Does this skill support frameworks other than pytest?

While it focuses primarily on pytest patterns, it also incorporates standard library tools like unittest.mock and general testing principles applicable across the Python ecosystem.

How does this help with Test-Driven Development (TDD)?

It provides the structural guidance and code boilerplate needed to write failing tests first, allowing you to define requirements before implementing the logic.

Can I use this for testing asynchronous code?

Yes, the skill includes specific patterns for testing async functions and concurrent operations using pytest-asyncio and asyncio mocking techniques.

Can it help me mock external APIs during testing?

Absolutely. It includes patterns for using patch and MagicMock to simulate API responses, ensuring your tests remain fast, isolated, and deterministic without live network calls.

Python Testing Patterns

Name: Python Testing Patterns
Author: as4584

byas4584

Security & Testing

Implements robust Python testing strategies using pytest, fixtures, and mocking to ensure code reliability and high coverage.

About

The Python Testing Patterns skill equips Claude with specialized knowledge to architect and implement comprehensive test suites using the industry-standard pytest framework. It covers essential testing paradigms including the Arrange-Act-Assert (AAA) pattern, fixture-based setup and teardown, parameterization for data-driven tests, and advanced mocking of external dependencies. Whether you are building unit tests for simple functions, managing complex async operations, or establishing TDD workflows, this skill provides the implementation patterns and best practices needed to maintain high-quality, bug-free Python codebases.

Key Features

Sophisticated mocking techniques for isolating external services and APIs
Advanced fixture management for efficient setup and teardown logic
0 GitHub stars
Comprehensive pytest framework implementation and best practices
Async and concurrent operation testing for modern Python applications
Parameterized testing patterns for high-volume data validation

Use Cases

Developing test-driven unit and integration tests for Python microservices
Automating validation of complex data inputs using parameterized test suites
Mocking database connections and network responses to isolate business logic

About

Key Features

Sophisticated mocking techniques for isolating external services and APIs
Advanced fixture management for efficient setup and teardown logic
0 GitHub stars
Comprehensive pytest framework implementation and best practices
Async and concurrent operation testing for modern Python applications
Parameterized testing patterns for high-volume data validation

Use Cases

Developing test-driven unit and integration tests for Python microservices
Automating validation of complex data inputs using parameterized test suites
Mocking database connections and network responses to isolate business logic