kata-python-implement

To implement a Python backend feature, you provide requirements to generate typed domain entities, Protocol ports, Pydantic schemas, SQLAlchemy 2.0 async repositories, and FastAPI endpoints. This workflow ensures architecture-consistent delivery from domain design through the HTTP layer.

Structuring a Python backend feature involves mapping layers into typed dataclasses for domain logic and Protocol ports for boundaries. Pydantic schemas validate boundary models, ensuring strict typing and clean separation between domain logic and infrastructure adapters.

Yes, the workflow generates integration tests for SQLAlchemy 2.0 async repositories using a real database. It also produces pytest unit tests for domain logic and HTTP endpoint tests for FastAPI, ensuring comprehensive test-driven delivery.

You validate a Python feature implementation by running ruff for formatting and linting, and mypy for strict typing. The workflow enforces these quality gates alongside pytest pass criteria and safe error handling patterns before finalizing the code.

Yes, you can use this approach to add or change domain logic, persistence behavior, and FastAPI endpoints in an architecture-consistent way. It applies to scenarios requiring modifications to existing infrastructure adapters and boundary models.