api-documentation-generator
About
This skill generates comprehensive API documentation from source code, including OpenAPI specifications and endpoint details. It's ideal for creating or updating API docs when users mention endpoints or documentation needs. The tool produces structured documentation with parameters, responses, and code examples for each endpoint.
Documentation
API Documentation Generator Skill
Generates
- OpenAPI/Swagger specifications
- API endpoint documentation
- SDK usage examples
- Integration guides
- Error code references
- Authentication guides
Documentation Structure
For Each Endpoint
## GET /api/v1/users/:id
### Description
Brief explanation of what this endpoint does
### Parameters
| Name | Type | Required | Description |
|------|------|----------|-------------|
| id | string | Yes | User ID |
### Response
**200 Success**
```json
{
"id": "usr_123",
"name": "John Doe",
"email": "[email protected]",
"created_at": "2025-01-15T10:30:00Z"
}
404 Not Found
{
"error": "USER_NOT_FOUND",
"message": "User does not exist"
}
Examples
cURL
curl -X GET "https://api.example.com/api/v1/users/usr_123" \
-H "Authorization: Bearer YOUR_TOKEN"
JavaScript
const user = await fetch('/api/v1/users/usr_123', {
headers: { 'Authorization': 'Bearer token' }
}).then(r => r.json());
Python
response = requests.get(
'https://api.example.com/api/v1/users/usr_123',
headers={'Authorization': 'Bearer token'}
)
user = response.json()
Quick Install
/plugin add https://github.com/luongnv89/claude-howto/tree/main/doc-generatorCopy and paste this command in Claude Code to install this skill
GitHub 仓库
Related Skills
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
llamaindex
MetaLlamaIndex is a data framework for building RAG-powered LLM applications, specializing in document ingestion, indexing, and querying. It provides key features like vector indices, query engines, and agents, and supports over 300 data connectors. Use it for document Q&A, chatbots, and knowledge retrieval when building data-centric applications.
go-test
MetaThe go-test skill provides expertise in Go's standard testing package and best practices. It helps developers implement table-driven tests, subtests, benchmarks, and coverage strategies while following Go conventions. Use it when writing test files, creating mocks, detecting race conditions, or organizing integration tests in Go projects.
