| name | python |
| description | Default Python stack for Lambda: uv + Astral tools, typed code, schemas, and Hypothesis. |
Python Workflow
Use this skill when working on Python projects or adding Python support.
Tooling baseline
- Use
uvfor environments, dependency management, and running commands. - Prefer Astral tooling for quality gates:
rufffor lint/format andtyfor type checking. - Favor strict typing everywhere; avoid
Anyunless the boundary truly requires it.
Typing and schemas
- Type every function signature (params + return) and keep types narrow.
- Use Pydantic models for inputs, outputs, and configuration schemas.
- Prefer typed collections and
typing_extensionsfor newer typing features.
Testing
- Write tests with
pytestand property tests withhypothesiswhen behavior is stateful or rule-based. - Add coverage checks (e.g., pytest-cov) and keep coverage green for new code paths.
Packaging
- Structure the code as a releasable PyPI package.
- Use a
pyproject.tomlwith build metadata, versioning, and asrc/layout. - Ensure imports and entrypoints work when installed from a wheel.
Quality gates
- For pre-commit hooks, run formatting last so lint fixes land before formatting.
- Keep linting, type checking, and tests passing before closing work.