adversarial-qc

To run an adversarial review, trigger the skill on your AI-generated deliverable. It applies structured parallel verification using two independent agents to audit checklist items with evidence, comparing results to generate an evidence-backed QC certificate.

Multi-agent verification runs two independent reviewers against a structured checklist to audit factual claims, numbers, and logic. Cross-model or single-model agents compare evidence to detect surface and structural issues before generating a pass, fail, or review verdict.

Yes, evidence-based auditing applies to reviewing scripts, plans, analyses, and emails. You can configure depth using quick, standard, or deep modes and select specific checklists to verify factual claims, attribution, formatting, consistency, and scope.

The best way to generate an auditable QC certificate is to run parallel multi-agent verification on your deliverable. The process outputs an inline summary and an optional PDF certificate trail that flags disagreements for human review.

Structured parallel verification is not suited for lightweight conversational exchanges or trivial outputs. It is designed for substantive artefacts like reports and analyses where factual claims, numbers, logic, and attribution require evidence-backed auditing.

Yes, factuality checking with cross-model agents blocks shipping in high-risk scenarios by outputting a QC certificate that flags disagreements. This actionable escalation requires human review before delivering the AI-generated artefact.