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Found 34 Skills
Pytest testing patterns for Python. Trigger: When writing or refactoring pytest tests (fixtures, mocking, parametrize, markers). For Prowler-specific API/SDK testing conventions, also use prowler-test-api or prowler-test-sdk.
Запуск тестов проекта: unit, integration. Определение типа проекта и выбор стратегии.
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.
Improve test coverage in the OpenAI Agents Python repository: run `make coverage`, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before writing tests.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Test-driven development workflow enforcement for Python and React projects. Use when the user requests TDD, test-first development, or red-green-refactor methodology. Enforces strict cycle: write ONE failing test -> implement minimum code to pass -> refactor while green -> repeat. Applies to both backend (pytest) and frontend (Testing Library). Changes agent behavior to write tests before code. Does NOT provide testing patterns (use pytest-patterns or react-testing-patterns for how to write tests).
pytest - Python's most powerful testing framework with fixtures, parametrization, plugins, and framework integration for FastAPI, Django, Flask
Generate pytest test cases for Python functions and classes
Provides comprehensive guidance for pytest testing framework including test writing, fixtures, parametrization, mocking, and plugins. Use when the user asks about pytest, needs to write Python tests, use pytest fixtures, or configure pytest for Python projects.
pytest Python testing framework with fixtures. Use for Python testing.
Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.
Use when writing, fixing, editing, or refactoring Python tests. Enforces Clean Code principles—fast tests, boundary coverage, one assert per test.