What is convergence in the Ralph Wiggum workflow?

Convergence is reached when the project achieves 10 consecutive clean iterations with no new issues, all tests passing, and category scores exceeding 95%.

How does the parallel agent swarm work?

The skill spawns 3-5 parallel agents, each assigned a unique review style and focus area to prevent overlap and ensure comprehensive codebase coverage.

Why is TDD enforced by the skill?

Enforcing TDD ensures that every fix is verifiable and prevents regressions, guaranteeing that the autonomous agent doesn't introduce new bugs while fixing existing ones.

Does this skill work for projects other than game development?

Yes. While it includes game-specific personas, its core review styles (Security, Performance, SOLID) and TDD loops are applicable to any software project.

Ralph Wiggum Autonomous TDD Loop

Name: Ralph Wiggum Autonomous TDD Loop
Author: adaptationio

byadaptationio

Seguridad y Pruebas

Automates the software development lifecycle using a parallel agent swarm and rigorous test-driven development to ensure codebase stability and high-quality convergence.

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Ralph Wiggum v2 is an advanced autonomous development framework designed to manage complex codebases through a swarm of specialized AI agents. By employing a strict test-first methodology and rotating through diverse review personas—ranging from performance analysts to security auditors—this skill systematically identifies bugs, writes failing tests, and implements minimal fixes. It is uniquely suited for large-scale quality improvement loops, game development projects, and comprehensive refactoring tasks where the goal is to reach a 'converged' state of zero crashes and high test coverage.

Características Principales

Parallel Agent Swarm with 30+ specialized review personas
Strict TDD enforcement requiring failing tests before any code implementation
Comprehensive state management for long-running autonomous development tasks
Dynamic category evolution and quality scoring system
0 GitHub stars
Automatic convergence detection based on consecutive clean iterations

Casos de Uso

Continuous automated security and performance auditing for complex systems
Large-scale codebase quality improvement and technical debt reduction
Autonomous game development and logic refinement loops

Acerca de

Características Principales

Parallel Agent Swarm with 30+ specialized review personas
Strict TDD enforcement requiring failing tests before any code implementation
Comprehensive state management for long-running autonomous development tasks
Dynamic category evolution and quality scoring system
0 GitHub stars
Automatic convergence detection based on consecutive clean iterations

Casos de Uso

Continuous automated security and performance auditing for complex systems
Large-scale codebase quality improvement and technical debt reduction
Autonomous game development and logic refinement loops