task updates

.vscode/tasks.json

@@ -5,7 +5,12 @@
       "label": "Ruff: Check",
       "type": "shell",
       "command": "uv",
-      "args": ["run", "ruff", "check", "."],
+      "args": [
+        "run",
+        "ruff",
+        "check",
+        "."
+      ],
       "options": {
         "cwd": "${workspaceFolder}"
       },
@@ -15,7 +20,11 @@
       "label": "Ty: Check",
       "type": "shell",
       "command": "uv",
-      "args": ["run", "ty", "check"],
+      "args": [
+        "run",
+        "ty",
+        "check"
+      ],
       "options": {
         "cwd": "${workspaceFolder}"
       },
@@ -25,7 +34,11 @@
       "label": "Docs: Build",
       "type": "shell",
       "command": "uv",
-      "args": ["run", "zensical", "build"],
+      "args": [
+        "run",
+        "zensical",
+        "build"
+      ],
       "options": {
         "cwd": "${workspaceFolder}"
       },
@@ -56,7 +69,12 @@
       "label": "Docker: Compose Up (Build)",
       "type": "shell",
       "command": "docker",
-      "args": ["compose", "up", "--build"],
+      "args": [
+        "compose",
+        "up",
+        "--build",
+        "-d"
+      ],
       "options": {
         "cwd": "${workspaceFolder}"
       },

docs/architecture.md

@@ -253,7 +253,7 @@ Allowed exception:
 Existing markdown reference sets are valid examples of authored source material for this architecture:
 
 1. docs/skills/pytesting/references/pytest-docs.md
-2. docs/skills/python-logging-dictconfig/references/python-logging-docs.md
+2. docs/skills/python-logging/references/python-logging-docs.md
 3. 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.

docs/contracts/prompt.md

@@ -58,7 +58,7 @@ Rules:
 
 Valid examples:
 
-1. `fill-pytest-scaffold`
+1. `pytest-fill-scaffold`
 2. `review-pr-comments`
 3. `scaffold-fastapi-service`
 

docs/mcp_layout.md

@@ -194,7 +194,7 @@ This keeps docs publication explicit and predictable.
 Existing reference docs remain valid content inputs in this pattern:
 
 1. docs/skills/pytesting/references/pytest-docs.md
-2. docs/skills/python-logging-dictconfig/references/python-logging-docs.md
+2. docs/skills/python-logging/references/python-logging-docs.md
 3. docs/skills/fastapi-uv-docker/references/fastapi-best-practices.md
 
 These are source documents, not deployment artifacts.

docs/prompts/pytest-fill-scaffold/PROMPT.md

@@ -1,8 +1,8 @@
 ---
-name: fill-pytest-scaffold
+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: fill-pytest-scaffold
+  id: pytest-fill-scaffold
   version: 1.0.0
   tags:
     - pytest
@@ -10,7 +10,7 @@ x-personal-mcp:
     - scaffolding
     - prompts
   capabilities:
-    - resource://prompts/fill-pytest-scaffold/document
+    - resource://prompts/pytest-fill-scaffold/document
   arguments:
     target_files:
       description: Target test file paths under tests/.
@@ -26,7 +26,7 @@ x-personal-mcp:
       required: false
 ---
 
-# Fill Pytest Scaffold
+# Pytest Fill Scaffold
 
 Use this prompt after test scaffolding exists and method names/docstrings are already in place.
 

docs/prompts/pytest-scaffold/PROMPT.md

@@ -0,0 +1,97 @@
+---
+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.

docs/skills/fastapi-async-sqlalchemy-modernization/SKILL.md

@@ -62,6 +62,9 @@ Use these concepts as the planning backbone:
 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).
+8. SQLModel adoption where appropriate:
+   Prefer SQLModel for typed ORM models and API-facing data models when it reduces duplication, while preserving SQLAlchemy async lifecycle patterns.
+   See the [SQLModel integration reference](references/sqlmodel.md).
 
 ### Concept Reference Map
 
@@ -74,6 +77,7 @@ Use these concepts as the planning backbone:
 | 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) |
+| SQLModel adoption where appropriate | [SQLModel integration reference](references/sqlmodel.md) |
 
 ## Decision Points
 
@@ -85,6 +89,7 @@ Use these branching decisions before proposing migration steps.
 | 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 |
+| Model layer | Existing SQLModel models fit roadmap | SQLAlchemy-only models: evaluate SQLModel adoption by bounded module |
 | Concurrency | Background jobs/tasks use DB | No background DB use |
 | Transaction style | Explicit context-managed transactions | Implicit/autobegin side effects |
 
@@ -115,6 +120,17 @@ Define one canonical model to migrate toward.
 
 Completion check: architecture diagram can explain where engine/session are created, used, and closed.
 
+### Step 1.5: Decide SQLModel Adoption Scope
+
+Decide where SQLModel should be introduced during modernization.
+
+- Prefer SQLModel when it reduces duplicated schema definitions between ORM entities and API data models.
+- Keep SQLAlchemy async engine/session lifecycle as the runtime foundation.
+- Use bounded adoption first (one module or feature area), then expand after validation.
+- If project is already heavily SQLAlchemy-only and stable, document rationale for staying SQLAlchemy-only.
+
+Completion check: plan includes an explicit SQLModel branch with target modules and non-goals.
+
 ### Step 2: Plan Engine Modernization
 
 Plan engine creation and pool behavior.
@@ -198,6 +214,7 @@ Create modernization quality gates.
 - Concurrency tests confirming one-session-per-task behavior.
 - Lifespan tests verifying cleanup calls and ordering.
 - Health/readiness tests including DB connectivity checks.
+- If SQLModel is adopted, model-validation tests cover SQLModel table models and API models at module boundaries.
 
 Completion check: all quality gates pass under the target async configuration.
 
