Full Claude Opus 4.6 System Prompt Leaked on GitHub

✍️ OpenClaw Radar📅 Published: February 7, 2026🔗 Source
Full Claude Opus 4.6 System Prompt Leaked on GitHub
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The full system prompt for Claude Opus 4.6 has been leaked and published on GitHub, giving researchers and developers insight into how Anthropic instructs their most capable model.

Why This Matters

System prompts are typically confidential as they contain the core instructions that shape model behavior. Understanding these prompts can help:

  • Developers learn best practices for prompting
  • Researchers study alignment techniques
  • Users understand why Claude behaves certain ways

The leak of Claude Opus 4.6's system prompt is significant for the AI agent ecosystem as it opens up discussions around transparency and accountability in AI development. It allows a broader audience to analyze the ethical considerations and design choices made by Anthropic, potentially influencing future models and prompting a shift towards more open practices in the industry.

Historical Context

System prompt leaks have happened with various AI models and typically reveal interesting insights about how companies attempt to control model behavior, safety guidelines, and response formatting.

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Key Takeaways

  • The leaked prompt provides a rare glimpse into the operational guidelines of one of the leading AI models.
  • Such leaks can foster collaboration and innovation among developers and researchers by sharing knowledge and strategies.
  • Understanding the prompt can help users better navigate interactions with the AI, leading to more effective use cases.
  • Transparency in AI development may lead to improved trust and safety standards across the industry.

Where to Find It

The prompt is available on GitHub at: github.com/asgeirtj/system_prompts_leaks

This repository appears to collect system prompts from various AI models for research and educational purposes.

Getting Started

To explore the leaked system prompt, visit the provided GitHub link and review the document. Developers can analyze the structure and content of the prompt to enhance their own prompting techniques. Researchers can use this information to inform their studies on AI alignment and safety. Additionally, users can gain insights into how Claude Opus 4.6 is designed to respond, which can improve their interactions and expectations when using the model.

📖 Read the full source: r/LocalLLaMA

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👀 See Also