Exploring System Prompts of Claude, ChatGPT, Gemini, and Specialized AI Agents
A massive collection of leaked and official system prompts documents the hidden behavioral rules, default tool configurations, and orchestration frameworks of leading AI chat interfaces. Developers can inspect these prompts to master prompt-engineering patterns and tool-calling structures.
Impact: Medium
Why it matters
Analyzing system prompts allows developers to reverse-engineer how top-tier platforms manage complex workflows and apply these learnings to their own applications.
TL;DR
- 01Provides blueprints of how commercial platforms orchestrate agentic behavior.
- 02Shows how Anthropic binds 48 tools and 16 skills in Claude Design.
- 03Offers templates for persona-driven prompts like friendly or cynical.
Key facts
- Claude Design Tools Bound
- 48 tools, 16 skills, 9 starter sources
- Supported Platforms
- ChatGPT, Claude, Gemini
Analyzing Agentic Systems at the Prompt Level
The public system prompts repository provides developers with a direct blueprint of how commercial AI platforms orchestrate agentic behavior. Instead of guessing how Claude Code or ChatGPT Canvas manages state and multi-step reasoning, you can read their underlying instructions. For example, the system prompt for Claude Design showcases how Anthropic binds 48 specialized tools and 16 skills, including specific glob and grep implementations, directly into the model's runtime context.
Adapting Production Guardrails
Custom LLM applications often suffer from prompt injection and unpredictable behavior. By examining how OpenAI structures instructions for its high-reasoning GPT-5 mode, developers can learn how to:
- Enforce step-by-step thinking processes before outputting answers.
- Setup fallback rules for tool-calling failures.
- Construct robust system prompts that prevent users from escaping the agent's core instructions.
Exploring Custom Modes
The collection includes system prompts for various persona-driven models, such as Codex, friendly, pragmatic, and cynical variants. These are valuable templates for anyone building customer service bots or specialized developer assistants, illustrating how subtle changes in adjective framing and output constraints alter LLM behavior.
✓ When to use
- When designing custom system instructions for production agents.
✕ When NOT to use
- If you are only building simple wrappers with no complex tool-calling or reasoning workflows.