Python Coding Guidelines

**Judgement:** High-quality, practical Python guideline skill for modern codebases. It is well-suited as a default coding standard for many projects, especially when consistency and idiomatic Python are priorities. **Strengths:** - Enforces clear, modern style guidelines (PEP 8, Black-compatible line length, naming, imports). - Bakes in basic quality gates: syntax validation, running tests, and formatting checks. - Promotes Pythonic patterns (comprehensions, context managers, EAFP, f-strings, dataclasses, pathlib) and discourages common anti-patterns (mutable defaults, bare except, globals, star imports). - Encourages recent Python versions (3.10+) and uv-based dependency management where available. **Risks / Limitations:** - Focuses mainly on style and basic correctness; it is not a full security, performance, or architecture guideline. - Some recommendations (shell-based commands, uv usage) assume a POSIX-like environment and may not directly apply on all systems or CI setups. - Does not deeply cover advanced topics like async patterns, packaging best practices, or complex testing strategies (property-based tests, integration/e2e). **Recommended scenarios:** - Establishing or enforcing a baseline coding standard for Python services and libraries using Python 3.10+. - Guiding AI-assisted code generation and refactoring to keep output idiomatic, testable, and stylistically consistent. - Serving as a review checklist for teams aiming to raise basic code quality without adopting a very heavy process.