How does this skill prevent infinite loops or token waste?

The skill utilizes mandatory maximum iteration caps, progress monitoring, and stuck-detection strategies to terminate the process if progress stalls or costs exceed limits.

When should I avoid using the Ralph Wiggum workflow?

Avoid using it for tasks requiring subjective human judgment, such as UI/UX design choices, or for highly ambiguous tasks without clear success metrics.

What is a Ralph Wiggum loop?

It is a continuous iteration pattern that keeps an AI agent working on a specific task until it reaches a verifiable completion signal or 'promise'.

What is a 'Completion Promise'?

A Completion Promise is a unique, exact-match string (e.g., COMPLETE ) that the agent is instructed to output only when all technical criteria are fully satisfied.

Can I use this for overnight automation?

Yes, it is specifically designed for overnight progress by allowing the AI to iterate through well-defined task backlogs while the developer is away.

Ralph Wiggum Continuous Iteration

Name: Ralph Wiggum Continuous Iteration
Author: v1truv1us

byv1truv1us

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Productivity & Workflow

Automates repetitive development tasks through a continuous iteration loop with objective completion criteria and safety guards.

About

This skill implements the Ralph Wiggum pattern, a specialized execution loop designed for autonomous development within the Claude Code ecosystem. It enables Claude to handle tasks like TDD implementation, bug fixing, and refactoring by repeatedly attempting solutions, running verifications, and self-correcting until objective success criteria are met. By integrating safety caps and completion promises, it allows for reliable, often overnight, progress on complex but well-defined coding objectives without requiring constant manual intervention.

Key Features

Objective completion signals using unique exact-match 'promises'
2 GitHub stars
Automated TDD and bug-fixing iteration loops
Configurable safety caps and max-iteration limits to prevent token waste
Integrated progress monitoring and stuck-state detection
Support for autonomous overnight development runs

Use Cases

Running test-driven development (TDD) until all unit tests pass green
Resolving complex bugs that require multiple iterative debugging and fix cycles
Executing large-scale refactoring tasks with automated linting and test verification

About

Key Features

Objective completion signals using unique exact-match 'promises'
2 GitHub stars
Automated TDD and bug-fixing iteration loops
Configurable safety caps and max-iteration limits to prevent token waste
Integrated progress monitoring and stuck-state detection
Support for autonomous overnight development runs

Use Cases

Running test-driven development (TDD) until all unit tests pass green
Resolving complex bugs that require multiple iterative debugging and fix cycles
Executing large-scale refactoring tasks with automated linting and test verification