@@ -222,6 +239,7 @@ A plan is complete only when it includes:
 - Explicit context-manager patterns for resource ownership.
 - AsyncExitStack composition strategy.
 - Transaction policy and exception behavior.
+- SQLModel adoption branch (use/adopt/defer) with rationale.
 - Concrete tests and rollout checkpoints.
 - A documented advisory backlog for non-critical implicit I/O improvements.
 
@@ -232,6 +250,7 @@ A plan is complete only when it includes:
 - Implicit commit/rollback behavior with unclear ownership.
 - Global mutable session state.
 - Lifespan cleanup that depends on implicit garbage collection.
+- Forcing SQLModel rewrites across the entire codebase in one phase without module-level rollout.
 
 ## Output Contract
 

docs/skills/fastapi-async-sqlalchemy-modernization/references/index.md

@@ -13,6 +13,7 @@ Purpose: concept registry for modernization guidance used by this skill.
 | 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 adoption and boundaries | [sqlmodel.md](sqlmodel.md) | adopted | advisory | platform/backend | 2026-06-26 |
 
 ---
 

docs/skills/fastapi-async-sqlalchemy-modernization/references/session.md

@@ -98,6 +98,12 @@ 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.
 
+## SQLModel Alignment
+
+- If using SQLModel, keep the same session ownership model: one `async_sessionmaker`, one `AsyncSession` per request/unit-of-work.
+- SQLModel does not replace SQLAlchemy async lifecycle primitives; it complements model declaration and typed data handling.
+- During migration, avoid mixed ad hoc patterns where some handlers create SQLAlchemy sessions directly while others use SQLModel-specific wrappers.
+
 ---
 
 ## Concurrency Rules

docs/skills/fastapi-async-sqlalchemy-modernization/references/sqlmodel.md

@@ -0,0 +1,123 @@
+# SQLModel Adoption and Boundaries
+
+!!! info "Primary sources"
+    - [SQLModel documentation](https://sqlmodel.tiangolo.com/)
+    - [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: advisory
+    - Applies to: api-runtime, workers, tests
+    - Last reviewed: 2026-06-26
+
+---
+
+## Purpose
+
+Define when and how to use SQLModel in an async FastAPI + SQLAlchemy modernization effort.
+
+The goal is pragmatic adoption: use SQLModel where it reduces model duplication and improves typing ergonomics, without disrupting established async engine/session lifecycle rules.
+
+---
+
+## Scope and Non-Goals
+
+- In scope: model-layer decisions, integration boundaries, phased adoption strategy.
+- Out of scope: full framework rewrites and all-at-once model migration.
+
+---
+
+## Rules
+
+- Keep SQLAlchemy async primitives as the runtime base: `create_async_engine`, `async_sessionmaker`, and `AsyncSession`.
+- Prefer SQLModel for new domain modules where table models and API schemas would otherwise be duplicated.
+- Migrate by bounded module or feature area; do not force whole-repo conversion in one phase.
+- Keep transaction and session ownership policies identical whether models are SQLAlchemy Declarative or SQLModel.
+- Document explicit reasons when SQLModel is deferred for a module.
+
+---
+
+## Recommended Patterns
+
+### Pattern A: Bounded module adoption
+
+- Choose one feature slice (for example, billing, projects, or auth profile data).
+- Introduce SQLModel models for that slice only.
+- Keep unchanged modules on existing SQLAlchemy models until a dedicated migration phase.
+
+### Pattern B: 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 C: Keep async lifecycle unchanged
+
+```python
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+engine = create_async_engine(settings.database_url, pool_pre_ping=True)
+session_factory = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
+```
+
+---
+
+## Interoperability Notes
+
+- SQLModel is designed as a thin layer over SQLAlchemy and Pydantic, so mixed codebases are expected during migration.
+- 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 adoption as equivalent to async-session modernization.
+- Rewriting all models at once without rollback checkpoints.
+- Introducing SQLModel in handlers while keeping old global/shared session patterns.
+- Mixing multiple query/session idioms within the same module without clear conventions.
+
+---
+
+## Operational Checks
+
+- Modernized module documents whether it is SQLModel-first or SQLAlchemy-only.
+- Session/transaction ownership remains consistent across both model styles.
+- New model modules use explicit API boundary models where needed.
+
+---
+
+## Testing Checks
+
+- Module-level tests verify CRUD semantics for adopted SQLModel models.
+- API tests verify response/request model behavior for SQLModel-based endpoints.
+- Regression tests confirm unchanged modules continue to function during phased rollout.
+
+---
+
+## Migration Notes
+
+- Start with low-risk bounded domains.
+- Expand only after validation of session lifecycle, transaction behavior, and endpoint correctness.
+- Maintain a tracked backlog of deferred modules with rationale and planned phase.

