Awesome AI System Prompts — 32+ Tool Prompts Revealed

What it is

Awesome AI System Prompts is a curated GitHub repository containing extracted system prompts from over 32 production AI tools. The collection includes prompts from ChatGPT, Claude, Cursor, v0, Manus, Devin, Windsurf, Perplexity, and more. Each tool has its own directory with the full extracted system prompt. The project is MIT licensed.

It targets prompt engineers, AI developers, and researchers who want to study how production AI tools structure their system prompts for specific tasks.

How it saves time or tokens

Studying production system prompts reveals patterns for instruction formatting, guardrail design, and capability scoping that you can adapt for your own applications. Instead of trial-and-error prompt engineering, you learn from prompts that power tools used by millions.

How to use

1. Browse the repository:

git clone https://github.com/dontriskit/awesome-ai-system-prompts.git
cd awesome-ai-system-prompts
ls

1. Each tool has its own directory with the full system prompt.
2. Study the prompt structure and adapt patterns for your own AI applications.

Example

# Clone the repository
git clone https://github.com/dontriskit/awesome-ai-system-prompts.git
cd awesome-ai-system-prompts

# Browse available tools
ls -la

# Read a specific tool's system prompt
cat cursor/system-prompt.md
cat v0/system-prompt.md

Each prompt reveals how production tools handle safety, tool use, formatting, and domain-specific instructions.

Related on TokRepo

• Prompt Library — Curated prompts and templates for AI tools
• AI Tools for Coding — AI coding tools whose prompts are in this collection

Key considerations

When evaluating Awesome AI System Prompts for your workflow, consider the following factors. First, assess whether your team has the technical prerequisites to adopt this tool effectively. Second, evaluate the maintenance burden against the productivity gains. Third, check community activity and documentation quality to ensure long-term viability. Integration with your existing toolchain matters more than feature count alone. Start with a small pilot project before rolling out across the organization. Monitor resource usage during the initial adoption phase to identify bottlenecks early. Document your configuration decisions so team members can onboard independently.

Common pitfalls

• System prompts are extracted at a point in time; tools update their prompts frequently, so the collection may lag behind current versions.
• Copying production prompts verbatim may violate terms of service; use them as learning references, not direct copies.
• Prompt patterns that work for one model may not transfer directly to another; adapt techniques rather than copying wholesale.