What problem does it solve? Python codebases drift into inconsistent typing, legacy imports, and weak error handling when multiple contributors or AI agents write code without shared language-level rules. This Skill provides concrete, enforceable standards for modern Python so generated and reviewed code stays consistent. ## Core Features & Use Cases - Typing discipline: Mandates builtin generics, explicit annotations at public boundaries, and bans Any and bare object in favor of TypeAlias, NewType, Protocol, or Pydantic v2 models. - Import and packaging rules: Enforces explicit relative imports, grouped import order, __all__ public surfaces, and uv/pyproject.toml tooling without parallel pip workflows. - Error handling and testing patterns: Requires fail-fast validation, semantic custom exceptions, structured logging, and pytest/pytest-mock conventions with shared fixtures. - Use Case: When an AI agent writes a new Python module, it applies these rules automatically — using pathlib instead of os.path, Google-style docstrings, keyword-only arguments, and then validating with ruff, mypy, and pytest before completion. ## Quick Start Apply the ankyr-python rules to write a typed, ruff-clean Python module with Google docstrings and pytest tests.
