Can I use this for testing asynchronous code?

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

Does it help with code coverage?

Yes, the skill provides guidance on measuring what code is exercised by tests and helps you identify and address untested code paths.

Can I mock external APIs with this skill?

Absolutely. It provides detailed patterns for using unittest.mock and monkeypatch to simulate external services, environment variables, and API responses.

What testing framework does this skill support?

This skill primarily supports pytest, which is the industry-standard testing framework for Python due to its powerful fixture system and simplicity.

Is this skill suitable for Test-Driven Development (TDD)?

Yes, the skill is specifically designed to support TDD workflows by providing the structures needed to write failing tests before implementing features.

Python Testing Patterns

Name: Python Testing Patterns
Author: HermeticOrmus

byHermeticOrmus

Seguridad y Pruebas

Implements comprehensive Python testing strategies using pytest, fixtures, and mocking to ensure high-quality, reliable codebases.

Acerca de

This skill provides a comprehensive framework for writing, organizing, and executing Python tests using industry-standard tools like pytest. It guides users through the entire testing lifecycle—from unit and integration tests to mocking external services and testing complex asynchronous operations. By enforcing the AAA (Arrange, Act, Assert) pattern and leveraging advanced features like parameterization and custom fixtures, it helps developers build maintainable test suites that improve code coverage and catch regressions early in the development process.

Características Principales

Advanced fixture management for efficient setup and teardown
Data-driven testing via parameterized test cases
0 GitHub stars
Specialized patterns for testing async code and exception handling
Mocking external dependencies using unittest.mock and monkeypatch
Pytest implementation for unit, integration, and functional testing

Casos de Uso

Implementing Test-Driven Development (TDD) workflows directly within Claude Code
Building a robust unit test suite for a new Python library or application
Mocking API responses and database connections in complex integration tests

Acerca de

Características Principales

Advanced fixture management for efficient setup and teardown
Data-driven testing via parameterized test cases
0 GitHub stars
Specialized patterns for testing async code and exception handling
Mocking external dependencies using unittest.mock and monkeypatch
Pytest implementation for unit, integration, and functional testing

Casos de Uso

Implementing Test-Driven Development (TDD) workflows directly within Claude Code
Building a robust unit test suite for a new Python library or application
Mocking API responses and database connections in complex integration tests