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🎯 Zero-AI-Trace Framework

Welcome to the official wiki of the Zero-AI-Trace Framework - A strict framework for controlling ChatGPT and other LLMs to produce authentic and undetectable content.

🚀 Quick Start

Installation

npm install -g zero-ai-trace-framework

Immediate Usage

zero-ai-trace show    # Display the prompt
zero-ai-trace validate # Validate configuration

📖 Main Navigation

🏁 Getting Started

• Getting Started - Installation and configuration guide
• Quick Reference - Essential commands and concepts
• Installation - All installation methods

🔧 Technical Documentation

• CLI Commands - Complete CLI documentation
• Advanced Guide - Optimizations and advanced techniques
• Tutorial - Step-by-step mastery guide

🎯 Framework Reference

• Core Principles - Framework's fundamental rules
• Style Humanization - Techniques for natural writing
• Anti Detection - Strategies to avoid AI detection

💡 Practical Examples

• Examples - Collection of concrete examples
• Use Cases - Applications by domain
• Best Practices - Optimal usage tips

🔗 Integrations

• ChatGPT Integration - ChatGPT configuration
• API Integration - API usage
• Templates - Reusable snippets and templates

🛠 Development

• Development Setup - Development environment
• Testing - Automated testing guide
• Contributing - How to contribute to the project

🌐 Other Languages

• Français/Accueil - French documentation

❓ Support

• FAQ - Common questions and answers
• Troubleshooting - Solutions to common issues
• Support - Where to ask for help

🎯 What is the Zero-AI-Trace Framework?

The Zero-AI-Trace Framework is a set of strict guidelines that transforms how LLMs (Large Language Models) generate content. It targets three main objectives:

1. 🔍 Transparency: Force verification and labeling of uncertain content
2. 🚫 Authenticity: Eliminate typically AI-sounding formulations
3. 💫 Naturalness: Inject rhythm and human imperfections

✨ Key Features

• ✅ Mandatory verification of uncertain content with labeling system
• 🎭 Automatic humanization of writing style
• 🔒 Anti-detection techniques by breaking AI patterns
• 🛠️ Integrated correction protocols automatic self-correction
• 📦 Compact format optimized for system injection
• 🔧 Universal compatibility with all major LLMs
• 🧪 Automated testing and continuous validation
• 🚀 Professional CLI interface with 6 commands
• 📚 Comprehensive documentation and detailed guides

🎬 Quick Example

❌ Without the framework:

I highly recommend using this approach as it significantly improves performance in all possible contexts.

✅ With the framework:

[Inference] This approach seems to work well from what I observe, but it totally depends on your specific context.

📊 Project Statistics

• Current Version: 1.0.1
• Automated Tests: 13/13 passing
• CLI Commands: 6 available
• Prompt Variants: 6 automatically generated
• Integration Templates: 3+ supported platforms

🤝 Community

• 💬 GitHub Discussions
• 🐛 Report a Bug
• ⭐ Star the Project