Is this skill suitable for Test-Driven Development (TDD)?

Yes, the skill is designed to guide the Red-Green-Refactor cycle and emphasizes the AAA (Arrange, Act, Assert) pattern for clean test structure.

How does this skill handle external API dependencies?

It provides detailed guidance on using unittest.mock and monkeypatch to simulate external service responses, ensuring tests remain fast and isolated.

Does this skill support the pytest framework?

Yes, it is fully optimized for pytest, providing patterns for fixtures, parameterization, markers, and common pytest plugins.

Can I use this skill to test asynchronous Python code?

Absolutely. It includes specific patterns for testing async functions and concurrent operations using pytest-asyncio and related tools.

Python Testing Patterns

Name: Python Testing Patterns
Author: EngineerWithAI

byEngineerWithAI

Security & Testing

Implements comprehensive Python testing suites using pytest, advanced mocking techniques, and industry-standard testing patterns.

About

The Python Testing Patterns skill equips Claude with specialized knowledge for architecting robust test suites in Python. It provides standardized implementation patterns for unit, integration, and functional testing using the pytest framework, including sophisticated use of fixtures, dependency mocking, and parameterization. This skill is essential for developers looking to implement Test-Driven Development (TDD) or improve code reliability through automated testing of complex scenarios like async operations, database interactions, and external API integrations.

Key Features

Comprehensive pytest implementation strategies for unit and integration tests
Advanced fixture management for efficient test setup and teardown
Specialized patterns for testing async code and database operations
0 GitHub stars
Dependency isolation using unittest.mock and monkeypatching
Parameterized testing for robust edge case and data-driven coverage

Use Cases

Isolating and debugging complex logic through modular unit testing and mocking
Implementing strict Test-Driven Development (TDD) workflows in AI-assisted coding
Building automated test suites for high-performance Python APIs and microservices

About

Key Features

Comprehensive pytest implementation strategies for unit and integration tests
Advanced fixture management for efficient test setup and teardown
Specialized patterns for testing async code and database operations
0 GitHub stars
Dependency isolation using unittest.mock and monkeypatching
Parameterized testing for robust edge case and data-driven coverage

Use Cases

Isolating and debugging complex logic through modular unit testing and mocking
Implementing strict Test-Driven Development (TDD) workflows in AI-assisted coding
Building automated test suites for high-performance Python APIs and microservices