System Prompts and Models of AI Tools

Key takeaways#

• This GitHub repository is a large reference collection of system prompts, internal tool notes, and AI model details from many AI products.
• It is best treated as a research resource for builders studying prompt architecture, agent tool design, and product behavior.
• It is not a runnable app. Use it to compare patterns, not as drop-in production code.
• The repository is active and high-signal, with more than 139,000 GitHub stars observed during this pipeline run.

What this resource covers#

system-prompts-and-models-of-ai-tools collects examples and notes related to products such as Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, Notion AI, Perplexity, Replit, Trae, Warp, Windsurf, Xcode, v0, and other AI developer or productivity tools. The repository description says it covers system prompts, internal tools, and AI models. That makes it useful for builders who want to understand how real AI products frame instructions, expose tools, and structure agent behavior.

The important thing is scope. This is a reference library, not an official documentation site for those products. Treat each file as research material. Read the repository history, linked sources, and context before relying on any single prompt. Product behavior changes often, and system prompts can become stale when vendors update models, tools, safety rules, or product flows.

Why builders should care#

System prompts are one of the clearest windows into how AI products work. They show what the product asks the model to prioritize, which tools the model can call, what tone it should use, and which boundaries it must respect. For agent builders, those examples are practical. They can reveal patterns for tool naming, instruction hierarchy, context packing, refusal handling, user confirmation, file access, and multi-step planning.

Use this repository when you are designing an AI coding assistant, research agent, browser agent, design agent, or internal operations bot. Compare how several products describe similar jobs. Look for repeated patterns: explicit role definition, ordered workflow steps, strict tool-use rules, examples of allowed outputs, and clear failure modes. Then write your own smaller prompt for your use case. Copying a large prompt wholesale usually creates brittle behavior; adapting the pattern is safer.

Suggested workflow#

1. Start with the product category closest to your use case, such as coding, design, research, search, or automation.
2. Read several prompts side by side. Note how each one handles role, context, tools, output format, and safety.
3. Extract only the patterns you need. Keep your production prompt short enough to audit.
4. Test with real tasks. Check whether the model follows the workflow or ignores parts of the instruction.
5. Revisit the repository when major AI products ship new agent features.

Practical cautions#

Do not assume every prompt in the repository is current, complete, or officially endorsed by the vendor named in a folder. Do not paste third-party prompts into a sensitive production environment without legal, security, and privacy review. If you use these examples to improve an internal system, document the source and keep a changelog of what you adopted.

For OpenTools readers, the value is pattern recognition. The repository helps teams move from vague prompt writing to more explicit agent design: define the role, constrain the tools, state the process, verify the output, and make the handoff clear.