How many iterations should I set for my task?

The skill provides recommendations based on complexity: simple tasks usually need 15-30 iterations, while complex projects may require 100-200 to ensure thorough implementation and testing.

What is a 'Completion Promise'?

A completion promise is a specific, verifiable string or condition that signals to the autonomous loop that the task has been successfully and correctly completed.

How does the orchestrator choose a prompt generator?

It analyzes indicators in your task description: 'Fix' or 'repair' triggers Single-Task; 'CRUD' or multiple features trigger Multi-Task; 'from scratch' triggers Project; and 'analyze' or 'audit' triggers Research.

Can I use this for non-coding research tasks?

Yes, the 'ralph-prompt-research' generator is specifically designed for analysis, investigations, and documentation tasks that require synthesis and evidence-based conclusions.

What is the Ralph Wiggum technique?

It is an autonomous loop technique for AI coding based on the philosophy that failures are deterministic and can be fixed through structured, automated iteration.

Ralph Prompt Builder

Name: Ralph Prompt Builder
Author: adaptationio

byadaptationio

Developer Tools

Orchestrates and generates optimized prompts for the Ralph Wiggum autonomous loop technique to automate coding tasks.

About

The Ralph Prompt Builder is a master orchestrator designed to streamline the creation of high-quality, iterative prompts for Claude Code. By analyzing your specific task requirements—whether they involve a quick bug fix, a complex multi-step feature, a full-scale project, or deep technical research—it automatically routes your request to the most effective specialized generator. This skill ensures every prompt includes critical components like binary success criteria, verification commands, and TDD patterns, helping you leverage the philosophy that coding failures are deterministic and fixable through structured autonomous iterations.

Key Features

Built-in guidance for defining verifiable completion promises
Automated routing to Single-Task, Multi-Task, Project, or Research generators
Intelligent task classification across four specialized categories
Complexity-based recommendations for maximum iteration settings
Standardized TDD-focused prompt structures for higher success rates
0 GitHub stars

Use Cases

Conducting deep-dive security audits or architectural research with evidence-based deliverables.
Generating reliable prompts for autonomous bug fixing and refactoring existing codebases.
Planning and executing multi-phase feature implementations with clear checkpoints.

About

Key Features

Built-in guidance for defining verifiable completion promises
Automated routing to Single-Task, Multi-Task, Project, or Research generators
Intelligent task classification across four specialized categories
Complexity-based recommendations for maximum iteration settings
Standardized TDD-focused prompt structures for higher success rates
0 GitHub stars

Use Cases

Conducting deep-dive security audits or architectural research with evidence-based deliverables.
Generating reliable prompts for autonomous bug fixing and refactoring existing codebases.
Planning and executing multi-phase feature implementations with clear checkpoints.