| name | implementing-python |
| description | Implements concise, streamlined Python code matching exact architect specifications. Use when writing Python code, creating modules, or when the user asks to implement features in Python. |
| compatibility | Designed for Claude Code |
| metadata | {"allowed-tools":"Read, Grep, Glob, Edit, Write, Bash, WebSearch, WebFetch","argument-hint":["feature-name"],"stability":"stable","content-hash":"sha256:202611f850f48c0ffb59ba553331765e55a322c2242757f1e2d31ce0a9340f3a","last-verified-cc-version":"1.0.34"} |
Python Implementation
Target: $ARGUMENTS
Creates focused, streamlined Python implementations following architect
specifications exactly. No over-engineering.
Python Standards
See references/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
- Create focused tests matching task complexity
- Run
make validate and fix all 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
Before completing any task:
make validate
All type checks, linting, and tests must pass.