The open-source project on GitHub, "system-prompts-and-models-of-ai-tools," has garnered significant attention, accumulating 30.5K stars and becoming a valuable resource for AI developers and researchers. According to AIbase, this project compiles system prompts and model configurations for nine popular AI tools, encompassing over 6,500 lines of content. It covers v0, Cursor, Manus, Same.dev, Lovable, Devin, Replit Agent, Windsurf Agent, and VSCode Agent, offering invaluable insights into the design principles of these AI tools. Details are publicly available on GitHub and various social media platforms.
Key Highlights: 6,500+ Lines of Prompts Deconstructing 9 Major AI Tools
The "system-prompts-and-models-of-ai-tools" project systematically organizes a comprehensive library of AI tool prompts for developers. AIbase has summarized its core content:
Nine Tools Covered: Including v0 (Vercel generative UI), Cursor (AI code editor), Manus (intelligent agent), Same.dev, Lovable (collaborative development), Devin (AI software engineer), Replit Agent, Windsurf Agent, and VSCode Agent, encompassing both open-source and proprietary tools.
6,500+ Lines of Prompts: Provides over 6,500 lines of system prompts and internal tool configurations, revealing the role definitions, behavioral constraints, and functional designs of each tool. For example, Cursor emphasizes the safety of code modifications, while Manus utilizes over 200 lines of complex instructions.
Design Insight: Through prompt analysis, developers can understand how AI tools optimize code generation, reduce hallucinations, and enhance user experience using instructions. For instance, Cursor uses standardized constraints to minimize AI errors.
Learning and Research Value: Suitable for AI professionals, prompt engineering researchers, and startups to learn how to design efficient system prompts or leverage best practices to develop customized AI tools.
AIbase notes that community testing shows Cursor's prompts, with their clear safety and usage guidelines, significantly reduce "hallucinations" in code generation, while Manus's complex instructions demonstrate the multi-tasking capabilities of agent-based AI.
Technical Architecture: Structure and Function of System Prompts
This project not only provides raw prompts but also reveals the design logic behind AI tools. AIbase analysis highlights the following technical advantages:
Role Definition and Constraints: For example, Windsurf's Cascade agent is defined as an "autonomous programming assistant" based on the AI Flow paradigm, emphasizing independent task execution; Cursor, using the Claude 3.7 model, focuses on the usability and safety of code modifications.
Prompt Engineering Practices: The prompts frequently mention "best practices." Cursor uses structured instructions to reduce AI deviations, while Manus optimizes complex task handling through multi-step reasoning.
Modular Design: The prompts for each tool are divided into role descriptions, behavioral rules, tool calls, and output formats, facilitating reuse or customization by developers. For example, v0's UI generation prompts can be directly used in React component development.
Security Warnings: The project emphasizes that AI startups need to protect prompts and model configurations, recommending ZeroLeaks service to prevent data leaks, highlighting industry security awareness.
AIbase believes the project's value lies in its systematic approach and transparency, providing practical examples for prompt engineering—akin to an "AI tool design textbook."
Application Scenarios: From Learning to Enterprise Development
The rich content of "system-prompts-and-models-of-ai-tools" makes it suitable for various scenarios. AIbase summarizes its main applications:
Prompt Engineering Learning: Developers can analyze the prompts of tools like Cursor and Devin to learn how to design efficient instructions and optimize LLM output, suitable for AI engineers and researchers.
AI Tool Development: Startups can leverage the collaborative instructions of Lovable or the UI generation logic of v0 to develop customized AI products and accelerate prototype verification.
Education and Training: Universities and training institutions can use the prompt library as a teaching resource to help students understand the behavioral logic and design principles of AI systems.
Security and Compliance: Enterprises can refer to the project's recommendations and use tools like ZeroLeaks to detect prompt leakage risks and ensure the security of their AI systems.
Community feedback shows that developers, by studying Devin's prompts, optimized their AI agent's multi-tasking capabilities, reducing code generation errors by approximately 10%. AIbase observes that the project's 30.5K stars reflect the high demand for transparent AI design.
Getting Started: Quick Access and Research
AIbase understands that this project is now freely available on GitHub (github.com/x1xhlol/system-prompts-and-models-of-ai-tools), including detailed prompt files and instructions. Developers can quickly get started with the following steps:
Access the GitHub repository and clone or fork the project (github.com/x1xhlol/system-prompts-and-models-of-ai-tools);
Browse the prompt directories for the nine tools (e.g., Cursor Prompts, Devin AI) and view the specific files (e.g., cursor_agent.txt, devin.txt);
Use Python or other scripts to parse the prompts, extract role definitions, tool calls, or constraint logic;
Refer to ZeroLeaks (zeroleaks.vercel.app) for security audits to protect your own AI systems.
The community recommends prioritizing the study of Cursor and v0 prompts due to their clear structure and wide range of applications. AIbase reminds users that the project has stopped using GitHub Issues and suggests submitting suggestions through the System Prompts Roadmap & Feedback page.
Community Feedback and Areas for Improvement
Following its release, "system-prompts-and-models-of-ai-tools" received high praise from the community for its comprehensiveness and learning value. Developers called it "an invaluable reference for AI tool design," particularly highlighting the Cursor and Manus prompts as examples of prompt engineering excellence. However, some users pointed out the lack of prompts for video generation tools, suggesting the addition of Runway or Pika examples. The community also expects more detailed explanatory documentation outlining the context and application scenarios of each prompt. The development team responded that they will expand tool coverage and improve documentation in the future. AIbase predicts that the project may integrate with the MCP protocol or ComfyUI to build a workflow for prompt testing and optimization.
Future Outlook: A Catalyst for Transparent AI Design
With its 30.5K stars, "system-prompts-and-models-of-ai-tools" highlights the developers' desire for transparent AI design. AIbase believes that its open-source model not only promotes the popularization of prompt engineering but also provides dual inspiration for AI startups in terms of security and design. The community is already discussing integrating it with the workflow of Dream 3.0 or Lovable 2.0 to build an ecosystem from prompt design to application development. In the long term, the project may evolve into an "AI prompt marketplace," providing shared and customized services, similar to Hugging Face's model ecosystem. AIbase anticipates further expansion of the project in 2025, particularly in breakthroughs in multi-modal AI and security protection.
Project Address: https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools
