What are fixture factories in this context?

The skill demonstrates how to create fixtures that return functions (factories), enabling you to generate multiple objects with custom parameters within a single test case while managing cleanup.

Can this skill help with code coverage reporting?

Yes, it integrates pytest-cov to track code coverage, generate terminal or HTML reports, and enforce minimum coverage percentages to ensure code quality.

How do I run tests in parallel using this skill?

The skill provides patterns for pytest-xdist, allowing you to use commands like 'pytest -n auto' to automatically distribute tests across all available CPU cores.

Does this support testing asynchronous Python code?

Yes, it includes specific patterns and configurations for pytest-asyncio to handle async functions, async fixtures, and event loop management.

Advanced Pytest Testing

Name: Advanced Pytest Testing
Author: laurigates

bylaurigates

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Seguridad y Pruebas

Implements sophisticated Python testing patterns including scoped fixtures, parallel execution, and async support for robust application suites.

Acerca de

This skill equips Claude with the expertise to architect and maintain high-quality Python test infrastructures using advanced pytest capabilities. It covers sophisticated patterns such as fixture factories, multi-scope lifecycle management, and indirect parametrization. Additionally, it provides specialized configuration for critical plugins like pytest-cov for coverage, pytest-xdist for parallelization, and pytest-asyncio for modern async applications, ensuring your test suite remains maintainable, fast, and comprehensive as your project scales.

Características Principales

Advanced fixture lifecycle and factory pattern management
Automated coverage reporting and enforcement with pytest-cov
Parallel test execution configuration using pytest-xdist
Asynchronous testing support via pytest-asyncio
3 GitHub stars
Complex test parametrization and custom marker implementation

Casos de Uso

Architecting scalable testing frameworks for large-scale Python projects
Optimizing CI/CD pipelines with parallel execution and coverage thresholds
Implementing advanced mocking and dependency injection for unit testing

Acerca de

Características Principales

Advanced fixture lifecycle and factory pattern management
Automated coverage reporting and enforcement with pytest-cov
Parallel test execution configuration using pytest-xdist
Asynchronous testing support via pytest-asyncio
3 GitHub stars
Complex test parametrization and custom marker implementation

Casos de Uso

Architecting scalable testing frameworks for large-scale Python projects
Optimizing CI/CD pipelines with parallel execution and coverage thresholds
Implementing advanced mocking and dependency injection for unit testing