What is a Ralph Wiggum loop?

A Ralph Wiggum loop is a continuous iteration pattern that directs an AI agent to work on a task repeatedly until a specific completion signal or objective criteria is achieved.

How does this skill manage API costs?

It includes token usage tracking and iteration logging, allowing users to set budgets and monitor cumulative consumption throughout the autonomous cycle.

How do I prevent the loop from running forever?

The skill mandates safety measures including a Max Iterations Cap (recommended 10-50) and 'stuck detection' which terminates the loop if no progress is detected after several attempts.

What is a 'Completion Promise'?

A Completion Promise is a unique exact-match string (like COMPLETE ) that the AI must output to signal it has verified the task is finished.

Can I use Ralph Wiggum for UI/UX design?

It is not recommended for tasks requiring subjective human judgment. It is most effective for tasks with binary success states, such as passing unit tests or clearing linter errors.

Ralph Wiggum Continuous Iteration

Name: Ralph Wiggum Continuous Iteration
Author: v1truv1us

byv1truv1us

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Productivity & Workflow

Implements a continuous iteration loop pattern for autonomous development and automated verification tasks.

About

The Ralph Wiggum skill enables AI agents to work autonomously on well-defined tasks by following a systematic iteration pattern. Designed for scenarios like TDD implementation, bug fixing, and overnight development, it allows Claude to persistently refine code until specific, objective completion criteria are met. By establishing safety limits, token caps, and verifiable success signals, this skill transforms Claude into an autonomous developer capable of handling repetitive debugging and feature implementation cycles without constant human intervention.

Key Features

Pre-built prompt templates for complex development workflows
2 GitHub stars
Objective completion promises with safety caps and token monitoring
Automated TDD and bug-fixing iteration loops
Stuck-state detection to prevent infinite loops and token waste
Autonomous 'overnight' development mode for long-running tasks

Use Cases

Resolving complex bugs that require multiple debugging and verification cycles
Automating Test-Driven Development (TDD) until all test suites pass green
Executing autonomous feature implementation during non-working hours

About

Key Features

Pre-built prompt templates for complex development workflows
2 GitHub stars
Objective completion promises with safety caps and token monitoring
Automated TDD and bug-fixing iteration loops
Stuck-state detection to prevent infinite loops and token waste
Autonomous 'overnight' development mode for long-running tasks

Use Cases

Resolving complex bugs that require multiple debugging and verification cycles
Automating Test-Driven Development (TDD) until all test suites pass green
Executing autonomous feature implementation during non-working hours