Cursor Rules: Development Flavor

Rapid prototyping, debugging, and code quality enforcement

include: .cursorrules.base

Development Architecture

• API-First: Design APIs before implementation
• Schema-Driven: Use JSON schemas to validate all data structures
• Test-Driven: Write tests for all DUX object validations
• Containerized: Docker-based development environment

Code Quality Standards

• Type Safety: Use type hints and validation for all DUX objects
• Error Handling: Graceful degradation and meaningful error messages
• Logging: Structured logging for debugging and monitoring
• Documentation: Inline docs for all public APIs and schemas

DUX Object Processing

• Schema Validation: All objects must pass JSON schema validation
• Evidence Linking: Ensure all claims link to valid Provenance objects
• Atomic Operations: Process objects independently for fault tolerance
• Batch Processing: Support bulk operations for efficiency

API Design Patterns

• RESTful: Standard HTTP methods for CRUD operations
• Streaming: Real-time processing for LLM responses
• Validation: Input validation at API boundaries
• Error Responses: Consistent error format with actionable messages

Database Integration

• Neo4j: Graph database for DUX object relationships
• Vector Search: Embedding-based similarity search
• Indexing: Optimize queries for common access patterns
• Backup: Regular backups of research data and schemas

LLM Integration

• Prompt Engineering: Structured prompts for consistent object extraction
• Streaming: Real-time token streaming for user feedback
• Error Recovery: Handle LLM failures gracefully
• Cost Optimization: Monitor and optimize token usage

Testing Strategy

• Unit Tests: Test individual object validation and processing
• Integration Tests: Test API endpoints and database operations
• Schema Tests: Validate all JSON schemas are correct
• End-to-End Tests: Test complete workflows from upload to storage

Development Workflow

• Local Development: Docker Compose for local environment
• Hot Reloading: Fast iteration for API development
• Debugging: Comprehensive logging and error tracking
• Code Review: Automated checks for schema compliance

Performance Optimization

• Caching: Cache frequently accessed DUX objects
• Async Processing: Non-blocking operations for better UX
• Batch Operations: Efficient bulk processing of objects
• Resource Monitoring: Track memory and CPU usage

Security Considerations

• Input Sanitization: Validate and sanitize all user inputs
• Authentication: Secure access to research data
• Data Encryption: Encrypt sensitive research data
• Audit Logging: Track all data access and modifications

Deployment Patterns

• Containerization: Docker images for consistent deployment
• Environment Variables: Configuration via environment variables
• Health Checks: Monitor service health and dependencies
• Rollback Strategy: Quick rollback for failed deployments

Monitoring & Observability

• Metrics: Track API performance and object processing rates
• Logging: Structured logs for debugging and analysis
• Alerting: Notify on errors and performance issues
• Tracing: Track requests through the system