docs/skills/pydantic-settings/SKILL.md

@@ -0,0 +1,310 @@
+---
+name: pydantic-settings
+description: "Practical guide for implementing typed application configuration with pydantic-settings. Use when designing BaseSettings models, choosing env naming strategy, configuring dotenv or secrets, and customizing source priority safely."
+x-personal-mcp:
+  id: pydantic-settings
+  version: 1.0.0
+  tags:
+    - python
+    - pydantic
+    - pydantic-settings
+    - configuration
+    - env-vars
+    - secrets
+    - dotenv
+    - source-priority
+  capabilities:
+    - resource://skills/pydantic-settings/document
+---
+
+# 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 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",
+  )
+
+  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. Add ContextVar-Scoped Constructors And Accessors
+
+When configuration and database resources should be request- or context-scoped, use `ContextVar` backed constructor and accessor methods.
+
+Example pattern:
+
+```python
+from contextlib import contextmanager
+from contextvars import ContextVar
+from functools import cache
+
+from pydantic import SecretStr
+from pydantic_settings import BaseSettings
+from sqlmodel import Session, create_engine
+from sqlalchemy import Engine
+
+
+class DbSettings(BaseSettings):
+  model_config = {
+    "env_prefix": "DB_",
+    "extra": "ignore",
+  }
+
+  host: str = "localhost"
+  port: int = 5432
+  username: str
+  password: SecretStr
+
+  @property
+  def dsn(self) -> str:
+    return (
+      "postgresql://"
+      f"{self.username}:{self.password.get_secret_value()}"
+      f"@{self.host}:{self.port}/mydatabase"
+    )
+
+
+_db_settings: ContextVar[DbSettings | None] = ContextVar("db_settings", default=None)
+_db_conn: ContextVar[Engine | None] = ContextVar("db_conn", default=None)
+
+
+def get_db_settings(**kwargs) -> DbSettings:
+  settings = _db_settings.get()
+  if settings is None:
+    settings = DbSettings(**kwargs)
+    _db_settings.set(settings)
+    cleanup_engine()
+  return settings
+
+
+@cache
+def get_db_engine() -> Engine:
+  engine = _db_conn.get()
+  if engine is None:
+    engine = create_engine(get_db_settings().dsn)
+    _db_conn.set(engine)
+  return engine
+
+
+def cleanup_engine() -> None:
+  engine = _db_conn.get()
+  if engine is not None:
+    engine.dispose()
+    _db_conn.set(None)
+    get_db_engine.cache_clear()
+
+
+@contextmanager
+def get_session():
+  with Session(get_db_engine()) as session:
+    yield session
+```
+
+Design notes:
+
+1. `get_db_settings` is the constructor/accessor for settings and can accept explicit overrides in tests.
+2. `get_db_engine` is the constructor/accessor for the engine and reuses context-local state.
+3. `cleanup_engine` must run when settings change so stale DSNs do not leak across contexts.
+4. `get_session` centralizes session creation so call sites never build engines directly.
+
+Quality gate:
+
+1. Overriding settings triggers engine cleanup and cache invalidation.
+2. No module-level global engine is created outside accessors.
+3. Session creation always goes through `get_session()`.
+
+### 7. Add Focused Resource-Lifecycle Test
+
+Do not add tests that re-validate baseline `pydantic-settings` functionality (for example env parsing, alias semantics, or source precedence) unless you have custom behavior layered on top.
+
+Minimum test to add (only when an engine accessor exists):
+
+1. assert the database engine is not instantiated more than once for repeated accessor calls in the same lifecycle/context
+
+If the project has no database engine accessor, skip this section.
+
+Suggested invocation:
+
+1. `uv run pytest -q`
+
+## Completion Checks
+
+1. A single typed settings model exists for the service boundary.
+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. If an engine accessor exists, engine construction occurs at most once per lifecycle/context.
+
+## 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)
+
+### 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)

docs/skills/python-logging-dictconfig/SKILL.md

@@ -1,115 +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.'
-x-personal-mcp:
-    id: python-logging-dictconfig
-    version: 1.0.0
-    tags:
-        - logging
-        - python
-        - observability
-    capabilities:
-        - resource://skills/python-logging-dictconfig/document
----
-
-# 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`.

docs/skills/python-logging/SKILL.md

@@ -0,0 +1,143 @@
+---
+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.'
+x-personal-mcp:
+    id: python-logging
+    version: 1.0.0
+    tags:
+        - logging
+        - python
+        - observability
+    capabilities:
+        - resource://skills/python-logging/document
+---
+
+# 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 overview, library guidance, handlers, and dictConfig schema: [Python logging references](./references/python-logging-docs.md)
+- Minimal network logging example with a receiver and queue-backed client: [Network logging minimal example](./references/network-logging-minimal-example.md)
+- HTTP JSON logging example with `httpx` and a queue-backed client: [HTTPX logging handler example](./references/httpx-logging-handler-example.md)
+
+## 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. For small scripts, `basicConfig` can be enough; for applications, prefer a centralized configuration function.
+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.
+
+## 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.
+
+### Minimal dictConfig Baseline
+
+```python title="logging_config.py"
+import logging.config
+
+LOGGING = {
+    "version": 1,
+    "disable_existing_loggers": False,
+    "formatters": {
+        "console": {
+            "format": "%(asctime)s.%(msecs)03d %(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_logging() -> None:
+    logging.config.dictConfig(LOGGING)
+```
+
+```python title="app.py"
+from .logging_config import configure_logging
+
+configure_logging()
+```
+
+```python title="feature.py"
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def run(count: int) -> None:
+    logger.info("Processing %s items", count)
+```
+
+## 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.
+- 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`.

