What problem does it solve?
Python codebases often drift into inconsistent typing, unclear data model boundaries, weak test coverage, and toolchain fragmentation. This Skill gives the AI a consistent set of Python development conventions so edits follow your project's standards instead of ad-hoc habits.
Core Features & Use Cases
- Type Annotation Guidance: Prioritizes type information on public interfaces, core models, and complex return values, with explicit handling of
Any trade-offs.
- Pydantic & Data Model Boundaries: Keeps input/output models separated and validation logic clear without over-engineered validators.
- pytest Practices: Encourages behavior-expressive test names, parametrization, local fixtures, and coverage of edge cases and exception paths.
- Toolchain Consistency: Aligns with existing project tooling, especially
uv-based dependency, run, and test entry points.
- Use Case: When asking the AI to add a new API endpoint in a FastAPI-style project, the Skill ensures typed signatures, separated request/response models, and matching pytest coverage.
Quick Start
Ask the AI to edit or add Python code in this project while following the cc-python-dev conventions for typing, Pydantic models, and pytest coverage.
