What problem does it solve? UX reviews are often ad-hoc, vibe-based, and miss entire classes of issues — a code read can't judge visual hierarchy, a screenshot can't measure layout shift, and neither can spot a capability the surface never built. This Skill provides a repeatable, evidence-based audit of one screen at a time, benchmarked against Tidwell's Designing Interfaces pattern catalog and a companion ux checklist skill. ## Core Features & Use Cases - Three-layer audit: L1 static code reading (missing states, absent patterns), L2 visual screenshot review (hierarchy, spacing, responsive, dark mode), and L3 dynamic journey testing with quantified CLS/LCP/INP metrics via agent-browser CDP automation. - Evidence-grounded findings: every finding cites file:line, a verified screenshot, or a captured metric, with a shared severity rubric and a coverage matrix that prevents concluding visual verdicts from code. - Closed-loop feedback: generalizable gaps and exemplary good cases are fed back into the ux checklist skill, and each audit is saved as a worked-example reference for the next run. - Use Case: Run an L1 audit of an OAuth consent screen to discover it lacks a switch-account affordance — a class norm every comparable provider ships — then land the finding as a new checklist item. ## Quick Start Ask the AI to run a ux-audit on a specific page or surface, optionally scoping it with --l1, --l2, or --l3 to choose the audit layer.