docs/skills/python-logging/references/httpx-logging-handler-example.md

@@ -0,0 +1,280 @@
+# HTTPX Logging Handler Example
+
+Use this reference when a Python application needs to send log records to an HTTP endpoint with [`httpx`](https://www.python-httpx.org/). The example follows the same boundaries as the TCP network example: feature modules use normal named loggers, application startup configures logging once, a queue keeps HTTP I/O off the caller path, and the receiver or collector owns final routing.
+
+Source docs to keep nearby:
+
+- [HTTPX clients](https://www.python-httpx.org/advanced/clients/) for connection pooling and shared request configuration.
+- [HTTPX timeouts](https://www.python-httpx.org/advanced/timeouts/) for connect, read, write, and pool timeout behavior.
+- [HTTPX JSON requests](https://www.python-httpx.org/quickstart/#sending-json-encoded-data) for posting JSON payloads.
+- [HTTPX exceptions](https://www.python-httpx.org/quickstart/#exceptions) for `RequestError`, `HTTPStatusError`, and `HTTPError` handling.
+- [Dealing with handlers that block](https://docs.python.org/3/howto/logging-cookbook.html#dealing-with-handlers-that-block) for why slow handlers should sit behind `QueueHandler` and `QueueListener`.
+- [`QueueHandler`](https://docs.python.org/3/library/logging.handlers.html#queuehandler) and [`QueueListener`](https://docs.python.org/3/library/logging.handlers.html#queuelistener) for queue-backed logging mechanics.
+- [`logging.makeLogRecord`](https://docs.python.org/3/library/logging.html#logging.makeLogRecord) for rebuilding records from serialized fields.
+
+## Minimal Topology
+
+Send each record as a JSON HTTP request to a collector endpoint. Use `httpx.Client`, not the top-level `httpx.post`, because a handler may send many records to the same host and should reuse connections.
+
+```text
+application code -> named logger -> QueueHandler -> QueueListener -> HTTPX JSON handler -> HTTP receiver or collector
+```
+
+Application modules stay ordinary:
+
+```python title="feature.py"
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def sync_customer(customer_id: str) -> None:
+    logger.info("Syncing customer %s", customer_id)
+```
+
+The module does not know whether logs are written locally, sent over HTTP, or forwarded by a platform collector.
+
+## Minimal Receiver
+
+This receiver is for local testing. A production deployment would usually send the same JSON shape to a managed log collector, OpenTelemetry collector, service endpoint, or internal ingestion API.
+
+```python title="log_http_receiver.py"
+import json
+import logging
+import logging.config
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+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"]},
+}
+
+
+class LogRecordRequestHandler(BaseHTTPRequestHandler):
+    def do_POST(self) -> None:
+        if self.path != "/logs":
+            self.send_error(404)
+            return
+
+        length = int(self.headers.get("Content-Length", "0"))
+        body = self.rfile.read(length)
+
+        try:
+            payload = json.loads(body.decode("utf-8"))
+            record = logging.makeLogRecord(payload)
+        except Exception:
+            logging.getLogger(__name__).exception("Dropped malformed log record")
+            self.send_error(400)
+            return
+
+        logger = logging.getLogger(record.name)
+        if logger.isEnabledFor(record.levelno):
+            logger.handle(record)
+
+        self.send_response(204)
+        self.end_headers()
+
+    def log_message(self, format: str, *args: object) -> None:
+        logging.getLogger("http.server").debug(format, *args)
+
+
+def main() -> None:
+    logging.config.dictConfig(LOGGING)
+    server = ThreadingHTTPServer(("127.0.0.1", 9021), LogRecordRequestHandler)
+    with server:
+        logging.getLogger(__name__).info("Listening for HTTP log records")
+        server.serve_forever()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+### Receiver Mechanics
+
+- The receiver accepts `POST /logs` with a JSON body and returns `204` when the record is accepted.
+- `makeLogRecord` turns the JSON dictionary back into a standard `LogRecord`, so the receiver can use the normal logger hierarchy.
+- Receiver-side filtering still works, but client-side filtering is better when volume matters because it avoids serializing and transmitting records that will be discarded.
+- `log_message` is redirected into the logging system at `DEBUG` so access logs do not pollute normal output.
+- Binding to `127.0.0.1` keeps the demo local. If the receiver is reachable across a network, put authentication, TLS, rate limits, and request size limits in front of it.
+
+## Client Configuration
+
+The client side uses a queue-backed logging handler. The listener thread owns the `httpx.Client`, posts JSON records, and closes the connection pool during shutdown.
+
+```python title="logging_config.py"
+import copy
+import logging
+import logging.handlers
+import queue
+
+import httpx
+
+
+class PreservingQueueHandler(logging.handlers.QueueHandler):
+    def prepare(self, record: logging.LogRecord) -> logging.LogRecord:
+        copied = copy.copy(record)
+        copied.message = copied.getMessage()
+        copied.msg = copied.message
+        copied.args = None
+
+        if copied.exc_info is not None and copied.exc_text is None:
+            copied.exc_text = logging.Formatter().formatException(copied.exc_info)
+        copied.exc_info = None
+
+        return copied
+
+
+class HttpxJsonLogHandler(logging.Handler):
+    def __init__(self, collector_url: str, 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(2.0, connect=1.0, write=2.0, pool=1.0)
+        self.client = httpx.Client(base_url=collector_url, headers=headers, timeout=timeout)
+
+    def emit(self, record: logging.LogRecord) -> None:
+        try:
+            response = self.client.post("/logs", json=self._payload_from_record(record))
+            response.raise_for_status()
+        except httpx.HTTPError:
+            self.handleError(record)
+
+    def close(self) -> None:
+        self.client.close()
+        super().close()
+
