api-documenter
Description
Auto-generate API documentation from code and comments.
How to Use
- Visit the GitHub repository to get the SKILL.md file
- Copy the file to your project root or .cursor/rules directory
- Restart your AI assistant or editor to apply the new skill
Full Skill Documentation
name
api-documenter
description
Auto-generate API documentation from code and comments. Use when API endpoints change, or user mentions API docs. Creates OpenAPI/Swagger specs from code. Triggers on API file changes, documentation requests, endpoint additions.
allowed-tools
Read, Write, Grep
Tags
About api-documenter
api-documenter is an AI skill in the docs category, designed to help developers and users work more effectively with AI tools. Auto-generate API documentation from code and comments.
This skill has earned 1,000 stars on GitHub, reflecting strong community adoption and trust. It is compatible with claude, codex.
Key Capabilities
Why Use api-documenter
Adding api-documenter to your AI workflow can significantly enhance your productivity in docs tasks. With pre-defined prompt templates and best practices, this skill helps AI assistants better understand your requirements and deliver more accurate responses.
Whether you use claude or codex, you can easily integrate this skill into your existing development environment.
Explore More docs Skills
Discover more AI skills in the docs category to build a comprehensive AI skill stack.
Related Skills
internal-comms
A set of resources to help write internal communications like status reports, leadership updates, newsletters, FAQs, and incident reports.
openapi-spec-generation
Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns.
content-research-writer
Assists in writing high-quality content by conducting research, adding citations, improving hooks, and iterating on outlines.
docs-review
Review documentation changes for compliance with the Metabase writing style guide.
coding-tutor
Personalized coding tutorials that build on your existing knowledge and use your actual codebase for examples.