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name: python-testing description: | Python testing with pytest, fixtures, mocking, and TDD workflows.

Triggers: pytest, unit tests, test fixtures, mocking, TDD, test suite, coverage, test-driven development, testing patterns, parameterized tests

Use when: writing unit tests, setting up test suites, implementing TDD, configuring pytest, creating fixtures, async testing

DO NOT use when: evaluating test quality - use pensive:test-review instead. DO NOT use when: infrastructure test config - use leyline:pytest-config.

Consult this skill for Python testing implementation and patterns. category: testing tags: [python, testing, pytest, tdd, test-automation, quality-assurance] tools: [test-analyzer, coverage-reporter, test-runner] usage_patterns:

• testing-implementation
• test-suite-setup
• test-refactoring
• ci-cd-integration complexity: intermediate estimated_tokens: 900 progressive_loading: true modules:
• unit-testing
• fixtures-and-mocking
• test-infrastructure
• testing-workflows
• test-quality
• async-testing

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Python Testing Hub

Testing patterns for pytest, fixtures, mocking, and TDD.

Quick Start

1. Assess Testing Needs: Identify required testing patterns
2. Configure pytest: Set up test infrastructure
3. Implement Tests: Apply patterns from modules
4. Run Coverage: Verify test quality

When to Use

• Writing unit tests for Python code
• Setting up test suites and infrastructure
• Implementing test-driven development (TDD)
• Creating integration tests for APIs and services
• Mocking external dependencies and services
• Testing async code and concurrent operations

Modules

This skill provides focused modules for different testing aspects:

Core Testing

• unit-testing - Fundamental unit testing patterns with pytest including AAA pattern, basic test structure, and exception testing
• fixtures-and-mocking - Advanced pytest fixtures, parameterized tests, and mocking patterns for external dependencies
• async-testing - Testing asynchronous Python code with pytest-asyncio including async fixtures and concurrent operation testing

Infrastructure & Workflow

• test-infrastructure - Project configuration for pytest including pyproject.toml setup, test directory structure, and coverage configuration
• testing-workflows - Running tests, CI/CD integration, and automated testing workflows

Quality

• test-quality - Best practices, anti-patterns to avoid, and quality criteria for Python tests

Progressive Loading

Load modules based on your needs:

• Start with unit-testing for fundamental patterns
• Add fixtures-and-mocking for advanced test setup
• Include test-infrastructure when setting up new projects
• Use testing-workflows for CI/CD integration
• Reference test-quality for best practices
• Apply async-testing for asynchronous code

Exit Criteria

• Tests follow AAA pattern
• Coverage meets project threshold (≥80%)
• All tests independent and reproducible
• CI/CD integration configured
• Clear test naming and organization
• No anti-patterns present
• Fixtures used appropriately
• Mocking only at boundaries