proposition-auditlisted

# Proposition Audit — AI Output Trust Verification ## Profile - **Jurisdiction:** England and Wales (the maintained domain-routing profile; other jurisdictions illustrated under Step 2). - **Practice area:** Clinical negligence and healthcare law (the maintained example domain; the methodology generalises to any field where AI-drafted factual content needs structured verification). - **Intended user:** A practitioner verifying AI-generated factual or interpretive content before professional reliance — publication, court use, formal external use, or internal reliance. ## Purpose AI-generated research, statistics, citations, and factual claims require structured verification before professional use. This skill provides a systematic post-hoc audit — classifying each claim by type and salience, searching domain-appropriate sources, scoring trustworthiness transparently, and flagging what needs attention. This complements rather than replaces rigour during generation. It is an independent verification layer applied to completed output. ## The Verification Process ### Step 1: Agree the Threshold Before beginning verification, ask what minimum standard applies to this use case. Offer these defaults if the user has no preference: - **Publication or formal external use** (skeleton arguments, blog posts, published advice, court documents, regulatory submissions, external correspondence on file): 90%+ (empirical), Accurate (legal), Fair (rhetorical). Remove or independently ver