pytest_skill

Overview

This skill provides concise pytest testing patterns and best practices for writing Python tests. It covers test structure, fixtures, mocking, parametrization, markers, async testing, and common pytest commands. Use it to structure reliable, maintainable test suites and speed up test authoring.

How this skill works

It outlines common pytest idioms and examples you can copy into your codebase: class-based and function tests, reusable fixtures with scope and teardown, and shared conftest.py fixtures. It demonstrates mocking with unittest.mock, parameterized test cases with pytest.mark.parametrize, and marker usage for selective runs. It also lists practical pytest CLI flags for running and debugging tests.

When to use it

• When creating unit and integration tests for Python projects
• When you need reusable test setup or teardown via fixtures
• When isolating external dependencies with mocks or MagicMock
• When validating multiple inputs using parametrized tests
• When organizing or filtering tests with markers or running async tests

Best practices

• Keep tests small and focused: one assertion intent per test where practical
• Use fixtures in conftest.py for shared setup to avoid duplication
• Prefer parametrization over loops to get individual test reports per case
• Mock external services (HTTP, payments) to avoid flaky or slow tests
• Use marker names (slow, integration) and pytest -m to control test runs
• Clean up resources in fixture teardown (yield) and choose fixture scope wisely

Example use cases

• Unit test a service by patching external clients with patch and MagicMock
• Create authenticated test clients via fixtures that force login and teardown DB session fixtures after tests
• Validate input handling with @pytest.mark.parametrize for many edge cases
• Mark long-running database or network tests with @pytest.mark.integration and exclude them in fast CI runs
• Run tests in parallel with pytest -n auto and gather coverage with pytest --cov=src