Can I use this for testing asynchronous Python code?

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

Does this skill support frameworks other than pytest?

While the primary focus is on pytest patterns due to its industry dominance, the core testing concepts and mocking strategies can be adapted for other frameworks like unittest.

How does this help with test-driven development (TDD)?

It provides the structural boilerplate and logic patterns needed to write failing tests first, ensuring your implementation meets requirements from the start.

Does it handle mocking for external dependencies?

Yes, it provides detailed patterns for using unittest.mock and monkeypatch to isolate your code from environment variables, filesystems, and external services.

Python Testing Patterns

Name: Python Testing Patterns
Author: Microck

byMicrock

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Security & Testing

Implements comprehensive testing strategies using pytest, fixtures, and mocking to ensure robust and maintainable Python codebases.

About

Python Testing Patterns is a specialized skill that enables Claude to design and implement sophisticated test suites using the pytest framework. It provides standardized patterns for the Arrange-Act-Assert (AAA) structure, complex fixture management across various scopes, and data-driven parameterized testing. This skill is essential for developers looking to implement Test-Driven Development (TDD), mock external API dependencies, or verify asynchronous operations. By following these industry-standard patterns, users can achieve higher code quality, better test coverage, and more reliable CI/CD pipelines.

Key Features

Data-driven validation using parameterized test markers
Advanced pytest fixture implementation with session and module scoping
81 GitHub stars
Comprehensive mocking and patching strategies for external dependencies
Specialized patterns for testing asynchronous and concurrent code
Robust exception handling and error state verification

Use Cases

Building a comprehensive unit and integration test suite for a new Python service
Mocking third-party API responses to simulate edge cases and network failures
Automating database testing with isolated setup and teardown fixtures

About

Key Features

Data-driven validation using parameterized test markers
Advanced pytest fixture implementation with session and module scoping
81 GitHub stars
Comprehensive mocking and patching strategies for external dependencies
Specialized patterns for testing asynchronous and concurrent code
Robust exception handling and error state verification

Use Cases

Building a comprehensive unit and integration test suite for a new Python service
Mocking third-party API responses to simulate edge cases and network failures
Automating database testing with isolated setup and teardown fixtures