testing-workflow

A zero-warnings testing workflow uses pytest combined with linting, type checking, and formatting tools to block commits until all checks pass without warnings. This Skill configures pytest with coverage targets (minimum 80%, 100% on critical paths), mocking practices, and organized test structure using Arrange-Act-Assert patterns to ensure pristine code before integration.

Organize pytest tests using Arrange-Act-Assert patterns, parametrization, and fixtures for maintainability. Set minimum coverage targets at 80% overall and 100% on critical paths. This Skill enforces those standards across unit, integration, and end-to-end tests while ensuring all quality checks pass without warnings.

Yes, this Skill guides running targeted tests during development for fast iteration, then executing a full test suite (via `make check`) before committing. This balances speed and thoroughness, catching regressions and quality issues while preventing them from entering your codebase.

This Skill applies language-agnostic quality controls including linting, type checking, and formatting requirements alongside pytest. It treats all warnings as errors and blocks commits until these checks pass, maintaining consistent code standards across your project.

A zero-warnings policy requires resolving all issues immediately rather than deferring them, which can slow initial development. However, it prevents technical debt accumulation and rework, making it suitable for projects prioritizing code quality and maintainability over rapid prototyping.