+    def _payload_from_record(self, record: logging.LogRecord) -> dict[str, object]:
+        payload: dict[str, object] = {
+            "name": record.name,
+            "levelno": record.levelno,
+            "levelname": record.levelname,
+            "pathname": record.pathname,
+            "lineno": record.lineno,
+            "funcName": record.funcName,
+            "created": record.created,
+            "process": record.process,
+            "processName": record.processName,
+            "threadName": record.threadName,
+            "msg": record.getMessage(),
+            "args": None,
+        }
+        if record.exc_text is not None:
+            payload["exc_text"] = record.exc_text
+        return payload
+
+
+def configure_logging(
+    collector_url: str = "http://127.0.0.1:9021",
+    token: str | None = None,
+) -> logging.handlers.QueueListener:
+    log_queue: queue.Queue[logging.LogRecord] = queue.Queue(maxsize=1000)
+    queue_handler = PreservingQueueHandler(log_queue)
+    http_handler = HttpxJsonLogHandler(collector_url, token)
+    listener = logging.handlers.QueueListener(log_queue, http_handler, respect_handler_level=True)
+
+    root = logging.getLogger()
+    root.setLevel(logging.INFO)
+    root.handlers[:] = [queue_handler]
+
+    listener.start()
+    return listener
+```
+
+```python title="app.py"
+import logging
+
+from logging_config import configure_logging
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    listener = configure_logging()
+    try:
+        logger.info("Service started")
+        logger.warning("Example warning from the HTTPX client")
+    finally:
+        listener.stop()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+Start `log_http_receiver.py` first, then run `app.py`. The receiver should print the records using its own formatter.
+
+## HTTPX Mechanics
+
+- `httpx.Client` keeps a connection pool. That matters for log handlers because repeated top-level `httpx.post(...)` calls would create new connections instead of reusing them.
+- `base_url` makes the handler explicit about the collector host while keeping the endpoint path short.
+- `timeout` is explicit. HTTPX has default timeouts, but logging code should state its tolerance for connect, write, read, and pool waits.
+- `response.raise_for_status()` turns non-2xx responses into `HTTPStatusError`, which then goes through the logging handler's normal error path.
+- `close()` closes the HTTPX connection pool. Pair this with `listener.stop()` during application shutdown so queued records are sent and resources are released.
+
+## Logging Mechanics
+
+- The application still configures logging once. Feature modules only call named loggers.
+- The HTTP handler sits behind `QueueHandler` and `QueueListener` because HTTP requests can block on DNS, connection pooling, TLS, request writes, response reads, and collector back pressure.
+- `PreservingQueueHandler` copies the record and formats exception text before clearing `exc_info`, so the queued record is safe to serialize and still carries useful traceback information.
+- The handler serializes a deliberate subset of `LogRecord` fields. Sending `record.__dict__` wholesale is easy, but it can include unserializable objects, accidental high-cardinality fields, or data the collector should not receive.
+- Authentication is represented as an optional bearer token header. In real applications, read tokens from a secret manager or runtime configuration, not from source code.
+
+## Rationale Behind The Pattern
+
+### Prefer HTTP When The Receiver Is Already An HTTP API
+
+HTTP is a good fit when logs go to a collector, gateway, ingestion service, or internal API that already expects JSON over HTTPS. It also gives you familiar deployment controls: TLS termination, authentication, reverse proxies, rate limiting, request size limits, and conventional status codes.
+
+### Keep HTTP Off The Caller Path
+
+Even a fast collector can become slow during deploys, network incidents, or downstream outages. Queueing makes that failure mode a logging concern instead of a request-latency concern.
+
+### Use A Bounded Queue For Honest Back Pressure
+
+The example uses `queue.Queue(maxsize=1000)` so overload becomes visible. The default `QueueHandler.enqueue()` uses `put_nowait()`, so a full queue calls `handleError()`. For production, decide whether to drop logs, block briefly, spill to disk, or switch to a platform collector.
+
+### Filter Before Sending
+
+The collector can reject records, but rejected records already consumed CPU, queue capacity, and network bandwidth. Use client-side logger and handler levels to avoid sending noisy records unless the deployment explicitly needs them.
+
+## Production Checklist
+
+Before using this beyond a local demo:
+
+1. Use HTTPS and authenticate clients. Treat the collector endpoint as an ingestion boundary, not a public anonymous API.
+2. Set request size limits and reject malformed payloads early.
+3. Decide the outage policy for collector failures and full queues.
+4. Add service, environment, instance, request, trace, or tenant identifiers as explicit serialized fields when operators need correlation.
+5. Redact or avoid secrets before records leave the process.
+6. Normalize untrusted newline-containing values if the final destination is line-oriented.
+7. Tune timeouts and queue size under load, not only with a happy-path local receiver.
+8. Prefer a managed collector, OpenTelemetry pipeline, or platform-native logging when one already exists.
+
+## Review Questions
+
+Use these questions when reviewing HTTPX logging code:
+
+- Does application code only call named loggers, without direct HTTP calls from feature modules?
+- Is the HTTP handler behind a queue for web, async, worker, or high-throughput paths?
+- Does the handler use a reusable `httpx.Client` rather than top-level request functions?
+- Are timeouts explicit and short enough for a logging path?
+- Are collector failures, non-2xx responses, and full queues handled deliberately?
+- Does shutdown stop the listener and close the HTTPX client?
+- Are authentication, TLS, secrets, request size, and high-cardinality context handled deliberately?

