Can I use it to improve the coverage of an existing project?

Yes, it can scan your existing codebase, identify low-coverage areas, and generate the necessary tests to meet your quality targets.

Will it find edge cases I might have missed?

Yes, the skill specifically analyzes inputs to suggest tests for boundary values, empty states, and invalid data types to improve error handling.

Can it help me implement Test-Driven Development (TDD)?

Absolutely. The skill is designed to activate before implementation or during the creation of new features to help you write tests first.

Does this skill support both Python and JavaScript?

Yes, the skill includes specialized patterns and examples for popular frameworks in both Python (pytest) and JavaScript (Vitest/Jest).

How does it handle external dependencies like databases?

It provides implementation patterns for mocking and stubbing external services, ensuring your tests remain fast, independent, and repeatable.

Test-Driven Quality

Name: Test-Driven Quality
Author: rysweet

byrysweet

•

Security & Testing

Automates the generation and improvement of comprehensive test suites following TDD principles to ensure high-quality, bug-free code.

About

The Test-Driven Quality skill transforms Claude into a specialized QA engineer that prioritizes Test-Driven Development (TDD) and the 'Test Pyramid' philosophy. It helps developers maintain high standards of code reliability by automatically identifying untested code paths, generating unit and integration tests, and suggesting edge cases that are often overlooked. Whether you are implementing new features, refactoring legacy code, or fixing regressions, this skill provides the structure and confidence needed to ship production-grade software with minimal technical debt.

Key Features

Automated generation of unit, integration, and end-to-end tests
Intelligent code analysis to identify and fill coverage gaps
16 GitHub stars
Edge case and boundary value identification for robust error handling
Built-in support for mocking, stubbing, and parameterized testing patterns
Standardized naming conventions and directory structures for test suites

Use Cases

Generating comprehensive regression tests after fixing a critical bug
Implementing a full suite of unit tests for a new API module
Increasing test coverage on a legacy codebase before a major refactor

About

Key Features

Automated generation of unit, integration, and end-to-end tests
Intelligent code analysis to identify and fill coverage gaps
16 GitHub stars
Edge case and boundary value identification for robust error handling
Built-in support for mocking, stubbing, and parameterized testing patterns
Standardized naming conventions and directory structures for test suites

Use Cases

Generating comprehensive regression tests after fixing a critical bug
Implementing a full suite of unit tests for a new API module
Increasing test coverage on a legacy codebase before a major refactor