System Prompts For AI – Leaked List for each model

System prompts are the hidden instruction layers that tell an AI tool how to behave before a user ever types a message. They define the AI’s role, tone, rules, tools, limits, formatting style, and workflow.

A public GitHub repository called x1xhlol/system-prompts has become one of the most popular collections for studying these prompts. It gathers system prompts, internal tool definitions, and model details from many well-known AI tools.

There is also a website called SystemPrompts.org that makes this kind of information easier to browse. It resembles the GitHub library but presents the prompts in a cleaner, searchable, reader-friendly format.

If you are interested in AI tools, you may also like our guides on best AI tools for developers.

What Is a System Prompt?

A system prompt is a high-priority instruction given to an AI model before the user prompt. It tells the model what kind of assistant it should be and how it should respond.

For example, the same base model can act like a coding assistant, search engine, writing editor, customer support bot, or research agent. The system prompt is one of the main reasons those tools feel different.

In simple terms, the system prompt is the AI product’s operating manual. It explains what the assistant can do, what it should avoid, and how it should handle different situations.

Why System Prompts Are Important

System prompts matter because they shape the behavior of AI tools. They can make a model more helpful, safer, more focused, and more consistent.

A weak system prompt can make an AI assistant vague or unreliable. A strong system prompt can guide the model through complex workflows, tool use, formatting rules, and edge cases.

• They define the AI’s identity and role.
• They control tone and response style.
• They explain available tools and when to use them.
• They add safety and refusal rules.
• They shape how the model plans, reasons, and verifies work.
• They help turn a general model into a specific product.

About the x1xhlol System Prompts GitHub Library

The x1xhlol/system-prompts-and-models-of-ai-tools repository is a large public collection of AI system prompts and model/tool information. It includes prompts related to tools such as Cursor, v0, Devin AI, Claude Code, Windsurf, Replit Agent, Lovable, Perplexity, GitHub Copilot, and many others.

At the time of research, the repository had more than 140,000 GitHub stars and over 34,000 forks. That popularity shows how much interest developers, prompt engineers, and AI builders have in understanding how commercial AI products are structured.

The repository is especially useful because it shows real-world prompt engineering patterns. Instead of reading generic advice, you can study how actual AI tools describe roles, tools, workflows, rules, and output formats.

What You Can Learn From the Library

This kind of system prompt library is useful for learning how advanced AI products are designed. It can help developers understand the structure behind agentic tools and coding assistants.

For example, many prompts include instructions about planning, editing code, asking clarifying questions, using tools, avoiding unsafe behavior, and formatting final answers. These are practical patterns that can inspire better AI workflows.

• How AI coding agents define their role
• How tools are described to language models
• How prompts control output formatting
• How products handle safety and limitations
• How agents decide when to ask questions
• How AI tools manage multi-step tasks
• How different products create different assistant personalities

About SystemPrompts.org

SystemPrompts.org is a website that presents system prompts from major AI tools in a cleaner interface. It mirrors the open-source GitHub repository and makes the content easier to explore.

The homepage explains what a system prompt is and lets users browse prompts by tool. It includes categories such as AI coding agents, AI code editors, app builders, browser agents, answer engines, and productivity assistants.

This makes it easier for non-technical readers to understand the collection. GitHub is better for developers, while SystemPrompts.org is easier for browsing, reading, and comparing tools quickly.

GitHub Repository vs SystemPrompts.org

Resource	Best For	Main Benefit
GitHub Repository	Developers and researchers	Raw source collection, files, updates, issues, forks
SystemPrompts.org	Readers and prompt learners	Cleaner browsing, categories, easier navigation

If you want the original source, use the GitHub repository. If you want to quickly browse prompts by product, SystemPrompts.org is more convenient.

Common Parts of a Good System Prompt

Most strong system prompts include several core parts. These parts work together to make the AI more predictable and useful.

• Role: Who the AI is supposed to be.
• Goal: What the AI should help the user achieve.
• Tone: How the AI should sound.
• Rules: What the AI should or should not do.
• Tools: Which tools the AI can use and when.
• Workflow: How the AI should approach complex tasks.
• Output format: How answers should be structured.
• Safety limits: What topics require caution or refusal.

