Does it support data-driven testing?

Absolutely. It provides detailed examples for test parametrization, including handling multiple parameter combinations and expected failures.

How do I organize my test suite with this skill?

The skill recommends a standardized project layout and provides configuration templates for pyproject.toml and conftest.py to keep your test code organized.

How does this skill help with pytest fixtures?

It provides standardized patterns for basic, scoped, teardown, and factory fixtures to help you manage test dependencies efficiently and avoid code duplication.

Can I use this skill for mocking API calls?

Yes, the skill includes specialized guidance for using the monkeypatch fixture to mock functions, environment variables, and dictionary items for isolated testing.

Pytest Python Testing

Name: Pytest Python Testing
Author: alijilani-dev

byalijilani-dev

Security & Testing

Facilitates the creation and management of comprehensive pytest suites for Python projects using advanced fixtures, mocking, and parametrization.

About

The Pytest Python Testing skill empowers developers to build robust, maintainable test suites for Python applications. It provides domain-specific guidance on implementing core pytest features, including advanced fixture patterns, test parametrization for edge-case coverage, and mocking with monkeypatch. By automating test discovery and providing standardized patterns for markers and assertions, this skill ensures that testing remains a seamless part of the development workflow, whether you are setting up a new project or debugging complex failures in an existing codebase.

Key Features

Mocking and dynamic patching with monkeypatch
Comprehensive exception and floating-point assertion patterns
Test parametrization for efficient data-driven testing
Standardized project structure and configuration templates
Advanced fixture implementation and scope management
0 GitHub stars

Use Cases

Refactoring repetitive test code into reusable, scoped fixtures
Setting up a standardized pytest configuration for a new Python microservice
Implementing complex mocks for external API calls and environment variables

About

Key Features

Mocking and dynamic patching with monkeypatch
Comprehensive exception and floating-point assertion patterns
Test parametrization for efficient data-driven testing
Standardized project structure and configuration templates
Advanced fixture implementation and scope management
0 GitHub stars

Use Cases

Refactoring repetitive test code into reusable, scoped fixtures
Setting up a standardized pytest configuration for a new Python microservice
Implementing complex mocks for external API calls and environment variables