How does the multi-agent workflow improve code quality?

By using separate agents for testing and implementation, the skill ensures the implementation agent doesn't see the internal reasoning of the tests, forcing it to write robust code that truly solves the specification.

How does it help achieve 95% code coverage?

The skill runs coverage reports, identifies specific uncovered lines or branches, and spawns targeted agents to generate the precise tests needed to close those gaps.

What is test gaming in AI development?

Test gaming occurs when an AI agent writes implementation code that specifically satisfies test cases without actually fulfilling the underlying business logic or handling real-world edge cases.

What test frameworks are supported by this skill?

Multi-AI Testing is framework-agnostic and works with major tools including Jest, Vitest, pytest, Playwright, and Cypress.

What are property-based tests in this context?

Property-based tests verify that specific invariants of your code (like 'a token must always contain a userID') hold true across a wide range of randomized inputs, catching bugs that standard unit tests miss.

Multi-AI Testing & TDD

Name: Multi-AI Testing & TDD
Author: adaptationio

byadaptationio

0•

Seguridad y Pruebas

Implements rigorous test-driven development workflows using independent multi-agent verification to ensure high code coverage and prevent test gaming.

Multi-AI Testing is a specialized Claude Code skill designed to automate and enforce high-quality software testing through a robust multi-agent architecture. By separating the roles of test generation, implementation, and verification, it prevents the common AI pitfall of 'test gaming,' where code is overfitted just to pass specific scripts. The skill facilitates a complete TDD lifecycle—including property-based testing and edge case discovery—aiming for a minimum 80% coverage gate and a 95% target, while significantly reducing maintenance through self-healing test patterns.

Características Principales

01Comprehensive test generation for Unit, Integration, E2E, and Property-based suites

02Dynamic coverage validation with automated gap identification and filling

03Independent multi-agent verification to prevent test gaming and overfitting

04AI-powered edge case discovery and self-healing test adaptation

05Automated TDD workflows including Test-Fail-Implement-Pass-Verify cycles

060 GitHub stars

Casos de Uso

01Verifying that AI-generated implementations fulfill requirements through independent logic audits

02Enforcing strict Test-Driven Development for mission-critical application logic

03Rapidly increasing test coverage for legacy codebases to 95% thresholds

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adaptationio/skrillz multi-ai-testing

For use in Claude.ai and ChatGPT

Download Skill