visual-verdict

To get a deterministic visual verdict, supply a generated screenshot and one or more reference images to identify visual discrepancies. The Skill evaluates layout, typography, and styling differences, returning a strict JSON payload with a pass/fail verdict, score, and actionable suggestions.

Yes, you can automate UI visual regression testing using the strict JSON output produced by this visual verdict process. The deterministic pass/fail verdicts and structured difference logging are designed for automated pipelines and seamless handoff between design and engineering teams.

A UI screenshot comparison detects visual discrepancies in layout, spacing, and typography. By applying domain-specific evaluation through an optional category hint, the visual verdict precisely identifies styling differences between the generated render and the provided reference images.

Yes, the visual verdict output includes specific suggestions for fixing UI styling differences. The strict JSON payload contains a differences array detailing the layout and typography issues, alongside reasoning and remediation suggestions to guide the next visual QA iteration.

To evaluate UI components for specific domains during visual QA, provide a category hint alongside the generated screenshot and reference images. This domain-specific evaluation context helps tailor the pixel-diff comparison and refines the resulting JSON verdict for accurate visual regression checks.

The best way to structure visual QA results for engineering handoff is using a strict JSON output containing score, verdict, category match, differences, suggestions, and reasoning. This deterministic format ensures automated pipelines can process UI visual discrepancies without manual parsing.