How does it help with flaky tests?

The skill identifies non-deterministic patterns like race conditions, arbitrary sleep calls, and time-dependency, offering refactoring strategies to inject time or use proper asynchronous handling.

Can it detect overmocking?

Yes, it analyzes test files to identify when internal utilities, value objects, or too many dependencies are mocked, which often leads to fragile tests that don't verify real behavior.

What testing frameworks does this skill support?

It provides specific expertise and configuration examples for Vitest (JavaScript/TypeScript) and pytest (Python), though the general testing principles apply across all modern languages and frameworks.

Does it provide code coverage guidance?

It offers specific commands and configuration snippets for Vitest and pytest-cov to help you visualize coverage, find untested paths, and set up automated coverage thresholds.

Test Quality Analysis

Name: Test Quality Analysis
Author: laurigates

bylaurigates

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Security & Testing

Analyzes test suites to detect anti-patterns, improve coverage, and eliminate flaky tests for more reliable software delivery.

About

The Test Quality Analysis skill provides expert-level diagnostics for your testing suites, helping you identify common pitfalls like overmocking, fragile selectors, and non-deterministic (flaky) tests. By applying best practices like the AAA pattern and providing specific guidance for both JavaScript (Vitest) and Python (pytest) environments, it enables developers to maintain high-performance, maintainable test code that truly validates application behavior rather than implementation details.

Key Features

Optimizes test performance by identifying slow setup/teardown and expensive dependencies
Enforces the AAA (Arrange, Act, Assert) pattern for clearer and more maintainable tests
Analyzes code coverage to find critical untested business logic and error paths
3 GitHub stars
Identifies and provides architectural fixes for flaky or non-deterministic tests
Detects common test smells like overmocking and implementation-detail testing

Use Cases

Refactoring legacy test suites that have become slow, fragile, or hard to maintain over time
Automating the detection of coverage gaps in critical payment, authentication, or security workflows
Performing deep code reviews to ensure newly written tests meet high quality and reliability standards

About

Key Features

Optimizes test performance by identifying slow setup/teardown and expensive dependencies
Enforces the AAA (Arrange, Act, Assert) pattern for clearer and more maintainable tests
Analyzes code coverage to find critical untested business logic and error paths
3 GitHub stars
Identifies and provides architectural fixes for flaky or non-deterministic tests
Detects common test smells like overmocking and implementation-detail testing

Use Cases

Refactoring legacy test suites that have become slow, fragile, or hard to maintain over time
Automating the detection of coverage gaps in critical payment, authentication, or security workflows
Performing deep code reviews to ensure newly written tests meet high quality and reliability standards