Python Backend Quality

Core Standard

Python projects need explicit packaging, typed boundaries, fast tests, and predictable runtime configuration. Prefer boring structure over clever dynamic behavior.

Required Checks

• Packaging: pyproject.toml, lockfile (uv.lock or equivalent), clear scripts, Python version pin, and no ad-hoc global installs.
• Boundaries: Pydantic/dataclasses/TypedDicts for request, config, tool, API, database, and external-service inputs.
• Types: meaningful annotations, mypy/pyright/basedpyright where practical, no broad Any at trust boundaries.
• Tests: pytest or equivalent with unit tests for pure logic and integration tests for API/auth/database boundaries.
• Quality tools: Ruff format/lint, deterministic imports, CI command that runs lint, typecheck, and tests.
• Runtime: env validation, secret redaction, structured logging, graceful startup/shutdown, and clear error models.
• Frameworks: FastAPI dependencies and routers stay thin; Django views/serializers/services are separated; Flask routes validate inputs before business logic.
• Data: migrations are explicit, sessions/transactions are scoped, and database errors do not leak raw internals.

Output

For each finding, include the file evidence, risk, smallest practical fix, and tests to run. Do not claim production-ready Python unless packaging, tests, typing, env handling, and deployment checks are evidenced.

Common Mistakes

• Treating requirements.txt alone as a full project contract.
• Putting business logic directly in FastAPI route functions, Django views, or Flask handlers.
• Swallowing exceptions with except Exception: pass or returning None without context.
• Logging raw request bodies, tokens, connection strings, prompts, or provider errors.