Does this skill support testing asynchronous Python code?

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

How does it handle database testing?

It provides patterns for using fixtures to manage database connections and automated teardowns, ensuring strict test isolation.

What testing framework does this skill prefer?

This skill is primarily optimized for pytest, which is the industry-standard testing framework for modern Python development.

Can I use this for API integration testing?

Absolutely. The skill provides patterns for mocking the requests library and testing functional end-to-end API flows.

Python Testing Patterns

Name: Python Testing Patterns
Author: HermeticOrmus

byHermeticOrmus

Security & Testing

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

About

Python Testing Patterns equips Claude with a specialized framework for creating maintainable and comprehensive test suites for any Python project. It covers the entire testing lifecycle, from basic unit tests using the Arrange-Act-Assert (AAA) pattern to complex scenarios involving asynchronous operations, external API mocking, and environment variable manipulation. By utilizing standardized templates for fixtures, parameterization, and exception handling, this skill ensures that your Python code is verified against edge cases and regression-tested efficiently within your CI/CD pipeline.

Key Features

Data-driven testing patterns with pytest parameterization
Asynchronous code testing patterns for concurrent operations
Advanced pytest fixture management for modular setup and teardown
Standardized exception and error handling verification
Mocking external dependencies using unittest.mock and monkeypatch
0 GitHub stars

Use Cases

Refactoring legacy Python codebases with confidence using Test-Driven Development (TDD)
Building a comprehensive test suite for a new Python API or backend microservice
Simulating complex external system failures and database states using mocks and patches

About

Key Features

Data-driven testing patterns with pytest parameterization
Asynchronous code testing patterns for concurrent operations
Advanced pytest fixture management for modular setup and teardown
Standardized exception and error handling verification
Mocking external dependencies using unittest.mock and monkeypatch
0 GitHub stars

Use Cases

Refactoring legacy Python codebases with confidence using Test-Driven Development (TDD)
Building a comprehensive test suite for a new Python API or backend microservice
Simulating complex external system failures and database states using mocks and patches