Documentation Generation Doc Generate

# Automated Documentation Generation Implementation Playbook

This file contains detailed patterns, checklists, and code samples referenced by the skill.

## Instructions

Generate comprehensive documentation by analyzing the codebase and creating the following artifacts:

### 1. **API Documentation**
- Extract endpoint definitions, parameters, and responses from code
- Generate OpenAPI/Swagger specifications
- Create interactive API documentation (Swagger UI, Redoc)
- Include authentication, rate limiting, and error handling details

### 2. **Architecture Documentation**
- Create system architecture diagrams (Mermaid, PlantUML)
- Document component relationships and data flows
- Explain service dependencies and communication patterns
- Include scalability and reliability considerations

### 3. **Code Documentation**
- Generate inline documentation and docstrings
- Create README files with setup, usage, and contribution guidelines
- Document configuration options and environment variables
- Provide troubleshooting guides and code examples

### 4. **User Documentation**
- Write step-by-step user guides
- Create getting started tutorials
- Document common workflows and use cases
- Include accessibility and localization notes

### 5. **Documentation Automation**
- Configure CI/CD pipelines for automatic doc generation
- Set up documentation linting and validation
- Implement documentation coverage checks
- Automate deployment to hosting platforms

### Quality Standards

Ensure all generated documentation:
- Is accurate and synchronized with current code
- Uses consistent terminology and formatting
- Includes practical examples and use cases
- Is searchable and well-organized
- Follows accessibility best practices

## Reference Examples

### Example 1: Code Analysis for Documentation

**API Documentation Extraction**
```python
import ast
from typing import Dict, List

class APIDocExtractor:
    def extract_endpoints(self, code_path):
        """Extract API endpoints and their documentation"""
        endpoints = []

        with open(code_path, 'r') as f:
            tree = ast.parse(f.read())

        for node in ast.walk(tree):
            if isinstance(node, ast.FunctionDef):
                for decorator in node.decorator_list:
                    if self._is_route_decorator(decorator):
                        endpoint = {
                            'method': self._extract_method(decorator),
                            'path': self._extract_path(decorator),
                            'function': node.name,
                            'docstring': ast.get_docstring(node),
                            'parameters': self._extract_parameters(node),
                            'returns': self._extract_returns(node)
                        }
                        endpoints.append(endpoint)
        return endpoints

    def _extract_parameters(self, func_node):
        """Extract function parameters with types"""
        params = []
        for arg in func_node.args.args:
            param = {
                'name': arg.arg,
                'type': ast.unparse(arg.annotation) if arg.annotation else None,
                'required': True
            }
            params.append(param)
        return params
```

**Schema Extraction**
```python
def extract_pydantic_schemas(file_path):
    """Extract Pydantic model definitions for API documentation"""
    schemas = []

    with open(file_path, 'r') as f:
        tree = ast.parse(f.read())

    for node in ast.walk(tree):
        if isinstance(node, ast.ClassDef):
            if any(base.id == 'BaseModel' for base in node.bases if hasattr(base, 'id')):
                schema = {
                    'name': node.name,
                    'description': ast.get_docstring(node),
                    'fields': []
                }

                for item in node.body:
                    if isinstance(item, ast.AnnAssign):
                        field = {
                            'nam