How does this skill improve my Python tests?

It enforces industry-standard patterns like AAA (Arrange-Act-Assert) and ensures proper use of pytest fixtures and parameterization for cleaner code.

Is it compatible with standard Python projects?

Yes, it is designed for any Python project that utilizes pytest as its primary testing framework, including web frameworks like FastAPI or Flask.

Can it help with testing asynchronous code?

Yes, it includes specific patterns for using pytest-asyncio to test concurrent operations and asynchronous functions effectively.

Does it support mocking external APIs?

Absolutely, it provides structured patterns for using unittest.mock and patch to simulate external service responses without making real network calls.

Python Testing Patterns

Name: Python Testing Patterns
Author: HermeticOrmus

byHermeticOrmus

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Seguridad y Pruebas

Implements professional testing strategies for Python applications using pytest, advanced mocking, and test-driven development best practices.

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This Claude Code skill provides a comprehensive toolkit for building resilient Python test suites. It guides Claude to implement robust testing patterns ranging from basic unit tests using the AAA (Arrange, Act, Assert) pattern to advanced techniques like async testing, database mocking, and parameterized validation. By leveraging pytest fixtures and monkeypatching, it ensures test isolation and clean execution environments, helping developers improve code coverage and catch regressions early in the development lifecycle.

Características Principales

Async testing support for concurrent operations and API services
Parameterized testing for efficient multi-scenario validation
Advanced fixture management for complex setup and teardown logic
Comprehensive mocking strategies using unittest.mock and monkeypatch
Standardized pytest configuration and AAA test structure implementation
2 GitHub stars

Casos de Uso

Setting up a new test suite for a Python project with industry-standard patterns
Mocking external API dependencies and database operations for CI/CD pipelines
Refactoring legacy code by adding unit tests and achieving higher coverage

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Características Principales

Async testing support for concurrent operations and API services
Parameterized testing for efficient multi-scenario validation
Advanced fixture management for complex setup and teardown logic
Comprehensive mocking strategies using unittest.mock and monkeypatch
Standardized pytest configuration and AAA test structure implementation
2 GitHub stars

Casos de Uso

Setting up a new test suite for a Python project with industry-standard patterns
Mocking external API dependencies and database operations for CI/CD pipelines
Refactoring legacy code by adding unit tests and achieving higher coverage