str) -> dict[str, str]:
return read_docs_markdown_path(REGISTRY, path)
)
def docs_markdown(path: str) -> dict[str, str]:
return read_docs_markdown_path(registry, path)
@mcp.resource(
"resource://catalog/prompts_index",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def prompts_index() -> dict[str, Any]:
return build_prompts_index_payload(registry)
@mcp.tool
def search_patterns(
@mcp.resource(
"resource://catalog/prompts_index{?q,tag,cursor,limit}",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def prompts_index_query(
q: str | None = None,
tag: str | None = None,
cursor: str | None = None,
limit: int | None = None,
) -> dict[str, Any]:
return build_prompts_index_payload(
registry,
query=q,
tag=tag,
cursor=cursor,
limit=limit,
)
@mcp.resource(
"resource://catalog/prompts/{prompt_id}",
mime_type="application/json",
tags={"catalog"},
annotations=_ro_annotations(),
)
def prompt_detail(prompt_id: str) -> dict[str, Any]:
return build_prompt_detail_payload(registry, prompt_id)
@mcp.resource(
"resource://prompts/{prompt_id}/document",
mime_type="text/markdown",
tags={"prompt-doc"},
annotations=_ro_annotations(),
)
def prompt_document(prompt_id: str) -> dict[str, str]:
return read_prompt_document(registry, prompt_id)
@mcp.tool
def search_prompts(
query: str = "",
tags: list[str] | None = None,
skip: int = 0,
limit: int = 20,
) -> dict[str, Any]:
"""Search normalized pattern metadata with optional tags and pagination."""
return search_patterns_payload(
REGISTRY,
) -> dict[str, Any]:
"""Search prompt metadata with optional tags and pagination."""
return search_prompts_payload(
registry,
query=query,
tags=tags,
skip=skip,
limit=limit,
)
@mcp.tool
def get_pattern_by_id(id: str) -> dict[str, Any]:
"""Return one normalized pattern by stable id."""
return get_pattern_by_id_payload(REGISTRY, id)
@mcp.tool
def get_prompt_by_id(prompt_id: str) -> dict[str, Any]:
"""Return one prompt by stable id."""
return get_prompt_by_id_payload(registry, prompt_id)
@mcp.tool
def get_skill_document_by_id(skill_id: str) -> dict[str, Any]:
"""Return the canonical skill document payload for a stable skill id."""
if skill_id not in REGISTRY.skills_by_id:
return {"found": False, "id": skill_id}
return {
"found": True,
"document": read_skill_document(REGISTRY, skill_id),
}
@mcp.tool
def catalog_search_patterns(
query: str = "",
tags: list[str] | None = None,
skip: int = 0,
limit: int = 20,
) -> dict[str, Any]:
"""Compatibility alias for clients expecting catalog_* tool naming."""
return search_patterns(
query=query,
tags=tags,
skip=skip,
limit=limit,
)
@mcp.tool
def catalog_get_pattern_by_id(id: str) -> dict[str, Any]:
"""Compatibility alias for clients expecting catalog_* tool naming."""
return get_pattern_by_id(id)
@mcp.tool
def catalog_get_skill_document_by_id(skill_id: str) -> dict[str, Any]:
"""Compatibility alias for clients expecting catalog_* tool naming."""
return get_skill_document_by_id(skill_id)
_install_tool_fallback_transforms()
def create_mcp() -> FastMCP:
registry = get_docs_registry()
mcp = FastMCP("personal-mcp", on_duplicate="error")
_register_components(mcp, registry)
_register_prompt_objects(mcp, registry)
mcp.add_provider(create_skills_provider())
_install_tool_fallback_transforms(mcp)
return mcp
+9
View File
@@ -0,0 +1,9 @@
from .models.registry import DocsRegistry
from .models.registry import PromptRecord
from .models.registry import PromptSummaryRecord
__all__ = [
"DocsRegistry",
"PromptRecord",
"PromptSummaryRecord",
]
@@ -0,0 +1 @@
"""Functions to produce immutable dataclasses representing the document registry."""

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