Hallucination Risk Reviewer

Assess hallucination risk in LLM outputs by classifying claims into factual, citation, procedural, numerical, and domain-expertise categories. This generates a structured risk report with high/medium/low scores, calibration issues, and mitigation guidance for human review.

Identify hallucination claim types by categorizing AI outputs into factual world claims, citations, procedural content, numerical data, and domain-expertise assertions. This classification quantifies propagation risk and produces actionable mitigations for AI support bots and content generation.

You can evaluate AI support bot responses for citation and numerical hallucinations by reviewing the generated output. The skill classifies claim types, assesses calibration issues, and delivers a formal risk assessment with an overall usage recommendation.

This hallucination risk review works with AI outputs from Claude Code, Cursor, Codex, and similar environments. It analyzes the generated text to quantify propagation risk and provides structured mitigation guidance suitable for integration into product workflows.

Generate a formal risk assessment for AI-generated content by analyzing the output for hallucination risks and calibration issues. The process produces a structured report with high, medium, and low risk categories alongside an overall recommendation for usage decisions.

Perform a hallucination risk assessment on AI procedural instructions when evaluating outputs for high-stakes decisions or content generation. This identifies procedural claim inaccuracies, evaluates propagation risk, and provides mitigations to ensure safe usage.