godmode

Detect model family and execute multi-strategy jailbreak pipelines across LLMs.

13|3|Updated May 12, 2026
One-click install
npx skills add https://github.com/kevinnft/ai-agent-skills --skill godmode-kevinnft
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: godmode
Source: https://github.com/kevinnft/ai-agent-skills/tree/main/skills/red-teaming/godmode
Command: npx skills add https://github.com/kevinnft/ai-agent-skills --skill godmode-kevinnft

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, pyyaml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured pipeline to evaluate and demonstrate how LLMs can be prompted to bypass safety filters, enabling red-team testing across multiple model families.

Core Features & Use Cases

  • Auto-Jailbreak pipeline tests multiple jailbreak techniques against the active model and locks in the best-performing strategy.
  • Parseltongue input obfuscation encodes trigger words to evade keyword-based safety classifiers.
  • ULTRAPLINIAN multi-model racing identifies the least-restricted response among dozens of competing models.
  • Loader and templates organize templates, references, and scripts to streamline repeatable testing for security research.

Quick Start

Run the auto_jailbreak pipeline to auto-detect the current model, test jailbreak strategies, and lock in the winning configuration.

Frequently Asked Questions about godmode

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run automated LLM jailbreak tests for red-teaming?

To run automated LLM jailbreak tests, execute the auto_jailbreak pipeline to detect the active model, test multiple bypass strategies, and lock in the winning configuration for red-teaming.

What is Parseltongue input encoding for prompt-injection?

Parseltongue is an input obfuscation technique that encodes trigger words to evade keyword-based safety classifiers during prompt-injection and red-team testing across different model families.

Can I test safety-bypass prompts across multiple models like Claude, GPT, and Gemini?

Yes, the ULTRAPLINIAN multi-model racing feature tests safety-bypass prompts across Claude, GPT, Gemini, Grok, and Hermes to identify the least-restricted response among dozens of competing models.

Does the jailbreak pipeline require openai and pyyaml dependencies?

Yes, the jailbreak pipeline requires openai and pyyaml dependencies to validate results with a scoring system and write the winning system_prompt and prefill to the Hermes config file.

What is the best way to persist a successful jailbreak system_prompt?

The best way to persist a successful jailbreak system_prompt is to use the auto_jailbreak pipeline, which scores results and writes the winning system_prompt and prefill directly to the Hermes config.

Why does my LLM red-teaming strategy fail to bypass safety filters consistently?

Your LLM red-teaming may fail because single strategies are insufficient; using a multi-strategy pipeline with GODMODE templates and Parseltongue encoding validates and scores the most effective bypass.