ralph-loop

Run iterative prompt-response cycles with configurable limits and state persistence.

Updated Aug 23, 2026

Search Tags:#prompt engineering #code generation #self-improvement #iterative development #ai loop #continuous refinement

Ready to run

npx skills add https://github.com/usadamasa/claude-config --skill ralph-loop-usadamasa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ralph-loop
Source: https://github.com/usadamasa/claude-config/tree/main/dotclaude/skills/ralph-loop
Command: npx skills add https://github.com/usadamasa/claude-config --skill ralph-loop-usadamasa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables a continuous, iterative development loop where the AI repeatedly refines its output based on a given prompt, leading to self-improvement over multiple cycles.

Core Features & Use Cases

Iterative Refinement: Automatically re-prompts the AI with its own output to improve quality.
Configurable Iterations: Set a maximum number of iterations or run indefinitely.
Completion Promise: Define specific criteria for the AI to meet before exiting the loop.
Use Case: Use this for tasks requiring high precision or creative exploration, like generating complex code snippets or drafting detailed technical documentation, where successive refinement is key.

Quick Start

Start an iterative development loop with the prompt "Write a Python function to sort a list".

Frequently Asked Questions about ralph-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up an iterative self-improvement loop for AI prompt execution?▼

An iterative self-improvement loop repeatedly feeds AI-generated output back into the prompt to refine results. You configure iteration limits and completion promises to automatically manage repetitive prompt-response cycles for continuous refinement.

What is continuous refinement in AI code generation and how does it work?▼

Continuous refinement in AI code generation automatically re-prompts the AI with its own previous output to improve quality. It works by managing repetitive prompt-response cycles, persisting state via local files across multiple iterations.

Can I define specific completion criteria for an AI loop before it stops?▼

Yes, you can define a completion promise to set specific criteria the AI must meet before exiting the iterative loop. This ensures the self-improvement cycle stops only when your precision or quality requirements are satisfied.

Does the iterative development loop support multi-line prompts for complex code generation?▼

The iterative development loop fully supports multi-line prompts for complex code generation tasks. It manages repetitive prompt-response cycles and persists state via local files, enabling detailed technical documentation and successive refinement.

What are the limitations of running an indefinite iterative AI loop?▼

Running an indefinite iterative AI loop lacks a maximum iteration limit, meaning continuous refinement continues until the completion promise is met. Without clear completion criteria, this self-improvement cycle may run indefinitely without converging on a final output.