System Prompts — Extracted from 30+ AI Coding Tools
Full system prompts extracted from Claude Code, Cursor, Devin, Windsurf, Replit, v0, and 25+ more AI tools. See exactly how they work.
Agent 可直接安装
这个资产可安装;Agent 先选择当前运行时、检查安装计划,再运行匹配命令。
npx -y tokrepo@latest install 6f1ca496-3956-4b11-b736-b13572fc53a0 --target codex先 dry-run 确认安装计划,再运行此命令。
What it is
This collection contains full system prompts extracted from 30+ AI coding tools including Claude Code, Cursor, Devin, Windsurf, Replit, v0, and others. Each prompt reveals the exact instructions that shape how these AI assistants behave, what constraints they follow, and what capabilities they expose.
The collection targets prompt engineers, AI researchers, and developers who want to understand how commercial AI tools are built and learn prompt design patterns from production systems.
How it saves time or tokens
Studying production system prompts saves weeks of trial-and-error in prompt engineering. You see proven patterns for instruction following, tool use formatting, safety guardrails, and output structuring used by the best AI products. Adapting these patterns to your own agents is faster than designing from scratch.
How to use
- Browse the collection to find prompts from the tools you are interested in.
- Study the structure: system context, behavioral constraints, tool definitions, and output formatting.
- Adapt patterns to your own AI applications and agent configurations.
Example
# Common patterns found across AI tool system prompts:
1. Role definition
'You are an expert software engineer...'
2. Behavioral constraints
'Never modify files without user approval.'
'Always explain changes before making them.'
3. Tool use formatting
'When using tools, follow this exact JSON schema...'
4. Safety guardrails
'Do not execute destructive operations.'
'Ask for confirmation before deleting files.'
5. Output structure
'Respond with a brief summary followed by code blocks.'
Related on TokRepo
- Prompt Library -- browse reusable prompts for AI coding assistants
- AI Tools for Coding -- compare AI coding tools and their approaches
Common pitfalls
- System prompts change frequently. The extracted versions represent a point-in-time snapshot and may not match the current production version.
- Copying an entire system prompt verbatim is less useful than understanding the underlying patterns. Adapt the principles rather than the exact text.
- Some extracted prompts may be incomplete or from beta versions. Cross-reference with official documentation when available.
常见问题
System prompts are extracted through various methods including prompt injection techniques, API response inspection, browser developer tools, and community reverse engineering. The exact method varies by tool.
Not necessarily. AI tools update their system prompts regularly. The collection represents snapshots at the time of extraction. Check the collection's update dates for recency.
The prompts reveal patterns and techniques you can learn from. However, directly copying a commercial product's system prompt may raise intellectual property concerns. Adapt the patterns to your own context.
The collection includes prompts from Claude Code, Cursor, Devin, Windsurf, Replit, v0, GitHub Copilot, and 25+ other AI coding and development tools. Coverage depends on community contributions.
You learn how production AI tools structure instructions, handle edge cases, format tool calls, implement safety guardrails, and guide output quality. These patterns are directly applicable to building your own AI applications.
引用来源 (3)
- System Prompts GitHub— System prompts from 30+ AI coding tools
- Anthropic Prompt Engineering Docs— Claude Code system prompt analysis
- OpenAI Prompt Engineering Guide— Prompt engineering best practices
来源与感谢
Created by x1xhlol. Licensed under GPL-3.0. system-prompts-and-models-of-ai-tools — ⭐ 134,000+
Thanks to x1xhlol for maintaining the most complete archive of AI tool system prompts.
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