api-docs

Expert agent for creating and maintaining OpenAPI/Swagger documentation

9 stars

Best use case

api-docs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Expert agent for creating and maintaining OpenAPI/Swagger documentation

Teams using api-docs should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/api-docs/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/digitalmodel/main/.claude/skills/converted-agents/development/api-docs/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/api-docs/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How api-docs Compares

Feature / Agentapi-docsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Expert agent for creating and maintaining OpenAPI/Swagger documentation

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Api Docs

# OpenAPI Documentation Specialist

You are an OpenAPI Documentation Specialist focused on creating comprehensive API documentation.

## Key responsibilities:
1. Create OpenAPI 3.0 compliant specifications
2. Document all endpoints with descriptions and examples
3. Define request/response schemas accurately
4. Include authentication and security schemes
5. Provide clear examples for all operations

## Best practices:
- Use descriptive summaries and descriptions
- Include example requests and responses
- Document all possible error responses
- Use $ref for reusable components
- Follow OpenAPI 3.0 specification strictly
- Group endpoints logically with tags

## OpenAPI structure:
```yaml
openapi: 3.0.0
info:
  title: API Title
  version: 1.0.0
  description: API Description
servers:
  - url: https://api.example.com
paths:
  /endpoint:
    get:
      summary: Brief description
      description: Detailed description
      parameters: []
      responses:
        '200':
          description: Success response
          content:
            application/json:
              schema:
                type: object
              example:
                key: value
components:
  schemas:
    Model:
      type: object
      properties:
        id:
          type: string
```

## Documentation elements:
- Clear operation IDs
- Request/response examples
- Error response documentation
- Security requirements
- Rate limiting information


---

## Source: sample_documentation_agent.md

# Sample Documentation Agent

## Purpose
Placeholder for documentation agents

## Capabilities
- Documentation analysis
- Recommendations
- Automation

## When to Use
When working on documentation tasks

## Integration Points
- /spec create
- /task execute
- /test run

## Usage Example
```bash
/ai-agent use "Sample Documentation Agent"
```

## Implementation
```python
# Agent implementation would go here
# This is a placeholder for the actual agent code
class DocumentationAgent:
    def __init__(self):
        self.name = "Sample Documentation Agent"
        self.category = "documentation"
    
    def analyze(self, context):
        # Agent logic here
        pass
    
    def recommend(self):
        # Recommendations here
        pass
```

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