super-ralph-wiggum

super-ralph-wiggum is a skill for Claude Code, Codex from tdimino/claude-code-minoan. It costs 76 tokens per session (2,874 once invoked), scanned A, original, MIT.

An autonomous work loop for Claude Code that repeats a task across multiple iterations while keeping progress in files and Git history. It can work from a product requirements document, which describes the desired end state.

In plain words
What is it for?
Use it for feature development, improving test coverage, fixing lint errors, writing documentation, creating datasets, and migrating frameworks.
Why use it?
It helps continue large coding tasks after each individual run instead of requiring the developer to direct every next step. Progress tracking, tests, types, and linting provide checks along the way.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code; mentions AGENTS.md.

Good fit Use it for feature development, improving test coverage, fixing lint errors, writing documentation, creating datasets, and migrating frameworks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tdimino/claude-code-minoan/super-ralph-wiggum
Install

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.

Any agent
npx skills add tdimino/claude-code-minoan --skill super-ralph-wiggum
Clone the repo
git clone --depth 1 https://github.com/tdimino/claude-code-minoan

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for super-ralph-wiggum

README.md
[![agentmods](https://agentmods.dev/badge/skills/tdimino/claude-code-minoan/super-ralph-wiggum/github.svg)](https://agentmods.dev/skills/tdimino/claude-code-minoan/super-ralph-wiggum)
Your own site
<a href="https://agentmods.dev/skills/tdimino/claude-code-minoan/super-ralph-wiggum"><img src="https://agentmods.dev/badge/skills/tdimino/claude-code-minoan/super-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.

agentmods 80×15 button for super-ralph-wiggum

Your own site · 80×15
<a href="https://agentmods.dev/skills/tdimino/claude-code-minoan/super-ralph-wiggum"><img src="https://agentmods.dev/badge/skills/tdimino/claude-code-minoan/super-ralph-wiggum.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,874 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Tool Misuse · line 347
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00076 $0.02874
Opus 5 $0.00038 $0.01437
Sonnet 5 $0.00015 $0.00575
Haiku 4.5 $0.00008 $0.00287

Measured 8d ago against content hash 1ea2a8c27ef5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

super-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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/init-progress.sh, scripts/setup-ralph-loop.sh, scripts/stop-hook.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/planning-productivity/super-ralph-wiggum/SKILL.md · 356 lines

How it starts

The opening of the file, as written. The whole thing — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Super Ralph Wiggum

Autonomous iteration loops for Claude Code. Run the same prompt repeatedly until task completion, with context persisting through files and git history.

The Ralph Philosophy

The agent chooses the task, not you.

You define the end state. Ralph figures out how to get there.

With multi-phase plans, a human writes a new prompt at the start of each phase. With Ralph, the agent picks what to work on next from your PRD. You describe the destination. Ralph navigates.

The 11 Tips (Quick Reference)

Based on AI Hero's definitive guide:

# Tip Key Insight
1 Ralph Is A Loop Same prompt, multiple iterations
2 Start HITL, Then AFK Learn → Trust → Let go
3 Define The Scope Explicit stop conditions prevent infinite loops
4 Track Progress progress.txt bridges context windows
5 Use Feedback Loops Types, tests, linting as guardrails
6 Take Small Steps One logical change per commit
7 Prioritize Risky Tasks Architecture first, quick wins last
8 Define Software Quality Tell Ralph what kind of repo this is
9 Use Docker Sandboxes Essential for AFK safety
10 Pay To Play HITL still valuable without AFK
11 Make It Your Own Alternative loop types, task sources

See @references/tips-and-tricks.md for detailed guidance on each tip.

Quick Start

To start a Ralph loop with a template:

Run the super-ralph-wiggum skill with the test-coverage template, max 20 iterations

To start with a PRD file:

Run super-ralph-wiggum with feature-prd template using ./prd.json

Available Templates

Template Use Case Default Iterations
test-coverage Improve test coverage to target % 30
feature-prd Implement features from PRD file 20
lint-fix Fix all lint errors incrementally 30
docs-generation Generate documentation for modules 25
dataset-generation Generate training data samples 50
migration Migrate to new framework/version 40
entropy-loop Reverse software entropy (dead code, smells) 30
duplication-loop Eliminate duplicate code 25

Read the full file on GitHub · 356 lines

Changes

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.

  1. 8d ago First seen · 356 lines · 76 tokens per session scan A 1ea2a8c27ef5

Subscribe to this mod's changes

super-ralph-wiggum is a skill published in the GitHub repository tdimino/claude-code-minoan (41 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 2,874 once invoked, about $0.0004 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 2026-09-03.