docs/skills/python-logging/references/network-logging-minimal-example.md

@@ -0,0 +1,277 @@
+# Network Logging Minimal Example
+
+Use this reference when an application needs to send Python logs across a network to a receiver process. The example is intentionally small, but it keeps the important production-shaped boundaries: application modules use normal named loggers, startup code configures routing once, network I/O happens away from the caller path, and the receiver owns final formatting and destinations.
+
+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) for the standard socket-listener recipe.
+- [Dealing with handlers that block](https://docs.python.org/3/howto/logging-cookbook.html#dealing-with-handlers-that-block) for why `QueueHandler` and `QueueListener` belong in front of slow handlers.
+- [`SocketHandler`](https://docs.python.org/3/library/logging.handlers.html#sockethandler) for the built-in network handler and its pickle-based default wire format.
+- [`QueueHandler`](https://docs.python.org/3/library/logging.handlers.html#queuehandler) and [`QueueListener`](https://docs.python.org/3/library/logging.handlers.html#queuelistener) for queue-backed logging mechanics.
+- [`logging.makeLogRecord`](https://docs.python.org/3/library/logging.html#logging.makeLogRecord) for recreating a `LogRecord` from serialized fields.
+- [`socketserver`](https://docs.python.org/3/library/socketserver.html) for a tiny TCP receiver.
+- [logging configuration security considerations](https://docs.python.org/3/library/logging.config.html#security-considerations) for treating remote logging configuration and importable config objects as trusted inputs only.
+
+## Minimal Topology
+
+Run one receiver process near the final logging destination. Application processes send serialized records to it, and the receiver decides how those records are formatted, filtered, written, rotated, or forwarded.
+
+```text
+application code -> named logger -> QueueHandler -> QueueListener -> JSON TCP handler -> receiver -> final handlers
+```
+
+This shape is useful because network handlers can block. Even a socket handler can pause on DNS, connection setup, back pressure, or a slow collector. The queue keeps normal request, worker, or CLI code from doing that work directly.
+
+It also keeps application code boring in the best way:
+
+```python title="feature.py"
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def process_order(order_id: str) -> None:
+    logger.info("Processing order %s", order_id)
+```
+
+The feature module does not know whether logs go to a terminal, a file, a socket, or a collector. That decision belongs to application startup.
+
+## Receiver
+
+The receiver accepts newline-delimited JSON records, recreates `LogRecord` objects, and routes them through local logging configuration.
+
+```python title="log_receiver.py"
+import json
+import logging
+import logging.config
+import socketserver
+
+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"]},
+}
+
+
+class LogRecordHandler(socketserver.StreamRequestHandler):
+    def handle(self) -> None:
+        for line in self.rfile:
+            try:
+                payload = json.loads(line.decode("utf-8"))
+                record = logging.makeLogRecord(payload)
+            except Exception:
+                logging.getLogger(__name__).exception("Dropped malformed log record")
+                continue
+
+            logger = logging.getLogger(record.name)
+            if logger.isEnabledFor(record.levelno):
+                logger.handle(record)
+
+
+class LogRecordServer(socketserver.ThreadingTCPServer):
+    allow_reuse_address = True
+
+
+def main() -> None:
+    logging.config.dictConfig(LOGGING)
+    with LogRecordServer(("127.0.0.1", 9020), LogRecordHandler) as server:
+        logging.getLogger(__name__).info("Listening for log records")
+        server.serve_forever()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+### Receiver Mechanics
+
+- `dictConfig` is local to the receiver. Client processes do not decide the final formatter, file handler, rotation policy, or downstream sink.
+- `makeLogRecord` rebuilds a logging record from plain fields. This is the same reconstruction step used by the cookbook socket receiver, but this example uses JSON instead of unpickling bytes from the network.
+- The receiver looks up `logging.getLogger(record.name)` so package-level logger names still route through the normal logging hierarchy.
+- The `isEnabledFor` check lets receiver-side logger levels suppress records before handlers run. Client-side filtering is still preferred when possible because it avoids wasted network traffic.
+- Binding to `127.0.0.1` makes the demo local-only. Binding to `0.0.0.0` changes the trust boundary and should be paired with network controls, authentication, or a real collector protocol.
+
+## Client Configuration
+
+Configure logging once at application startup. The root logger writes to a queue, and a listener thread sends records over TCP.
+
+```python title="logging_config.py"
+import copy
+import json
+import logging
+import logging.handlers
+import queue
+import socket
+
+
+class PreservingQueueHandler(logging.handlers.QueueHandler):
+    def prepare(self, record: logging.LogRecord) -> logging.LogRecord:
+        copied = copy.copy(record)
+        copied.message = copied.getMessage()
+        copied.msg = copied.message
+        copied.args = None
+
+        if copied.exc_info is not None and copied.exc_text is None:
+            copied.exc_text = logging.Formatter().formatException(copied.exc_info)
+        copied.exc_info = None
+
+        return copied
+
+
+class JsonTcpHandler(logging.Handler):
+    def __init__(self, host: str, port: int, timeout: float = 2.0) -> None:
+        super().__init__()
+        self.host = host
+        self.port = port
+        self.timeout = timeout
+        self._socket: socket.socket | None = None
+
+    def emit(self, record: logging.LogRecord) -> None:
+        try:
+            payload = self._payload_from_record(record)
+            message = json.dumps(payload, separators=(",", ":")).encode("utf-8")
+            self._send(message + b"\n")
+        except Exception:
+            self._close_socket()
+            self.handleError(record)
+
+    def close(self) -> None:
+        self._close_socket()
+        super().close()
+
+    def _close_socket(self) -> None:
+        if self._socket is not None:
+            self._socket.close()
+            self._socket = None
+
+    def _send(self, message: bytes) -> None:
+        if self._socket is None:
+            self._socket = socket.create_connection((self.host, self.port), self.timeout)
