Can I use this skill for testing asynchronous Python code?

Yes, it includes specific patterns for testing async functions using pytest-asyncio and concurrent operation handling.

How does it handle external API dependencies?

It utilizes mocking and monkeypatching techniques to simulate API responses, allowing you to test your logic without making actual network requests.

What testing framework does this skill focus on?

This skill is primarily optimized for the pytest framework, though it also covers native Python modules like unittest.mock for mocking and patching.

Does it help with database testing?

Absolutely. The skill provides patterns for setting up and tearing down database connections using fixtures to ensure test isolation.

Python Testing Patterns

Name: Python Testing Patterns
Author: acaprino

byacaprino

Security & Testing

Implements comprehensive testing strategies using pytest, mocking, and fixtures for robust Python applications.

About

This skill provides a deep integration of Python testing best practices into Claude's workflow, specializing in the pytest framework. It guides users through the Arrange-Act-Assert (AAA) pattern, sophisticated mocking techniques using unittest.mock, and advanced scenarios like async testing and property-based verification. Whether you are building unit tests for individual functions or architecting complex integration suites for APIs and databases, this skill ensures your code maintains high quality and reliable coverage through standardized, maintainable testing patterns.

Key Features

0 GitHub stars
Parameterized testing for broad input coverage and edge cases
Async testing patterns for concurrent and I/O-bound operations
Advanced mocking and patching strategies for external dependencies
Exception handling and error state verification techniques
Standardized pytest configurations and fixture implementations

Use Cases

Implementing Test-Driven Development (TDD) for new Python features
Debugging complex logic through isolated unit and integration tests
Setting up comprehensive test suites for legacy Python codebases

About

Key Features

0 GitHub stars
Parameterized testing for broad input coverage and edge cases
Async testing patterns for concurrent and I/O-bound operations
Advanced mocking and patching strategies for external dependencies
Exception handling and error state verification techniques
Standardized pytest configurations and fixture implementations

Use Cases

Implementing Test-Driven Development (TDD) for new Python features
Debugging complex logic through isolated unit and integration tests
Setting up comprehensive test suites for legacy Python codebases