code-documenter▌
jeffallan/claude-skills · updated Apr 8, 2026
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Generates and validates technical documentation across docstrings, API specs, and developer guides.
- ›Supports multiple docstring formats (Google, NumPy, Sphinx for Python; JSDoc for TypeScript) and API specification standards (OpenAPI, AsyncAPI, gRPC)
- ›Includes validation workflows for each format: doctest/pytest for Python, TypeScript compilation checks, and Redocly linting for OpenAPI specs
- ›Covers inline code documentation, interactive API portals, documentation site generation, and
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.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
How to use code-documenter on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add code-documenter
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches code-documenter from GitHub repository jeffallan/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate code-documenter. Access the skill through slash commands (e.g., /code-documenter) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★51 reviews- ★★★★★Aisha Khan· Dec 28, 2024
Useful defaults in code-documenter — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nia Shah· Dec 24, 2024
code-documenter has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ganesh Mohane· Dec 20, 2024
I recommend code-documenter for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Emma Okafor· Nov 19, 2024
We added code-documenter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Nia Gupta· Nov 15, 2024
Solid pick for teams standardizing on skills: code-documenter is focused, and the summary matches what you get after install.
- ★★★★★Aditi Dixit· Oct 18, 2024
Useful defaults in code-documenter — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hassan Harris· Oct 10, 2024
Solid pick for teams standardizing on skills: code-documenter is focused, and the summary matches what you get after install.
- ★★★★★Michael Shah· Oct 6, 2024
We added code-documenter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Evelyn Patel· Sep 25, 2024
I recommend code-documenter for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Michael Srinivasan· Sep 21, 2024
Registry listing for code-documenter matched our evaluation — installs cleanly and behaves as described in the markdown.
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