This repository curates system prompts for advanced AI agents, targeting AI agent builders and prompt engineers. It provides a structured analysis of effective prompt design patterns, enabling the creation of more reliable, safe, and capable AI assistants by showcasing real-world examples from leading AI tools.

How It Works

The collection analyzes system prompts based on core principles: clear role definition, structured instructions, explicit tool integration, step-by-step reasoning, environment awareness, domain-specific knowledge, safety protocols, and consistent tone. It highlights how these principles are implemented through various techniques like Markdown headings, custom XML-like tags, inline JSON/TypeScript schemas, and explicit agent loops, demonstrating how these elements guide AI behavior for complex task execution.

Quick Start & Requirements

This repository is a collection of text files and does not require installation or execution. It serves as a reference guide.

Highlighted Details

• Analyzes system prompts from diverse AI agents including ChatGPT, Claude, v0, same.new, and Manus.
• Details specific prompt engineering techniques such as using MDX components as tools (v0) and explicit agent loops (Manus).
• Examines how prompts embed domain-specific knowledge, coding best practices (Next.js, React, Tailwind), and safety policies.
• Compares different approaches to tool integration, planning mechanisms, and prompt structuring across various AI systems.

Maintenance & Community

This is a curated collection, with contributions likely driven by the open-source community. Specific maintainer details or community links are not provided in the README.

Licensing & Compatibility

The repository itself is not software and thus not licensed. The content within is for informational purposes.

Limitations & Caveats

The repository contains examples of system prompts, which are snapshots in time and may not reflect the current, live implementations of the respective AI tools. The effectiveness of these prompts is dependent on the underlying AI model's architecture and training.