How does it handle database state between tests?

It utilizes pytest fixtures for the 'Arrange' and 'Teardown' phases, ensuring each test has a clean database state and doesn't leak data into other tests.

What testing framework does this skill use?

The skill focuses on pytest, the modern industry standard for Python, along with the built-in unittest.mock library for dependency isolation.

Does this skill support asynchronous Python code?

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

Can I use this for integration testing or only unit tests?

It covers the full spectrum of testing, including unit tests for isolated logic, integration tests for component interaction, and functional tests for end-to-end features.

Python Testing Patterns

Name: Python Testing Patterns
Author: HermeticOrmus

byHermeticOrmus

Seguridad y Pruebas

Implement robust testing strategies using pytest, mocking, and fixtures to ensure high-quality Python applications.

Acerca de

This skill provides an extensive framework for implementing best-in-class testing practices in Python. It equips Claude with the patterns needed to write unit, integration, and functional tests using pytest, manage complex setups with fixtures, and handle external dependencies via mocking. Whether you are practicing test-driven development (TDD), testing asynchronous code, or configuring CI/CD pipelines, this skill ensures your test suites are isolated, maintainable, and comprehensive.

Características Principales

Data-driven testing through advanced parameterization
Comprehensive mocking and patching of external dependencies
Specialized patterns for async code and exception handling
0 GitHub stars
Pytest implementation for unit, integration, and functional tests
Advanced fixture management for efficient setup and teardown

Casos de Uso

Developing new Python features using Test-Driven Development (TDD) workflows
Simulating external API failures and edge cases using sophisticated mocking
Refactoring legacy codebases with a safety net of automated unit tests

Acerca de

Características Principales

Data-driven testing through advanced parameterization
Comprehensive mocking and patching of external dependencies
Specialized patterns for async code and exception handling
0 GitHub stars
Pytest implementation for unit, integration, and functional tests
Advanced fixture management for efficient setup and teardown

Casos de Uso

Developing new Python features using Test-Driven Development (TDD) workflows
Simulating external API failures and edge cases using sophisticated mocking
Refactoring legacy codebases with a safety net of automated unit tests