ai-user-conflict-predictor

To predict user-AI conflicts, analyze system prompts and dialogue histories by evaluating six axes to produce a Collision Potential score. This identifies friction points and guides prompt design adjustments to improve trust and safety.

A Collision Potential score quantifies the likelihood of user-AI conflicts by evaluating six analytical axes within prompts and conversation logs. It classifies collision types and predicts potential interaction collapse over long-running conversations.

Analyze character configurations by inputting them with conversation logs to forecast how conflict patterns evolve over long-running interactions. The process outputs a conflict profile and actionable recommendations for behavior tuning.

Yes, prompt-analysis improves AI safety controls and UX by identifying and quantifying potential user-AI conflicts. It produces actionable improvement recommendations and collapse-prediction outputs to guide prompt adjustments and mitigate friction.

The conflict predictor integrates with other prompt stack diagnostic skills like ai-character-stability and ai-self-description-analyzer. This synergy allows you to comprehensively diagnose and mitigate conflicts across system prompts and character settings.

Collision prediction for prompt design relies on analyzing provided system prompts and conversation logs, meaning its accuracy depends on input quality. It guides adjustments but requires integrating with other skills to fully mitigate complex prompt stack conflicts.