Summary

guy915/System-Prompts is a community-driven repository curating system prompts for various Large Language Models (LLMs) including ChatGPT, Claude, and Gemini. It aims to provide users with a readily accessible, organized collection of effective prompts for diverse AI interactions, streamlining prompt engineering efforts for developers, researchers, and power users seeking to optimize LLM behavior.

How It Works

The project functions as a central, crowd-sourced collection point for LLM system prompts. Users are encouraged to contribute new or updated prompts via Pull Requests, fostering a collaborative environment. A Python script, compile_system_prompts.py, is provided as a utility to consolidate these disparate prompts into a single, unified Markdown file, facilitating easier management, review, and access for the community.

Quick Start & Requirements

• Initial setup involves cloning the repository. Running the compile_system_prompts.py script likely requires a standard Python environment.
• No specific dependencies (e.g., Python versions, libraries), hardware requirements (e.g., GPU, CUDA), or estimated setup time are detailed in the README.
• No official quick-start guides, documentation, or demo links are provided.

Highlighted Details

• The repository includes a detailed, albeit complex, set of instructions and alternative prompts designed for extracting system prompts directly from LLMs. This methodology appears to involve intricate text manipulation and potentially "jailbreaking" techniques to reveal underlying system instructions.
• The compile_system_prompts.py script offers a practical utility for generating a unified Markdown document from the collected prompts, serving as a central reference.

Maintenance & Community

• The project's growth and maintenance are primarily driven by community contributions through Pull Requests.
• No specific information is available regarding core maintainers, project sponsorships, partnerships, or dedicated community channels like Discord or Slack.

Licensing & Compatibility

• Crucially, the provided README text omits any mention of a software license.
• This absence creates significant uncertainty regarding usage rights, derivative works, and commercial compatibility, posing a potential adoption blocker.

Limitations & Caveats

• The lack of explicit licensing information is a critical omission that hinders clear adoption pathways.
• The README provides minimal detail on installation procedures, usage examples, or specific LLM compatibility beyond naming a few prominent models.
• The complex prompt extraction methodology described may require significant user effort to understand, implement, and adapt.