What problem does it solve? Structured banking briefs often contain vague wording, unresolved decisions, and missing requirement classes that surface too late in delivery. This Skill systematically scans extraction and compliance results to expose ambiguities and hidden requirements before TL handoff, converting them into tracked open questions or documented assumptions. ## Core Features & Use Cases - Eight Linguistic Detectors: Scans titles, descriptions, evidence, and customer-facing strings for lexical, syntactic, pragmatic, pronominal, quantifier, modal, commitment, and phase-boundary ambiguities, plus placeholder tokens like TBD. - Ten Hidden-Requirement Frames: Applies frames covering scale, timing, money, regulatory, operational, failure, integration, localization, lifecycle, and customer experience, with mandatory coverage accounting for all ten frames. - Severity-Floored Findings: Assigns P1/P2/P3 severities using documented floors, emitting open questions when human decisions are needed and assumptions only when a defensible default with a revisit trigger exists. - Use Case: Given a pipeline state JSON with completed story extraction and compliance results for a wire-transfer feature, produce a strict JSON report listing a P1 open question about Legal-approved customer status wording, a modal-hedge finding, and full frame-coverage metadata. ## Quick Start Scan this banking brief's pipeline state JSON for hidden requirements and linguistic ambiguities, then return the stage3 ambiguity JSON report.
