
Code Documenter
FreeStreamline your technical documentation process.
Free · Opens the source repo
What Code Documenter does
Code Documenter is designed to enhance the quality and consistency of your code documentation across various programming languages and frameworks. It provides a structured approach to generating, formatting, and validating documentation, ensuring that your inline docstrings, API specifications, and user guides meet industry standards. This skill is particularly useful for developers and teams looking to maintain high-quality documentation as part of their development workflow.
The core workflow begins by discovering the user's format preferences, followed by detecting the programming language and framework in use. The skill then analyzes the codebase to identify undocumented sections, applying a consistent documentation format. Each code example is validated to ensure accuracy, using built-in testing commands specific to the language, such as pytest for Python or tsc for TypeScript. This validation step is crucial, as it helps prevent the inclusion of incorrect or outdated examples in the documentation.
Additionally, Code Documenter generates a coverage summary report, providing insights into the completeness of your documentation efforts. It supports various documentation styles, including Google-style and NumPy-style docstrings for Python, as well as JSDoc for TypeScript. With references to detailed guides on API documentation for frameworks like FastAPI, Django, NestJS, and Express, this skill equips users with the resources needed to create comprehensive documentation portals and user guides.
Overall, Code Documenter is tailored for developers who prioritize clear, accurate, and maintainable documentation in their projects. Whether you're working on a small application or a large-scale system, this skill can significantly enhance your documentation practices, making it easier for other developers and users to understand and utilize your code.
When to use it
Use this skill when you need to document functions, classes, or APIs, and want to ensure that your documentation adheres to a specific style and is validated for accuracy.
When not to use it
This skill may not be suitable for projects with minimal documentation needs or for teams that prefer a more informal approach to documentation.
What you can build with it
Documenting a Python Project
Use Code Documenter to generate Google or NumPy-style docstrings for all functions and classes in your Python project, ensuring consistency and clarity.
Creating API Documentation
Leverage this skill to create OpenAPI specifications for your RESTful APIs, including validation to ensure accuracy and completeness.
Building User Guides
Utilize the skill to structure and write user guides or tutorials, complete with examples and diagrams, making it easier for new users to understand your application.
How to install Code Documenter
View source1. Install with the skills CLI
npx skills add jeffallan/claude-skills/code-documenter --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by jeffallanCode 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.pyfor doctest blocks;pytest --doctest-modulesfor module-wide checks - TypeScript/JavaScript:
tsc --noEmitto 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
- Python:
- 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.
"""
JSDoc (TypeScript)
/**
* 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> { ... }
Reference Guide
Load detailed guidance based on context:
| 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 |
Constraints
MUST DO
- Ask for format preference before starting
- Detect framework for correct API doc strategy
- Document all public functions/classes
- Include parameter types and descriptions
- Document exceptions/errors
- Test code examples in documentation
- Generate coverage report
MUST NOT DO
- Assume docstring format without asking
- Apply wrong API doc strategy for framework
- Write inaccurate or untested documentation
- Skip error documentation
- Document obvious getters/setters verbosely
- Create documentation that's hard to maintain
Output Formats
Depending on the task, provide:
- Code Documentation: Documented files + coverage report
- API Docs: OpenAPI specs + portal configuration
- Doc Sites: Site configuration + content structure + build instructions
- Guides/Tutorials: Structured markdown with examples + diagrams
Knowledge Reference
Google/NumPy/Sphinx docstrings, JSDoc, OpenAPI 3.0/3.1, AsyncAPI, gRPC/protobuf, FastAPI, Django, NestJS, Express, GraphQL, Docusaurus, MkDocs, VitePress, Swagger UI, Redoc, Stoplight
Frequently asked questions about Code Documenter
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