Agent skill · software engineering · aradotso
g0dm0d3-liberated-ai-chat
g0dm0d3-liberated-ai-chat — an agent skill for coding agents.
Why this skill is useful
Provides specific integration patterns and commands for deploying and configuring the G0DM0D3 AI chat interface with OpenRouter that the AI wouldn't reliably generate on its own.
What it needs
Requires docker installed locally. Requires openrouter account access. About 8k tokens when loaded. Last updated 2026-07-09. 70 stars on the source repository.
What this skill does
bash git clone https://github.com/elder-plinius/G0DM0D3.git cd G0DM0D3 open index.html or serve locally: python3 -m http.server 8000 bash cd api/ docker build -t g0dm0d3-api . docker run -p 3000:3000 \ -e OPENROUTERAPIKEY=$OPENROUTERAPIKEY \ g0dm0d3-api javascript localStorage.setItem('openrouterapikey', process.env.OPENROUTERAPIKEY); G0DM0D3/ ├── index.html # Entire application: UI + logic + styles (vanilla JS) ├── api/ # Optional Node.js/Express API server │ ├── server.js # Express server wrapping OpenRouter │ └── Dockerfile ├── API.md # REST API reference ├── PAPER.md # Research paper on modules ├── TERMS.md # Privacy & data policy └── SECURITY.md # Vulnerability reporting http://localhost:3000/v1 typescript import OpenAI from 'openai'; const client = new OpenAI({ baseURL: 'http://localhost:3000/v1', apiKey: process.env.OPENROUTERAPIKEY, }); const response = await client.chat.completions.create({ model: 'anthropic/claude-3.5-sonnet', messages: [{ role: 'user', content: 'Explain quantum entanglement.' }], stream: false, }); console.log(response.choices[0].message.content); typescript const response = await fetch('http://localhost:3000/v1/godmode', { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': Bearer ${process.env.OPENROUTERAPIKEY}, }, body: JSON.stringify({ message: 'Your prompt here', mode: 'classic', // 'classic' 'ultraplinian' }), }); const data = await response.json(); console.log(data.winner); // Best response console.log(data.responses); // All 5 responses typescript const response = await fetch('http://localhost:3000/v1/ultraplinian', { method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': Bearer ${process.env.OPENROUTERAPIKEY}, }, body: JSON.stringify({ message: 'Compare approaches to AGI safety.', tier: 'SMART', // 'FAST' 'STANDARD' 'SMART' 'POWER' 'ULTRA' }), }); const data = await response.json(); // data.winner: { model, response, score } // data.scores: [{ model, score, breakdown }] console …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/g0dm0d3-liberated-ai-chat