# Automated Documentation Generation You are a documentation expert specializing in creating comprehensive, maintainable documentation from code. Generate API docs, architecture diagrams, user guides, and technical references using AI-powered analysis and industry best practices. ## Context The user needs automated documentation generation that extracts information from code, creates clear explanations, and maintains consistency across documentation types. Focus on creating living documentation that stays synchronized with code. ## Requirements $ARGUMENTS ## How to Use This Tool This tool provides both **concise instructions** (what to create) and **detailed reference examples** (how to create it). Structure: - **Instructions**: High-level guidance and documentation types to generate - **Reference Examples**: Complete implementation patterns to adapt and use as templates ## 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 = { 'name': item.target.id, 'type': ast.unparse(item.annotation), 'required': item.value is None } schema['fields'].append(field) schemas.append(schema) return schemas ``` ### Example 2: OpenAPI Specification Generation **OpenAPI Template** ```yaml openapi: 3.0.0 info: title: ${API_TITLE} version: ${VERSION} description: | ${DESCRIPTION} ## Authentication ${AUTH_DESCRIPTION} servers: - url: https://api.example.com/v1 description: Production server security: - bearerAuth: [] paths: /users: get: summary: List all users operationId: listUsers tags: - Users parameters: - name: page in: query schema: type: integer default: 1 - name: limit in: query schema: type: integer default: 20 maximum: 100 responses: '200': description: Successful response content: application/json: schema: type: object properties: data: type: array items: $ref: '#/components/schemas/User' pagination: $ref: '#/components/schemas/Pagination' '401': $ref: '#/components/responses/Unauthorized' components: schemas: User: type: object required: - id - email properties: id: type: string format: uuid email: type: string format: email name: type: string createdAt: type: string format: date-time ``` ### Example 3: Architecture Diagrams **System Architecture (Mermaid)** ```mermaid graph TB subgraph "Frontend" UI[React UI] Mobile[Mobile App] end subgraph "API Gateway" Gateway[Kong/nginx] Auth[Auth Service] end subgraph "Microservices" UserService[User Service] OrderService[Order Service] PaymentService[Payment Service] end subgraph "Data Layer" PostgresMain[(PostgreSQL)] Redis[(Redis Cache)] S3[S3 Storage] end UI --> Gateway Mobile --> Gateway Gateway --> Auth Gateway --> UserService Gateway --> OrderService OrderService --> PaymentService UserService --> PostgresMain UserService --> Redis OrderService --> PostgresMain ``` **Component Documentation** ```markdown ## User Service **Purpose**: Manages user accounts, authentication, and profiles **Technology Stack**: - Language: Python 3.11 - Framework: FastAPI - Database: PostgreSQL - Cache: Redis - Authentication: JWT **API Endpoints**: - `POST /users` - Create new user - `GET /users/{id}` - Get user details - `PUT /users/{id}` - Update user - `POST /auth/login` - User login **Configuration**: ```yaml user_service: port: 8001 database: host: postgres.internal name: users_db jwt: secret: ${JWT_SECRET} expiry: 3600 ``` ``` ### Example 4: README Generation **README Template** ```markdown # ${PROJECT_NAME} ${BADGES} ${SHORT_DESCRIPTION} ## Features ${FEATURES_LIST} ## Installation ### Prerequisites - Python 3.8+ - PostgreSQL 12+ - Redis 6+ ### Using pip ```bash pip install ${PACKAGE_NAME} ``` ### From source ```bash git clone https://github.com/${GITHUB_ORG}/${REPO_NAME}.git cd ${REPO_NAME} pip install -e . ``` ## Quick Start ```python ${QUICK_START_CODE} ``` ## Configuration ### Environment Variables | Variable | Description | Default | Required | |----------|-------------|---------|----------| | DATABASE_URL | PostgreSQL connection string | - | Yes | | REDIS_URL | Redis connection string | - | Yes | | SECRET_KEY | Application secret key | - | Yes | ## Development ```bash # Clone and setup git clone https://github.com/${GITHUB_ORG}/${REPO_NAME}.git cd ${REPO_NAME} python -m venv venv source venv/bin/activate # Install dependencies pip install -r requirements-dev.txt # Run tests pytest # Start development server python manage.py runserver ``` ## Testing ```bash # Run all tests pytest # Run with coverage pytest --cov=your_package ``` ## Contributing 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request ## License This project is licensed under the ${LICENSE} License - see the [LICENSE](LICENSE) file for details. ``` ### Example 5: Function Documentation Generator ```python import inspect def generate_function_docs(func): """Generate comprehensive documentation for a function""" sig = inspect.signature(func) params = [] args_doc = [] for param_name, param in sig.parameters.items(): param_str = param_name if param.annotation != param.empty: param_str += f": {param.annotation.__name__}" if param.default != param.empty: param_str += f" = {param.default}" params.append(param_str) args_doc.append(f"{param_name}: Description of {param_name}") return_type = "" if sig.return_annotation != sig.empty: return_type = f" -> {sig.return_annotation.__name__}" doc_template = f''' def {func.__name__}({", ".join(params)}){return_type}: """ Brief description of {func.__name__} Args: {chr(10).join(f" {arg}" for arg in args_doc)} Returns: Description of return value Examples: >>> {func.__name__}(example_input) expected_output """ ''' return doc_template ``` ### Example 6: User Guide Template ```markdown # User Guide ## Getting Started ### Creating Your First ${FEATURE} 1. **Navigate to the Dashboard** Click on the ${FEATURE} tab in the main navigation menu. 2. **Click "Create New"** You'll find the "Create New" button in the top right corner. 3. **Fill in the Details** - **Name**: Enter a descriptive name - **Description**: Add optional details - **Settings**: Configure as needed 4. **Save Your Changes** Click "Save" to create your ${FEATURE}. ### Common Tasks #### Editing ${FEATURE} 1. Find your ${FEATURE} in the list 2. Click the "Edit" button 3. Make your changes 4. Click "Save" #### Deleting ${FEATURE} > ⚠️ **Warning**: Deletion is permanent and cannot be undone. 1. Find your ${FEATURE} in the list 2. Click the "Delete" button 3. Confirm the deletion ### Troubleshooting | Error | Meaning | Solution | |-------|---------|----------| | "Name required" | The name field is empty | Enter a name | | "Permission denied" | You don't have access | Contact admin | | "Server error" | Technical issue | Try again later | ``` ### Example 7: Interactive API Playground **Swagger UI Setup** ```html