Does this skill support testing for asynchronous applications?

Yes, it includes specific patterns for testing async code using pytest-asyncio, including async fixtures and concurrent operation testing.

What testing framework does this skill focus on?

This skill primarily focuses on pytest, the industry-standard testing framework for Python, incorporating its fixtures, parameterization, and plugin ecosystem.

How does it handle external API dependencies?

The skill provides patterns for using unittest.mock and patch decorators to simulate API responses, allowing you to test your code in isolation without making real network calls.

Can I use this for database integration testing?

Absolutely. It includes patterns for setting up database fixtures that handle connection setup, transaction management, and automatic cleanup after tests run.

Python Testing Patterns

Name: Python Testing Patterns
Author: HermeticOrmus

byHermeticOrmus

Security & Testing

Implements robust Python testing suites using pytest, advanced mocking, and test-driven development best practices.

About

Python Testing Patterns is a specialized skill designed to help developers build reliable, maintainable software through comprehensive testing strategies. It provides standardized implementation patterns for unit, integration, and functional testing using the pytest framework. By leveraging features like dependency mocking, parameterized testing, and async test handling, this skill enables Claude to assist in creating high-coverage test suites that ensure code quality and facilitate seamless CI/CD integration. It is an essential tool for developers looking to implement Test-Driven Development (TDD) or improve the stability of complex Python applications.

Key Features

Standardized pytest implementations for unit and integration tests
0 GitHub stars
Async testing support for concurrent operations and services
Advanced fixture patterns for efficient setup and teardown logic
Mocking strategies for external APIs and complex dependencies
Parameterized testing to increase coverage while reducing code duplication

Use Cases

Implementing Test-Driven Development (TDD) workflows in new Python projects
Refactoring legacy codebases by building a comprehensive safety net of tests
Simulating external service failures using advanced mocking and exception testing

About

Key Features

Standardized pytest implementations for unit and integration tests
0 GitHub stars
Async testing support for concurrent operations and services
Advanced fixture patterns for efficient setup and teardown logic
Mocking strategies for external APIs and complex dependencies
Parameterized testing to increase coverage while reducing code duplication

Use Cases

Implementing Test-Driven Development (TDD) workflows in new Python projects
Refactoring legacy codebases by building a comprehensive safety net of tests
Simulating external service failures using advanced mocking and exception testing