What is the default coverage standard?

The skill enforces a minimum coverage standard of 80% with branch coverage enabled to ensure deep logic validation.

What testing patterns does it promote?

It promotes the Arrange-Act-Assert (AAA) pattern for clear test structure and utilizes shared fixtures via conftest.py for efficient test data management.

Can it help with non-functional testing?

Yes, it includes specialized workflows for security testing, performance benchmarking, and end-to-end system validation.

Which testing frameworks does this skill support?

This skill is primarily optimized for pytest-based projects using the uv package manager, but it includes patterns applicable to unittest and general Python testing.

Automated Testing & QA

Name: Automated Testing & QA
Author: williaby

bywilliaby

Security & Testing

Generates, reviews, and executes comprehensive pytest-based testing suites to ensure code reliability, security, and performance.

About

This skill streamlines the entire testing lifecycle for Python projects, specifically targeting pytest-based environments. It provides automated test generation and code quality reviews while supporting advanced validation techniques like mutation testing and property-based testing. By enforcing a minimum 80% coverage standard and implementing industry-standard patterns like Arrange-Act-Assert (AAA), this skill helps developers build robust RAG applications and complex systems with confidence in their unit, integration, and end-to-end test suites.

Key Features

Automated test case generation for Python modules
Standardized test organization and fixture patterns
Comprehensive coverage reporting including branch coverage
Specialized workflows for security, performance, and E2E testing
Integration with mutation and property-based testing tools
0 GitHub stars

Use Cases

Generating initial test suites for new Python microservices
Implementing performance benchmarks and security audits for RAG systems
Auditing existing test code for quality and coverage gaps

About

Key Features

Automated test case generation for Python modules
Standardized test organization and fixture patterns
Comprehensive coverage reporting including branch coverage
Specialized workflows for security, performance, and E2E testing
Integration with mutation and property-based testing tools
0 GitHub stars

Use Cases

Generating initial test suites for new Python microservices
Implementing performance benchmarks and security audits for RAG systems
Auditing existing test code for quality and coverage gaps