+        self._socket.sendall(message)
+
+    def _payload_from_record(self, record: logging.LogRecord) -> dict[str, object]:
+        payload: dict[str, object] = {
+            "name": record.name,
+            "levelno": record.levelno,
+            "levelname": record.levelname,
+            "pathname": record.pathname,
+            "lineno": record.lineno,
+            "funcName": record.funcName,
+            "created": record.created,
+            "process": record.process,
+            "processName": record.processName,
+            "threadName": record.threadName,
+            "msg": record.getMessage(),
+            "args": None,
+        }
+        if record.exc_text is not None:
+            payload["exc_text"] = record.exc_text
+        return payload
+
+
+def configure_logging(host: str = "127.0.0.1", port: int = 9020) -> logging.handlers.QueueListener:
+    log_queue: queue.Queue[logging.LogRecord] = queue.Queue(maxsize=1000)
+    queue_handler = PreservingQueueHandler(log_queue)
+    network_handler = JsonTcpHandler(host, port)
+    listener = logging.handlers.QueueListener(log_queue, network_handler, respect_handler_level=True)
+
+    root = logging.getLogger()
+    root.setLevel(logging.INFO)
+    root.handlers[:] = [queue_handler]
+
+    listener.start()
+    return listener
+```
+
+```python title="app.py"
+import logging
+
+from logging_config import configure_logging
+
+logger = logging.getLogger(__name__)
+
+
+def main() -> None:
+    listener = configure_logging()
+    try:
+        logger.info("Service started")
+        logger.warning("Example warning from the client")
+    finally:
+        listener.stop()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+Start `log_receiver.py` first, then run `app.py`. The receiver should print the records using its own formatter.
+
+### Client Mechanics
+
+- `PreservingQueueHandler` copies the record before mutating it for queue transfer. The standard `QueueHandler.prepare()` intentionally formats and strips unpickleable fields; overriding it is the documented escape hatch when the listener side needs custom serialization or exception text.
+- `queue.Queue(maxsize=1000)` makes back pressure visible. An unbounded queue is simpler, but a bounded queue forces a production decision about whether to drop, block, buffer elsewhere, or fail when the collector cannot keep up.
+- `QueueListener` owns the slow handler thread. It should be stopped during application shutdown so queued records are processed before exit.
+- `JsonTcpHandler` keeps a TCP connection open after the first event. That avoids a connection handshake per log record while keeping the example small enough to inspect.
+- `handleError()` preserves standard logging error behavior. In production, set an explicit policy for dropped records and collector outages rather than assuming logs always arrive.
+
+## Rationale Behind The Pattern
+
+### Use JSON Instead Of The Default Pickle Payload
+
+The built-in `SocketHandler` sends a pickled record dictionary. That is convenient on a trusted local path, but unpickling network input is a poor default at a trust boundary. JSON is not a complete security boundary by itself, but it is inspectable, language-neutral, and avoids executing pickle payloads.
+
+If you use `SocketHandler` anyway, override `makePickle()` with a safer encoding, sign payloads with a scheme such as HMAC, or keep the listener strictly inside a trusted local network.
+
+### Put The Network Handler Behind A Queue
+
+The cookbook explicitly calls out network handlers as potentially blocking. A queue is the smallest standard-library pattern that separates business code from slow handler work. This matters for web requests, async event loops, worker hot paths, and CLIs where the user should not wait on a collector timeout.
+
+### Centralize Final Destinations In The Receiver
+
+Multiple processes writing one file directly is a common source of garbled output, failed rotation, and confusing retention behavior. A receiver process serializes that responsibility: clients emit events, and one process writes or forwards them according to one logging configuration.
+
+### Keep Logger Names Stable
+
+Use module loggers such as `logging.getLogger(__name__)`. Do not create a logger per request, user, socket, tenant, or connection. Put those values in structured fields or formatted messages instead. Logger objects are singletons and are not freed during normal execution, so unbounded logger names become an avoidable memory and routing problem.
+
+### Filter Early When Volume Matters
+
+The receiver can filter records, but records already crossed the network by then. Set client-side logger or handler levels so routine `DEBUG` records are not serialized and transmitted unless the deployment is intentionally collecting them.
+
+## Production Checklist
+
+Before using this beyond a local demo:
+
+1. Protect the receiver with a trusted network boundary, TLS, a VPN, mutual authentication, or a real log collector. Do not expose an unauthenticated logging port to untrusted clients.
+2. Decide the outage policy: drop records, block briefly, buffer locally, retry with backoff, or fail startup when the receiver is unavailable.
+3. Size the queue and choose the overflow behavior deliberately. The default `QueueHandler.enqueue()` uses `put_nowait()`, so a full bounded queue goes through `handleError()`.
+4. Include service, environment, instance, request, trace, or tenant identifiers when operators need cross-service correlation.
+5. Redact or avoid secrets before records leave the process.
+6. Escape or normalize untrusted newline-containing values if the final destination is line-oriented and vulnerable to log injection confusion.
+7. Load-test the receiver and validate shutdown behavior before relying on the logs during incidents.
+8. Prefer a managed collector, OpenTelemetry pipeline, syslog, container stdout collection, or platform-native logging when the deployment environment already provides one.
+
+## Review Questions
+
+Use these questions when reviewing network logging code:
+
+- Does normal application code only call named loggers, without attaching handlers in feature modules?
+- Is network or file I/O behind a queue for web, async, worker, or high-throughput paths?
+- Is the wire format safe for the trust boundary, or does it rely on unpickling unauthenticated input?
+- Are logger and handler levels set so noisy records are filtered before crossing the network?
+- Is collector failure behavior explicit and tested?
+- Does shutdown stop the listener and flush the queue?
+- Are secrets, user-controlled newlines, and high-cardinality context handled deliberately?

