better data models

src/personal_mcp/registry/models/document.py

@@ -1,56 +0,0 @@
-from pathlib import PurePosixPath
-from typing import Self
-
-from .common import StrictFrozenModel
-
-
-class MarkdownDocumentModel(StrictFrozenModel):
-    """Normalized markdown document content with path and frontmatter."""
-
-    relpath: str
-    content: str
-    frontmatter: str | None = None
-    skill_slug: str | None = None
-
-
-class SkillFilesBundleModel(StrictFrozenModel):
-    """Grouped skill documents partitioned into skill, references, and other files."""
-
-    slug: str
-    skill: MarkdownDocumentModel
-    references: tuple[MarkdownDocumentModel, ...] = ()
-    other: tuple[MarkdownDocumentModel, ...] = ()
-
-    @classmethod
-    def from_parts(
-        cls,
-        *,
-        slug: str,
-        skill: MarkdownDocumentModel,
-        references: tuple[MarkdownDocumentModel, ...] = (),
-        other: tuple[MarkdownDocumentModel, ...] = (),
-    ) -> Self:
-        # Ensure deterministic ordering even when the source collection is unordered.
-        references_sorted = tuple(sorted(references, key=lambda doc: doc.relpath))
-        other_sorted = tuple(sorted(other, key=lambda doc: doc.relpath))
-        return cls(
-            slug=slug,
-            skill=skill,
-            references=references_sorted,
-            other=other_sorted,
-        )
-
-
-def to_markdown_document_model(
-    *,
-    relpath: PurePosixPath,
-    content: str,
-    frontmatter: str | None,
-    skill_slug: str | None,
-) -> MarkdownDocumentModel:
-    return MarkdownDocumentModel(
-        relpath=relpath.as_posix(),
-        content=content,
-        frontmatter=frontmatter,
-        skill_slug=skill_slug,
-    )

src/personal_mcp/registry/models/prompt.py

@@ -1,8 +1,10 @@
 import re
 from collections.abc import Mapping
+from pathlib import PurePosixPath
 from typing import Any
 from typing import Literal
 
+import yaml
 from pydantic import Field
 from pydantic import field_validator
 
@@ -100,10 +102,21 @@ class PromptFrontmatter(StrictFrozenModel):
         return value
 
 
-class PromptDocumentModel(StrictFrozenModel):
-    """Structured representation of a prompt markdown document."""
+class StoredPrompt(StrictFrozenModel):
+    """Normalized prompt document content with path and frontmatter for storage in the registry."""
 
     prompt_id: str
-    relpath: str
+    relpath: PurePosixPath
     content: str
-    frontmatter: PromptFrontmatter
+    frontmatter: PromptFrontmatter | None = None
+
+    @field_validator("frontmatter", mode="before")
+    @classmethod
+    def parse_frontmatter_yaml(cls, value: str | None):
+        if value is None:
+            return None
+        try:
+            data = yaml.safe_load(value)
+        except yaml.YAMLError as e:
+            raise ValueError(f"invalid YAML in frontmatter: {e}") from e
+        return PromptFrontmatter.model_validate(data)

src/personal_mcp/registry/models/skill.py

@@ -1,6 +1,7 @@
 from collections.abc import Mapping
 from pathlib import PurePosixPath
 
+import yaml
 from pydantic import Field
 from pydantic import field_validator
 
@@ -107,19 +108,31 @@ class SkillFrontmatter(StrictFrozenModel):
         return value
 
 
-class SkillDocumentModel(StrictFrozenModel):
-    """Structured representation of a skill markdown document."""
-
-    skill_id: str
-    relpath: PurePosixPath
-    content: str
-    frontmatter: SkillFrontmatter
-
-
-class SkillReferenceDocumentModel(StrictFrozenModel):
+class StoredSkillReference(StrictFrozenModel):
     """Structured representation of a skill reference markdown document."""
 
     ref_id: str
     relpath: PurePosixPath
     content: str
     entry: ReferenceEntry
+
+
+class StoredSkill(StrictFrozenModel):
+    """Structured representation of a skill markdown document."""
+
+    skill_id: str
+    relpath: PurePosixPath
+    content: str
+    frontmatter: SkillFrontmatter
+    references: dict[str, StoredSkillReference] = Field(default_factory=dict)
+
+    @field_validator("frontmatter", mode="before")
+    @classmethod
+    def parse_frontmatter_yaml(cls, value: str | None):
+        if value is None:
+            return None
+        try:
+            data = yaml.safe_load(value)
+        except yaml.YAMLError as e:
+            raise ValueError(f"invalid YAML in frontmatter: {e}") from e
+        return SkillFrontmatter.model_validate(data)