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 S3YED/appie-kit --skill ralph-wiggumgit clone --depth 1 https://github.com/S3YED/appie-kitWrote 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/s3yed/appie-kit/ralph-wiggum)<a href="https://agentmods.dev/skills/s3yed/appie-kit/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/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/s3yed/appie-kit/ralph-wiggum"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/ralph-wiggum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.00698 |
| Opus 5 | $0.00015 | $0.00349 |
| Sonnet 5 | $0.00006 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
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 9d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph Wiggum Skill
Purpose: Autonomous coding loop technique for complex build tasks.
What it does: Runs an AI coding agent (Claude Code, Codex, etc.) in iterative loops — each iteration picks ONE spec/task, implements it, verifies, commits, and loops until done.
Based on: Geoffrey Huntley's Ralph Wiggum technique — fresh context each loop, persistent state via git/files.
Setup
Already installed at: ~/.openclaw/skills/ralph-wiggum/
Structure:
ralph-wiggum/
├── SKILL.md
├── scripts/
│ ├── ralph-loop.sh # Claude Code loop
│ └── ralph-loop-codex.sh # OpenAI Codex loop
└── specs/ # Spec files (you create these)
Ralph Wiggum commit: d205125cc33745116cce22d883417461174dcde5
How to Use
1. Create a Spec
Write a spec file in specs/ with:
- What to build
- Clear acceptance criteria
- Completion signal
2. Start the Loop
# Claude Code (recommended)
~/.openclaw/skills/ralph-wiggum/scripts/ralph-loop.sh
# Or OpenAI Codex
~/.openclaw/skills/ralph-wiggum/scripts/ralph-loop-codex.sh
# Limit iterations
~/.openclaw/skills/ralph-wiggum/scripts/ralph-loop.sh 20
3. How the Loop Works
Each iteration:
1. Ralph reads specs/ and picks highest priority incomplete spec
2. AI implements it completely
3. AI verifies acceptance criteria + runs tests
4. AI outputs "DONE" only if criteria pass
5. Bash loop checks for DONE → next iteration
6. Context cleared, fresh start
4. Exit Signals
<promise>DONE</promise>→ spec complete, next iteration<promise>ALL_DONE</promise>→ all specs complete, loop exits- Bash checks for DONE phrase in output
Key Concepts
Specs: Markdown files in specs/ with acceptance criteria. Lower number = higher priority.
Context Reset: Each iteration starts fresh — no accumulated confusion.
State Persistence: Progress stored in git commits + files.
Completion Verification: AI only outputs DONE when ALL criteria verified + tests pass.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 95 lines · 30 tokens per session scan A 1aae5ca76e59
ralph-wiggum is a skill published in the GitHub repository S3YED/appie-kit (8 stars, last pushed 13d ago), licensed MIT. It adds 30 tokens to every session and 698 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 2026-08-31.
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