docs/skills/python-logging/references/python-logging-docs.md

@@ -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`.
+- Prefer module loggers created with `logging.getLogger(__name__)`.
-- In modules, use `logging.getLogger(__name__)`.
+- Let applications configure handlers and formatters; libraries should emit logs without taking over routing.
-- Avoid calling `basicConfig` in libraries or scattered modules.
+- 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.

docs/usage.md

@@ -90,6 +90,94 @@ For prompt content, there is a third option when the client supports MCP prompt
 
 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.
 
+## Invocation Mechanics Deep Dive
+
+This section expands on how invocation works at runtime across chat entry points.
+
+### Invocation Surfaces
+
+A user request can arrive through one of these surfaces:
+
+1. plain chat request in Ask/Edit/Agent mode
+2. slash command invocation of a prompt or skill
+3. chat request with manually attached MCP resources
+
+Each surface changes how much discovery Copilot must do before applying guidance.
+
+### Resolution Order
+
+When multiple retrieval paths are possible, use this priority order:
+
+1. attached MCP resources already in context
+2. explicit slash-command workflow steps
+3. catalog-first discovery via MCP resources
+4. tool fallback (`list_resources` then `read_resource`, then thin catalog parity tools)
+
+This ordering keeps behavior predictable while minimizing unnecessary context expansion.
+
+### Prompt Invocation Pipeline
+
+For prompt-oriented flows, treat invocation as this sequence:
+
+1. parse prompt frontmatter and argument hints
+2. validate required inputs and ask one clarifying question if blocked
+3. run bounded discovery against prompt or skill catalogs
+4. fetch only selected document resources
+5. apply instructions to produce edits, recommendations, or commands
+6. report what was loaded and why
+
+Prompt objects and prompt document resources are additive mechanisms. The authored Markdown prompt document remains the canonical contract.
+
+### Argument Syntax Nuance
+
+Invocation strings such as target_modules=src/personal_mcp/registry/ingest/skill.py, mode=plan-only are a structured authoring convention, not a guaranteed client-level grammar.
+
+In practice:
+
+1. Prompt metadata defines expected argument names and intent.
+2. Prompt body instructions define how those inputs should be interpreted.
+3. Copilot may receive equivalent intent in freeform phrasing and still resolve it correctly.
+
+Implication for authors:
+
+1. Treat key=value examples as clarity aids for users.
+2. Do not assume strict parser enforcement unless your prompt explicitly validates and rejects malformed input.
+3. Include accepted invocation examples and one fallback freeform example so behavior is predictable for both humans and the model.
+
+This distinction is important because argument hints improve discoverability, while robust prompt instructions determine actual runtime reliability.
+
+### Skill Invocation Pipeline
+
+For guided skill loading, use this sequence:
+
+1. start from `resource://catalog/skills_index` or scoped index query
+2. inspect one or two top candidates for intent and capability fit
+3. fetch `resource://skills/<skill-id>/document`
+4. load references only when the task needs deeper detail
+5. apply only relevant sections and keep context bounded
+
+This avoids the common failure mode where many skill documents are loaded up front.
+
+### Determinism vs Flexibility
+
+Use this decision rule:
+
+1. choose slash-command invocation when repeatability and step order are critical
+2. choose guided loading when requests vary and speed matters more than strict orchestration
+3. escalate from guided loading to slash-command flow when confidence is low or conflicting skills appear
+
+### Invocation Trace (What to Log in Results)
+
+For transparent operation, include a concise invocation trace in task outputs:
+
+1. entry surface used (plain chat, slash command, or attached resource)
+2. discovery source used (catalog resource or tool path)
+3. resources fetched (ids only)
+4. clarifying questions asked (if any)
+5. reason for fallback or escalation (if used)
+
+This makes behavior auditable and easier to tune over time.
+
 ## Operating Pattern
 
 Use both modes intentionally in Copilot Chat.

src/personal_mcp/web/config.py

@@ -1,9 +1,11 @@
+from contextvars import ContextVar
 from pathlib import Path
 
 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[3]
 
 
@@ -11,7 +13,7 @@ class Settings(BaseSettings):
     """Runtime settings for the HTTP MCP and docs server."""
 
     model_config = SettingsConfigDict(
-        env_file=".env",
+        env_file=DEFAULT_ENV_FILE,
         env_prefix="PERSONAL_MCP_",
         extra="ignore",
     )
@@ -25,5 +27,12 @@ class Settings(BaseSettings):
     site_dir: Path = Field(default=_REPO_ROOT / "site")
 
 
+_settings: ContextVar[Settings | None] = ContextVar("settings", default=None)
+
+
 def get_settings() -> Settings:
-    return Settings()
+    settings = _settings.get()
+    if settings is None:
+        settings = Settings()
+        _settings.set(settings)
+    return settings

uv.lock

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 [[package]]
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+    { url = "https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl", hash = "sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76", size = 119243, upload-time = "2026-06-24T17:45:13.73Z" },
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zensical.toml

@@ -65,7 +65,8 @@ nav = [
   ] },
   { "Prompts" = [
     { "Authoring" = "prompts/authoring/PROMPT.md" },
-    { "Fill Pytest Scaffold" = "prompts/fill-pytest-scaffold/PROMPT.md" },
+    { "Pytest Fill Scaffold" = "prompts/pytest-fill-scaffold/PROMPT.md" },
+    { "Pytest Scaffold" = "prompts/pytest-scaffold/PROMPT.md" },
     { "Greenfield Architecture" = "prompts/greenfield-architecture/PROMPT.md" },
     { "MCP Consumer Repo Shim" = "prompts/mcp-consumer-repo-shim/PROMPT.md" },
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@@ -93,6 +94,7 @@ nav = [
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       { "Session" = "skills/fastapi-async-sqlalchemy-modernization/references/session.md" },
       { "Tx" = "skills/fastapi-async-sqlalchemy-modernization/references/transactions.md" },
+      { "SQLModel" = "skills/fastapi-async-sqlalchemy-modernization/references/sqlmodel.md" },
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       { "Template" = "skills/fastapi-async-sqlalchemy-modernization/references/template.md" },
@@ -120,8 +122,15 @@ nav = [
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     ] },
     { "Logging" = [
-      { "Overview" = "skills/python-logging-dictconfig/SKILL.md" },
+      { "Overview" = "skills/python-logging/SKILL.md" },
-      { "Docs" = "skills/python-logging-dictconfig/references/python-logging-docs.md" },
+      { "Docs" = "skills/python-logging/references/python-logging-docs.md" },
+      { "Network" = "skills/python-logging/references/network-logging-minimal-example.md" },
+      { "HTTPX" = "skills/python-logging/references/httpx-logging-handler-example.md" },
+    ] },
+    { "Pydantic Settings" = [
+      { "Overview" = "skills/pydantic-settings/SKILL.md" },
+      { "Source Docs" = "skills/pydantic-settings/references/source-documentation.md" },
+      { "Workflow" = "skills/pydantic-settings/references/implementation-workflow.md" },
     ] },
     { "Ruff" = [
       { "Overview" = "skills/ruff-linting-formating/SKILL.md" },