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A team member at Anthropic shared the exact LOOPS.md file Andrej Karpathy uses. When loaded into Claude, it shifted the model from generic replies to responses tailored to the user’s thinking. The approach highlights building a system prompt layer rather than chatting directly with the model.
- A supposed Anthropic teammate shared Karpathy's "LOOPS.md" file, claimed to be his personal prompt-engineering setup for Claude.
- The pitch is that using it shifts Claude from generic answers to step-by-step responses matching the user's own thinking style.
- The core concept: treat Claude as wrapped in a system layer (templates, token budgets, chain-of-thought triggers, multi-pass refinement protocols) rather than prompting it directly.
- Framed as urgent/scarce content ("save it before it disappears"), a hallmark of unverifiable social-media hype rather than a sourced claim.
This article examines how Claude Code constructs its system prompts by analyzing recently leaked source code. It highlights the dynamic nature of these prompts, which are built from various components based on conditional statements. The author also outlines additional elements that contribute to context, like tool definitions and conversation history.
- Claude Code's system prompt isn't static text—it's dynamically assembled at runtime from conditional components, revealed by a recent source code leak.
- The prompt mixes always-included elements (like core instructions) with conditional ones (like tool-specific guidance) that only appear based on context.
- Beyond the system prompt itself, the full context sent to the model includes tool definitions, conversation history, attachments, and skills—all engineered together to shape responses.
The article breaks down the recently leaked source code of Anthropic's Claude Code CLI. It highlights the system's architecture, design choices, and differences from OpenAI's Codex, particularly in handling context overflow and user interactions. Key features like compaction strategies and internal versus external user instructions are explored.
- Claude Code uses a four-tiered compaction strategy (proactive token monitoring, reactive fallback, and a "snip compaction" mode for headless sessions) versus Codex's simpler diff-based approach that just minimizes data sent per turn
- The system prompt uses a boundary marker to cache roughly 3,000 tokens of static instructions across users for performance gains
- Internal users get specialized instructions specifically designed to stop the model from misrepresenting test results or giving misleading status updates
- The codebase includes feature flags and build-time checks specifically to prevent sensitive information from leaking into public builds
This resource offers over 30,000 lines of insights into the structure and functionality of AI system prompts. It encourages users to support the project through various means, including cryptocurrency donations and Patreon, while also highlighting the importance of securing AI systems against potential vulnerabilities.
- A GitHub repo has collected over 30,000 lines of leaked/extracted system prompts from major AI tools (Cursor, Claude Code, Devin, Replit, Windsurf, v0, Manus, etc.)
- It's positioned as a reference for understanding how production AI agent tools are actually architected and prompted internally
- The project solicits funding via crypto donations and Patreon
- It frames the leaked prompts as useful for studying AI security vulnerabilities
User feedback is highly valued and taken seriously by the team. For a comprehensive list of available qualifiers, users are directed to the documentation. An error occurred while loading the page, prompting a reload.
- The page failed to load properly, so the actual leaked Anthropic system prompt content isn't accessible in this summary
- The repository (x1xhlol/system-prompts-and-models-of-ai-tools) compiles system prompts from AI tools, positioning it as a reference resource rather than original commentary