python-dev

A Python development workflow enforces consistency by automating a four-phase process: research, plan, chronicle, implement, and verify. This standardizes planning, testing, and documentation to reduce onboarding friction and align codebases with best practices.

FastAPI projects use Pydantic models for strict type hints and asyncpg to establish concurrent database query patterns. A structured workflow enforces these conventions alongside clean imports and one-global exception handling to reduce bugs and boost maintainability.

Standardizing Python testing with pytest requires enforcing a comprehensive testing and linting regime within a shared development workflow. This approach integrates phase-specific verification commands and staff reviews to keep team codebases aligned and maintainable.

Yes, this Python workflow applies to projects using pandas and SQLAlchemy alongside FastAPI and Pydantic. It guides researchers and engineers through development phases while enforcing type-safety and naming conventions specific to data tooling.

Documenting decisions and rationale in chronicles and plan files supports scalable collaboration and knowledge transfer. This workflow practice ensures that changes are documented across development phases, enabling better onboarding and project continuity.