What is a mutation score and why does it matter?

A mutation score measures the percentage of intentionally introduced code defects (mutants) that your tests successfully detect. This skill helps you reach a target score of 60% or higher.

Does this work with TDD?

Absolutely. It is designed to assist in the Red/Green/Refactor cycle by providing test scaffolding based on function requirements and signatures.

Which programming languages are supported?

The skill provides specific workflows, templates, and tool integrations for Go, Python, and TypeScript/JavaScript.

What makes this different from standard unit test generation?

This skill uses the 'Test Quality Pyramid,' combining unit tests with property-based testing—which research shows finds 50x more bugs—and mutation testing to verify test effectiveness.

Can I use this for existing legacy code?

Yes, it includes a workflow specifically for improving coverage by identifying uncovered lines and generating targeted tests to provide a regression safety net.

Test Generation Workflow

Name: Test Generation Workflow
Author: pagerguild

bypagerguild

Security & Testing

Automates the creation of high-quality test suites using property-based testing and mutation analysis to identify critical edge cases.

About

This skill implements a robust, multi-layered testing strategy for Claude Code, moving beyond simple unit tests to include property-based and mutation testing patterns. It analyzes code signatures to generate comprehensive unit tests for happy paths and edge cases, then applies property-based techniques to validate invariants across diverse inputs. By integrating mutation testing, it provides a rigorous feedback loop to ensure test quality actually catches bugs, helping developers achieve high-confidence code coverage across Go, Python, and TypeScript environments.

Key Features

Native support for Go (Gopter), Python (Hypothesis), and TypeScript (Fast-check)
0 GitHub stars
Targeted coverage improvement tools for legacy code and TDD workflows
Automated analysis of function signatures, invariants, and edge cases
Multi-layered test generation including unit, property-based, and mutation tests
Mutation testing validation to ensure tests effectively catch introduced bugs

Use Cases

Identifying and closing coverage gaps in existing codebases
Generating comprehensive test scaffolding for new feature implementations
Validating the effectiveness of test suites using mutation scoring

About

Key Features

Native support for Go (Gopter), Python (Hypothesis), and TypeScript (Fast-check)
0 GitHub stars
Targeted coverage improvement tools for legacy code and TDD workflows
Automated analysis of function signatures, invariants, and edge cases
Multi-layered test generation including unit, property-based, and mutation tests
Mutation testing validation to ensure tests effectively catch introduced bugs

Use Cases

Identifying and closing coverage gaps in existing codebases
Generating comprehensive test scaffolding for new feature implementations
Validating the effectiveness of test suites using mutation scoring