Can it help with testing asynchronous Python code?

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

Is it suitable for Test-Driven Development (TDD)?

Yes, the patterns are designed to support TDD workflows by providing clear structures for defining test cases and assertions before writing implementation code.

Does this skill handle database and file system testing?

Absolutely. It provides patterns for managing database connections through fixtures and handling temporary files to ensure tests don't leave side effects.

Which testing framework does this skill focus on?

This skill primarily focuses on pytest, the industry standard for Python testing, while also utilizing the built-in unittest.mock library for isolation.

How does it handle external API dependencies?

It uses advanced mocking and monkeypatching techniques to simulate external service responses, allowing tests to run quickly and reliably without internet access.

Python Testing Patterns

Name: Python Testing Patterns
Author: arthur0824hao

byarthur0824hao

Security & Testing

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

About

Python Testing Patterns is a specialized Claude Code skill designed to streamline the creation of high-quality test suites. It provides ready-to-use implementation patterns for unit, integration, and functional testing, leveraging the power of pytest and the Arrange-Act-Assert (AAA) methodology. Whether you are mocking external dependencies, testing complex async operations, or setting up parameterized data-driven tests, this skill ensures your Python code is reliable, maintainable, and thoroughly verified according to industry standards.

Key Features

Parameterized testing templates for efficient data-driven test coverage.
Advanced mocking and patching strategies for isolating external APIs and services.
Standardized test structures using the Arrange-Act-Assert (AAA) pattern.
0 GitHub stars
Comprehensive pytest patterns including session, module, and function-scoped fixtures.
Support for complex scenarios like async operations, exception handling, and monkeypatching.

Use Cases

Implementing mock-heavy tests for external third-party integrations and databases.
Building automated unit and integration test suites for Python backend services.
Modernizing legacy test suites with efficient fixtures and parameterized test cases.

About

Key Features

Parameterized testing templates for efficient data-driven test coverage.
Advanced mocking and patching strategies for isolating external APIs and services.
Standardized test structures using the Arrange-Act-Assert (AAA) pattern.
0 GitHub stars
Comprehensive pytest patterns including session, module, and function-scoped fixtures.
Support for complex scenarios like async operations, exception handling, and monkeypatching.

Use Cases

Implementing mock-heavy tests for external third-party integrations and databases.
Building automated unit and integration test suites for Python backend services.
Modernizing legacy test suites with efficient fixtures and parameterized test cases.