ralph-wiggum

Interactive self-referential AI loops for iterative development. Claude works on the same task repeatedly, seeing its previous work, until completion.

Installation

npx claude-plugins install @anthropics/claude-code-plugins/ralph-wiggum

Contents

Folders: commands, hooks, scripts

Files: README.md

Documentation

Implementation of the Ralph Wiggum technique for iterative, self-referential AI development loops in Claude Code.

What is Ralph?

Ralph is a development methodology based on continuous AI agent loops. As Geoffrey Huntley describes it: “Ralph is a Bash loop” - a simple while true that repeatedly feeds an AI agent a prompt file, allowing it to iteratively improve its work until completion.

The technique is named after Ralph Wiggum from The Simpsons, embodying the philosophy of persistent iteration despite setbacks.

Core Concept

This plugin implements Ralph using a Stop hook that intercepts Claude’s exit attempts:

# You run ONCE:
/ralph-loop "Your task description" --completion-promise "DONE"

# Then Claude Code automatically:
# 1. Works on the task
# 2. Tries to exit
# 3. Stop hook blocks exit
# 4. Stop hook feeds the SAME prompt back
# 5. Repeat until completion

The loop happens inside your current session - you don’t need external bash loops. The Stop hook in hooks/stop-hook.sh creates the self-referential feedback loop by blocking normal session exit.

This creates a self-referential feedback loop where:

• The prompt never changes between iterations
• Claude’s previous work persists in files
• Each iteration sees modified files and git history
• Claude autonomously improves by reading its own past work in files

Quick Start

/ralph-loop "Build a REST API for todos. Requirements: CRUD operations, input validation, tests. Output <promise>COMPLETE</promise> when done." --completion-promise "COMPLETE" --max-iterations 50

Claude will:

• Implement the API iteratively
• Run tests and see failures
• Fix bugs based on test output
• Iterate until all requirements met
• Output the completion promise when done

Commands

/ralph-loop

Start a Ralph loop in your current session.

Usage:

/ralph-loop "<prompt>" --max-iterations <n> --completion-promise "<text>"

Options:

• --max-iterations <n> - Stop after N iterations (default: unlimited)
• --completion-promise <text> - Phrase that signals completion

/cancel-ralph

Cancel the active Ralph loop.

Usage:

/cancel-ralph

Prompt Writing Best Practices

1. Clear Completion Criteria

❌ Bad: “Build a todo API and make it good.”

✅ Good:

Build a REST API for todos.

When complete:
- All CRUD endpoints working
- Input validation in place
- Tests passing (coverage > 80%)
- README with API docs
- Output: <promise>COMPLETE</promise>

2. Incremental Goals

❌ Bad: “Create a complete e-commerce platform.”

✅ Good:

Phase 1: User authentication (JWT, tests)
Phase 2: Product catalog (list/search, tests)
Phase 3: Shopping cart (add/remove, tests)

Output <promise>COMPLETE</promise> when all phases done.

3. Self-Correction

❌ Bad: “Write code for feature X.”

✅ Good:

Implement feature X following TDD:
1. Write failing tests
2. Implement feature
3. Run tests
4. If any fail, debug and fix
5. Refactor if needed
6. Repeat until all green
7. Output: <promise>COMPLETE</promise>

4. Escape Hatches

Always use --max-iterations as a safety net to prevent infinite loops on impossible tasks:

# Recommended: Always set a reasonable iteration limit
/ralph-loop "Try to implement feature X" --max-iterations 20

# In your prompt, include what to do if stuck:
# "After 15 iterations, if not complete:
#  - Document what's blocking progress
#  - List what was attempted
#  - Suggest alternative approaches"

Note: The --completion-promise uses exact string matching, so you cannot use it for multiple completion conditions (like “SUCCESS” vs “BLOCKED”). Always rely on --max-iterations as your primary safety mechanism.

Philosophy

Ralph embodies several key principles:

1. Iteration > Perfection

Don’t aim for perfect on first try. Let the loop refine the work.

2. Failures Are Data

“Deterministically bad” means failures are predictable and informative. Use them to tune prompts.

3. Operator Skill Matters

Success depends on writing good prompts, not just having a good model.

4. Persistence Wins

Keep trying until success. The loop handles retry logic automatically.

When to Use Ralph

Good for:

• Well-defined tasks with clear success criteria
• Tasks requiring iteration and refinement (e.g., getting tests to pass)
• Greenfield projects where you can walk away
• Tasks with automatic verification (tests, linters)

Not good for:

• Tasks requiring human judgment or design decisions
• One-shot operations
• Tasks with unclear success criteria
• Production debugging (use targeted debugging instead)

Real-World Results

• Successfully generated 6 repositories overnight in Y Combinator hackathon testing
• One $50k contract completed for $297 in API costs
• Created entire programming language (“cursed”) over 3 months using this approach

Learn More

• Original technique: https://ghuntley.co

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Source

View on GitHub