Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/fstandhartinger/ralph-wiggumnpx agentmods add skills/fstandhartinger/ralph-wiggum/ralph-wiggumWrote 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/fstandhartinger/ralph-wiggum/ralph-wiggum)<a href="https://agentmods.dev/skills/fstandhartinger/ralph-wiggum/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/fstandhartinger/ralph-wiggum/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/fstandhartinger/ralph-wiggum/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/fstandhartinger/ralph-wiggum/ralph-wiggum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
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 | $0.00048 | $0.01369 |
| Opus 5 | $0.00024 | $0.00685 |
| Sonnet 5 | $0.00010 | $0.00274 |
| Haiku 4.5 | $0.00005 | $0.00137 |
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 11d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph Wiggum
Autonomous AI coding with spec-driven development
What is Ralph Wiggum?
Ralph Wiggum combines Geoffrey Huntley's iterative bash loop with spec-driven development for fully autonomous AI-assisted software development.
The key insight: Fresh context each iteration. Each loop starts a new agent process with a clean context window, preventing context overflow and degradation.
When to Use This Skill
Use Ralph Wiggum when:
- You have multiple specifications/features to implement
- You want the AI to work autonomously through tasks
- You need consistent, verifiable completion of acceptance criteria
- You want to avoid context window problems in long sessions
How It Works
┌─────────────────────────────────────────────────────────────┐
│ RALPH LOOP │
├─────────────────────────────────────────────────────────────┤
│ Loop 1: Pick spec A → Implement → Test → Commit → DONE │
│ Loop 2: Pick spec B → Implement → Test → Commit → DONE │
│ Loop 3: Pick spec C → Implement → Test → Commit → DONE │
│ ... │
│ │
│ Each iteration = Fresh context window │
│ Shared state = Files on disk (specs, plan, history) │
└─────────────────────────────────────────────────────────────┘
Installation
Quick Install (via Skill Installers)
# Using Vercel's add-skill
npx add-skill fstandhartinger/ralph-wiggum
# Using OpenSkills
openskills install fstandhartinger/ralph-wiggum
Full Setup (Recommended)
For full Ralph Wiggum setup with constitution and interview:
# Tell your AI agent:
"Set up Ralph Wiggum using https://github.com/fstandhartinger/ralph-wiggum"
The agent will guide you through a lightweight, pleasant setup:
- Quick Setup (~1 min) — Create directories, download scripts
- Project Interview — Focus on your vision and goals (not tech details)
- Constitution — Create a guiding document for all sessions
- Next Steps — Clear guidance on creating specs and starting Ralph
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.
- 11d ago First seen · 181 lines · 48 tokens per session scan A 0c870df2aa95
ralph-wiggum is a skill published in the GitHub repository fstandhartinger/ralph-wiggum (293 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 1,369 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-30.
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