implementing-python

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

1. Read architect specifications from provided documents
2. Validate scope - Simple (100-200 lines) vs Complex (500+ lines)
3. Study existing patterns in src/ structure
4. Implement minimal solution matching stated functionality
5. Run make validate_quick after writing code - fix formatting/type errors immediately
6. Create focused tests matching task complexity
7. Run make validate for 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.