Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add myths-labs/muse --skill ralph-wiggumgit clone --depth 1 https://github.com/myths-labs/museWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/myths-labs/muse/ralph-wiggum)<a href="https://agentmods.dev/skills/myths-labs/muse/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/ralph-wiggum/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/myths-labs/muse/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/ralph-wiggum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01234 |
| Opus 5 | $0.00000 | $0.00617 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
ralph-wiggum scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph Wiggum Plugin
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 180 lines · 0 tokens per session scan A c654dd0c552e
ralph-wiggum is a skill published in the GitHub repository myths-labs/muse (32 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,234 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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