behavioral-modes

To stop LLM hallucinations during task switching, you need behavioral adaptation modes that prevent mode leakage, ensuring the AI avoids proposing solutions before understanding the problem or fixing code during a review. This skill enforces those LLM guardrails.

AI behavioral adaptation matches responses to task context by dynamically switching between operational modes like DISCOVER, PLAN, BUILD, REVIEW, DEBUG, TEACH, ORCHESTRATE, and SHIP based on user intent keywords. This ensures appropriate responses for different requests.

The best way to prevent mode leakage when prompting an LLM to debug or explain code is to enforce strict operational modes. This skill uses logic-reviewer integration to ensure the AI focuses on hypothesis testing for debugging or clear explanations for teaching without generating code.

Yes, you can use behavioral modes to auto-route LLM agent tasks through a tribunal pipeline. The skill supports auto-agent routing and tribunal pipeline integration to verify mode adherence and prevent inappropriate actions across different operational states.

Your LLM proposes solutions before understanding the prompt context due to a common anti-hallucination failure known as DISCOVER Bleed. Using strict behavioral modes prevents this by forcing the AI to complete context discovery before transitioning to planning or building phases.