System Prompts of AI Tools

Overview

The rapid evolution of artificial intelligence has transformed prompt engineering from a niche curiosity into a critical technical skill. "System Prompts of AI Tools," hosted on GitHub, serves as a massive, community-driven repository that grants unprecedented access to the hidden instructions powering today's most popular AI applications. Boasting over 142,000 stars, this repository is an invaluable developer resource that lifts the curtain on how leading AI models are directed to behave, interact, and enforce safety guardrails. The collection is remarkably comprehensive, featuring the full, exposed system prompts and internal tool specifications from an impressive roster of commercial applications. Users will find the foundational instructions for developer-centric platforms like Cursor, Devin AI, Lovable, Replit, and v0, alongside broader AI systems like Manus, NotionAI, Perplexity, and Windsurf. For developers, AI researchers, and tech enthusiasts, this project operates as a masterclass in advanced prompt engineering. By examining these carefully constructed directives, users can reverse-engineer AI behaviors to build more robust custom agents. It provides a rare look into the operational guardrails and complex personas designed by top-tier AI companies. Understanding how industry leaders structure their instructions—from formatting specific code blocks to enforcing strict safety protocols—offers an unparalleled educational advantage. Rather than guessing how an AI agent maintains context or handles edge cases, developers can study proven, production-ready frameworks and apply similar structural anatomy to their own system prompts. However, there are inherent limitations to relying on exposed system prompts. Because commercial AI tools continuously iterate and update their models behind the scenes, some of the archived prompts will inevitably become outdated as the platforms evolve. Despite this, the core mechanics of how these tools are instructed to process information, read files, and execute commands remain highly relevant as a historical and technical reference. Ultimately, this repository is much more than a simple archive; it is a highly valuable transparency tool. Whether you are looking to understand the safety guardrails of modern AI models, researching the anatomy of effective prompt design, or trying to build your own autonomous coding assistant, this free, open-source collection provides the critical blueprint needed to understand and leverage the true capabilities of commercial AI.

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Core Features

• Comprehensive collection of system prompts from popular AI tools
• Includes internal tools, AI models, and prompt engineering references
• Covers a wide range of applications like Cursor, Devin AI, Lovable, and v0
• Community-driven and continuously updated on GitHub

Use Cases

• Learning advanced prompt engineering techniques from commercial AI tools
• Understanding the safety and operational guardrails of various AI models
• Reverse-engineering AI behaviors for custom agent development
• Researching the structural anatomy of effective AI system prompts

Pricing

The repository is completely free and open-source for anyone to access and study.

Pros

• Highly valuable educational resource for prompt engineering
• Provides rare transparency into commercial AI guardrails and instructions

Cons

• Some exposed prompts may become outdated as the respective AI tools update their models

Frequently Asked Questions

Summarized from the official site: https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools

Is System Prompts of AI Tools free and open source?

Yes, it is a completely free, open-source collection hosted on GitHub. The repository is community-driven and provides public access to the hidden instructions powering popular AI applications.

What is System Prompts of AI Tools?

It is a massive repository that exposes the system prompts and internal tool specifications of leading commercial AI platforms. It features instructions from applications like Cursor, Devin AI, Lovable, Replit, v0, and Perplexity.

What are the use cases for studying exposed AI system prompts?

The collection serves as a masterclass for developers to learn advanced prompt engineering and reverse-engineer AI behaviors. Users can study these production-ready frameworks to understand safety guardrails and build more robust custom agents.

Are the system prompts in this repository always up to date?

No, some archived prompts will inevitably become outdated because commercial AI tools continuously iterate behind the scenes. However, the core mechanics of how these tools process information remain highly relevant as a technical reference.