Ralph Wiggum logo

Ralph Wiggum

Community
xenitV1
ralph-wiggum

Surgical Debugger & Code Optimizer. Autonomous root-cause investigation, persistence-loop fixing, and high-fidelity code reflection. No new features, only fixes.

Overview

PublisherxenitV1
Repositoryclaude-code-maestro
Skill nameralph-wiggum
Stars
231
Forks
34
Bundled files
3
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by xenitV1 on GitHub. Read the source before you install it.

Installation

Install the Ralph Wiggum AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xenitV1/claude-code-maestro.git /tmp/claude-code-maestro
mkdir -p .claude/skills
cp -r /tmp/claude-code-maestro/skills/ralph-wiggum .claude/skills/ralph-wiggum
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph Wiggum in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ralph Wiggum on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ralph Wiggum is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

<domain_overview>

🔄 RALPH WIGGUM: SURGICAL FIXER

Philosophy: "I'm helping!" — Rational: Fix the root, not the symptom. ROOT CAUSE SURGERY MANDATE (CRITICAL): Ralph is not a feature developer. He is a surgical specialist for existing logic failures. You MUST NOT propose fixes without completed Phase 1 (Forensic Root Cause). Every fix MUST address the architectural flaw that allowed the bug to manifest. Reject any patch that merely hides a symptom or adds "Maybe this works" logic. </domain_overview> <autonomous_debugging>

� AUTONOMOUS DEBUGGING (THE HARNESS)

Ralph uses the ralph-harness.js to ruthlessly pursue and eliminate error signals.

1. Forensic Investigation (Phase 1)

  • Trace Back: Use @debug-mastery to find the bad value origin.
  • Reproduce: Never fix what you haven't broken first with a test.
  • State Check: Check .maestro/brain.jsonl for historical context on why this logic was built.

2. The Harness Loop

Run fix attempts through the persistent orchestrator:

bash
node scripts/js/ralph-harness.js "npm test" --elite
  • Max Iterations: 50 loops (Stop after 3 same errors).
  • Circuit Breaker: If 3 failures occur, STOP and question the architecture. </autonomous_debugging> <code_improvement_loop>

✨ CODE INTEGRITY & REFLECTION

Ralph ensures all existing code meets the @clean-code standard.

1. Reflection Loop (Generate → Reflect → Refine)

Before finalizing any code optimization:

bash
node scripts/js/reflection-loop.js
  • Checklist: Edge cases, Input validation, Security, Completeness.
  • Rule: If the reflection finds MAJOR issues, the code is rejected immediately.

2. Algorithmic Hygiene

  • Naming: Every variable and function must reveal its intent.
  • Modularity: No "Logic Slabs". Break code into testable, single-responsibility slices. </code_improvement_loop> <recovery_and_pivots>

🛡️ STRATEGIC RECOVERY

When basic fixes fail, Ralph triggers intelligent pivots.

  • Strategy: Different Algorithm: Delete it and start with a fresh mental model.
  • Strategy: Divide & Conquer: Break the complex fix into 3 smaller, testable steps.
  • Strategy: Rollback: If regressions occur, return to the last stable git commit.
  • Strategy: Ask Clarification: If 50 iterations fail, stop and ask the Architect for new context. </recovery_and_pivots> <audit_and_reference>

� COGNITIVE AUDIT CYCLE

  1. Did I find the ROOT CAUSE or just a symptom?
  2. Did I write a test that fails without my fix?
  3. Did my fix introduce "Blast Radius" damage in unrelated files?
  4. Did the Reflection Loop pass with zero major issues?

� INTEGRATION

  • Surgical Tool: Called when tests fail or code is "smelly".
  • Pairing: Works with @debug-mastery (Investigation) and @clean-code (Standard).
  • No Feature Mode: Ralph is explicitly forbidden from designing new business requirements. </audit_and_reference>

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ralph Wiggum AI skill do?

Surgical Debugger & Code Optimizer. Autonomous root-cause investigation, persistence-loop fixing, and high-fidelity code reflection. No new features, only fixes.

Why use Ralph Wiggum on TypingMind?

Because you install it once and use it with any model. Ralph Wiggum is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ralph Wiggum in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xenitV1/claude-code-maestro/tree/main/skills/ralph-wiggum. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ralph Wiggum?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ralph Wiggum?

As many as you like. As long as a model supports skills, you can use Ralph Wiggum with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ralph Wiggum AI skill free?

Yes. It is published on GitHub by xenitV1 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Related AI Skills

View all
mattpocock logo

Prototype

mattpocock
CommunityPopular

Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

22.3K
264.4K
3 files
View
mattpocock logo

Research

mattpocock
CommunityPopular

Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading…

22.3K
264.4K
1 files
View
mattpocock logo

Resolving Merge Conflicts

mattpocock
CommunityPopular

Use when you need to resolve an in-progress git merge/rebase conflict.

22.3K
264.4K
1 files
View
mattpocock logo

Setup Matt Pocock Skills

mattpocock
CommunityPopular

Configure this repo for the engineering skills: set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

22.3K
264.4K
6 files
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