
Python Testing Patterns
Python-testing-patterns enables developers to implement comprehensive testing strategies using pytest, fixtures, mocking, and test-driven development practices for Python codebases. It provides guidance on unit tests, integration tests, test structure, coverage measurement, and test isolation to help engineers write robust test suites and maintain code quality. The skill addresses the problem of setting up effective testing infrastructure and following testing best practices across different test types and scenarios.
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- 17
- skills.sh installs
- 40K
- GitHub stars
- 42.4K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add wshobson/agents --skill python-testing-patterns --agent claude-codeInstalls into .claude/skills of the current project.
Files
Python Testing Patterns
Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices.
When to Use This Skill
- Writing unit tests for Python code
- Setting up test suites and test infrastructure
- Implementing test-driven development (TDD)
- Creating integration tests for APIs and services
- Mocking external dependencies and services
- Testing async code and concurrent operations
- Setting up continuous testing in CI/CD
- Implementing property-based testing
- Testing database operations
- Debugging failing tests
Core Concepts
1. Test Types
- Unit Tests: Test individual functions/classes in isolation
- Integration Tests: Test interaction between components
- Functional Tests: Test complete features end-to-end
- Performance Tests: Measure speed and resource usage
2. Test Structure (AAA Pattern)
- Arrange: Set up test data and preconditions
- Act: Execute the code under test
- Assert: Verify the results
3. Test Coverage
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Details
- Repository
- github.com/wshobson/agents
- Categories
- Testing & QA, Documentation, Python
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