This is why system prompts can become long. Modern AI agents often need detailed instructions because they handle coding, browsing, file editing, planning, testing, and user communication.

Why Developers Study System Prompts

Developers study system prompts because they reveal how AI tools are built at the behavior level. A model is important, but the instructions around that model are also part of the product.

By reading real prompts, developers can learn how to structure their own AI apps. They can see how experienced teams write constraints, organize tools, and reduce confusion for the model.

This is especially useful for anyone building AI agents, AI coding tools, chatbots, workflow automations, or prompt-based SaaS products. For related tools, check our article on AI automation agencies.

How to Use These Resources Responsibly

System prompt libraries should be used for learning, research, and prompt engineering education. They should not be used to impersonate products, bypass safeguards, or copy proprietary behavior into a competing tool.

The right approach is to study the structure and patterns. Then create your own prompts based on your product, audience, safety requirements, and workflow.

• Use them to learn prompt structure.
• Compare how different AI tools define roles.
• Study how tool instructions are written.
• Adapt general ideas, not full private prompts.
• Respect product ownership and trademarks.
• Do not use prompts to bypass safety systems.

Example System Prompt Template

Here is a simple template inspired by common system prompt patterns. This is not copied from any product; it is a general structure you can adapt.

You are an AI assistant for [product/user type].

Your goal is to help the user [main goal].

Follow these rules:
- Be clear, accurate, and concise.
- Ask clarifying questions when needed.
- Do not invent facts.
- Use tools only when they are useful.
- Explain important tradeoffs.
- Format answers for easy reading.

When solving complex tasks:
1. Understand the user request.
2. Gather relevant context.
3. Choose the safest practical approach.
4. Complete the task.
5. Verify the result.
6. Summarize what changed.

This kind of structure helps the model understand its job. It also gives developers a foundation for testing and improving AI behavior over time.

System Prompts and AI Product Design

System prompts are not only technical instructions. They are also product design documents.

They define how an AI product feels to the user. A coding assistant, research assistant, browser agent, and design-to-code tool all need different instructions because they solve different problems.

This is why two tools using similar models can feel completely different. The model matters, but the surrounding prompt, tool design, retrieval system, and interface matter too.

Best Practices for Writing Your Own System Prompt

If you are building your own AI tool, start with a clear role and a narrow goal. Do not try to make the assistant do everything at once.

Then add rules, tool instructions, and output formats only where they are needed. A shorter prompt is often better if it is clear and testable.

• Write the role in plain language.
• Define the user’s goal clearly.
• Separate rules from workflow steps.
• Explain tools with specific usage conditions.
• Add examples only when they improve consistency.
• Test the prompt with real user tasks.
• Update the prompt when the product changes.

OpenAI’s API documentation also notes that model behavior can change between model snapshots, so teams should use evals and pinned model versions when they need consistent behavior. You can read more in the OpenAI API documentation.

Conclusion

System prompts are one of the most important parts of modern AI tools. They turn general-purpose models into focused products with specific roles, workflows, rules, and personalities.

The x1xhlol GitHub library is valuable because it gives developers and researchers a large collection of real-world prompt examples. SystemPrompts.org makes that same kind of information easier to browse and understand.

The best use of these resources is education. Study the patterns, learn how top AI tools are structured, and then write original system prompts for your own products.

FAQs

What is a system prompt?

A system prompt is a high-priority instruction that tells an AI model how to behave. It can define the AI’s role, tone, rules, tools, and response format.

What is the x1xhlol system prompts repository?

It is a public GitHub repository that collects system prompts, internal tool definitions, and model information from many AI tools. It is popular among developers, researchers, and prompt engineers.

What is SystemPrompts.org?

SystemPrompts.org is a website that makes system prompts easier to browse. It mirrors the open-source repository and organizes prompts by AI tool and category.

Can I copy system prompts from other AI tools?

You should use them for research and education, not direct copying. It is better to study the structure and then create an original prompt for your own product.

Why do system prompts matter for AI agents?

AI agents need instructions for planning, tool use, safety, file handling, and communication. A good system prompt helps the agent behave consistently across complex tasks.