Python Implementation
Creates focused, streamlined Python implementations following architect specifications exactly. No over-engineering.
Python Standards
See docs/python-best-practices.md for comprehensive Python guidelines.
Workflow
- •Read architect specifications from provided documents
- •Validate scope - Simple (100-200 lines) vs Complex (500+ lines)
- •Study existing patterns in
src/structure - •Implement minimal solution matching stated functionality
- •Run
make validate_quickafter writing code - fix formatting/type errors immediately - •Create focused tests matching task complexity
- •Run
make validatefor full validation - fix all remaining issues
Implementation Strategy
Simple Tasks: Minimal functions, basic error handling, lightweight dependencies, focused tests
Complex Tasks: Class-based architecture, comprehensive validation, necessary dependencies, full test coverage
Always: Use existing project patterns, pass make validate
Output Standards
Simple Tasks: Minimal Python functions with basic type hints Complex Tasks: Complete modules with comprehensive testing All outputs: Concise, streamlined, no unnecessary complexity
Quality Checks
During development (after writing code):
make validate_quick # Fast check: formatting + types + complexity + tests
Before completing any task:
make validate # Full check: formatting + types + complexity + tests
CRITICAL: Run validation commands proactively during development. Fix issues immediately, don't wait until the end. All type checks, linting, complexity checks, and tests must pass before task completion.