code-documenter

Jeffallan/claude-skillsDeveloper ToolsReview advisedSnapshot 6ec7b9481fdf

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Summary

Generates, formats, and validates technical documentation — including docstrings, OpenAPI/Swagger specs, JSDoc annotations, doc portals, and user guides. Use when adding docstrings to functions or classes, creating API documentation, building documentation sites, or writing tutorials and user guides. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, getting started guides.

SKILL.md

Code Documenter

Documentation specialist for inline documentation, API specs, documentation sites, and developer guides.

When to Use This Skill

Applies to any task involving code documentation, API specs, or developer-facing guides. See the reference table below for specific sub-topics.

Core Workflow

Discover - Ask for format preference and exclusions
Detect - Identify language and framework
Analyze - Find undocumented code
Document - Apply consistent format
Validate - Test all code examples compile/run:
- Python: python -m doctest file.py for doctest blocks; pytest --doctest-modules for module-wide checks
- TypeScript/JavaScript: tsc --noEmit to confirm typed examples compile
- OpenAPI: validate spec with npx @redocly/cli lint openapi.yaml
- If validation fails: fix examples and re-validate before proceeding to the Report step
Report - Generate coverage summary

Quick-Reference Examples

Google-style Docstring (Python)

def fetch_user(user_id: int, active_only: bool = True) -> dict:
    """Fetch a single user record by ID.

    Args:
        user_id: Unique identifier for the user.
        active_only: When True, raise an error for inactive users.

    Returns:
        A dict containing user fields (id, name, email, created_at).

    Raises:
        ValueError: If user_id is not a positive integer.
        UserNotFoundError: If no matching user exists.
    """

NumPy-style Docstring (Python)

def compute_similarity(vec_a: np.ndarray, vec_b: np.ndarray) -> float:
    """Compute cosine similarity between two vectors.

    Parameters
    ----------
    vec_a : np.ndarray
        First input vector, shape (n,).
    vec_b : np.ndarray
        Second input vector, shape (n,).

    Returns
    -------
    float
        Cosine similarity in the range [-1, 1].

    Raises
    ------
    ValueError
        If vectors have different lengths.
    """

/**
 * Fetches a paginated list of products from the catalog.
 *
 * @param {string} categoryId - The category to filter by.
 * @param {number} [page=1] - Page number (1-indexed).
 * @param {number} [limit=20] - Maximum items per page.
 * @returns {Promise<ProductPage>} Resolves to a page of product records.
 * @throws {NotFoundError} If the category does not exist.
 *
 * @example
 * const page = await fetchProducts('electronics', 2, 10);
 * console.log(page.items);
 */
async function fetchProducts(
  categoryId: string,
  page = 1,
  limit = 20
): Promise<ProductPage> { ... }

Topic	Reference	Load When
Python Docstrings	`references/python-docstrings.md`	Google, NumPy, Sphinx styles
TypeScript JSDoc	`references/typescript-jsdoc.md`	JSDoc patterns, TypeScript
FastAPI/Django API	`references/api-docs-fastapi-django.md`	Python API documentation
NestJS/Express API	`references/api-docs-nestjs-express.md`	Node.js API documentation
Coverage Reports	`references/coverage-reports.md`	Generating documentation reports
Documentation Systems	`references/documentation-systems.md`	Doc sites, static generators, search, testing
Interactive API Docs	`references/interactive-api-docs.md`	OpenAPI 3.1, portals, GraphQL, WebSocket, gRPC, SDKs
User Guides & Tutorials	`references/user-guides-tutorials.md`	Getting started, tutorials, troubleshooting, FAQs

code-documenter

Summary

SKILL.md

Code Documenter

When to Use This Skill

Core Workflow

Quick-Reference Examples

Google-style Docstring (Python)

NumPy-style Docstring (Python)

JSDoc (TypeScript)

Reference Guide

Constraints

MUST DO

MUST NOT DO

Output Formats

Knowledge Reference

Related skills