Implement, refactor, test, and debug Python code with quality checks.
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This guide helps Python projects achieve consistent implementation, reliable tests, and robust type safety by outlining standards for tooling, formatting, and model usage.
Review the project against these standards and start applying them in your next change.
High-intent search queries and answers about installing and using this skill.
Configure pyproject.toml by defining ruff and mypy settings to enforce formatting and typing discipline. This setup ensures consistent implementation and robust type safety across your Python codebase.
Structure pytest testing by organizing fixtures and parameterization logically within your test suite. This approach provides comprehensive quality practices and reliable test coverage for your Python project.
Implement Pydantic models using Python 3.12 coding conventions and precise type hints. This ensures robust data validation and maintainable design patterns while reducing runtime issues.
Yes, the Python development guide supports both pytest and unittest frameworks. It provides comprehensive testing and quality practices, including fixtures, parameterization, and test organization for typical Python codebases.
Use ruff and mypy together to apply automated formatting and static type checking. This combination enforces strict coding conventions, identifies potential errors early, and maintains consistent project quality.
Handle runtime errors by applying robust error handling patterns and following packaging guidance. This reduces runtime issues and ensures maintainable design across typical Python codebases.
maksimzayatsGenerate pyproject.toml and Makefile with Ruff, mypy, and pytest for Specx services.
kartikfedRun Black, Ruff, Mypy, and Pytest checks on Python code.
TheBushidoCollectiveEnforce Python type safety with mypy, Protocols, TypedDict, and Generics.
billydeeii136Compute a weighted 0-10 codebase health score and track trends over time.
