How does this skill help with complex test setups?

It provides patterns for using Pytest fixtures with specific scopes (function, class, module, or session) and utilizing conftest.py to make setups available across entire directories.

How does it handle testing multiple scenarios for one function?

It utilizes the parametrize feature to run multiple input/output sets through the same test logic, ensuring that failures are isolated and easy to debug.

Can it help with testing asynchronous Python code?

Yes, it includes guidance for using @pytest.mark.asyncio and AsyncMock to properly test asynchronous functions and coroutines.

Does it support test coverage analysis?

Absolutely, it provides configuration snippets for pyproject.toml and command-line instructions to measure line and branch coverage effectively.

Where should I place my mock patches?

Following Pytest best practices, this skill guides you to patch objects where they are used (the target module) rather than where they are defined.

Pytest Testing

Name: Pytest Testing
Author: eyadsibai

byeyadsibai

安全与测试

Streamlines the creation, organization, and execution of robust Python test suites using the Pytest framework.

关于

This skill empowers developers to master Python testing by providing expert guidance on Pytest's most powerful features, including fixture management, advanced parametrization, and sophisticated mocking strategies. It bridges the gap between basic unit tests and comprehensive integration suites by enforcing best practices like Arrange-Act-Assert, efficient fixture scoping, and optimal test organization. Whether you are debugging a failing test or configuring branch coverage in a CI/CD pipeline, this skill ensures your Python code remains reliable and maintainable.

主要功能

Data-driven testing implementation using Pytest Parametrize
Standardized test suite organization (Unit, Integration, E2E)
0 GitHub stars
Advanced mocking and patching for isolated component testing
Comprehensive coverage configuration and command-line optimization
Expert fixture management with yield-based cleanup and scoping

使用场景

Implementing automated coverage thresholds and branch testing in CI pipelines
Refactoring repetitive test logic into clean, parameterized test cases
Designing reusable test fixtures for database connections and API clients

关于

主要功能

Data-driven testing implementation using Pytest Parametrize
Standardized test suite organization (Unit, Integration, E2E)
0 GitHub stars
Advanced mocking and patching for isolated component testing
Comprehensive coverage configuration and command-line optimization
Expert fixture management with yield-based cleanup and scoping

使用场景

Implementing automated coverage thresholds and branch testing in CI pipelines
Refactoring repetitive test logic into clean, parameterized test cases
Designing reusable test fixtures for database connections and API clients