visual-verdict

Automate visual QA by using a skill that compares UI screenshots against reference images to generate a structured JSON verdict for pass/fail and suggest edits. This ensures visual consistency between generated UIs and reference designs.

Screenshot validation generates actionable feedback by analyzing image differences and outputting a structured JSON verdict. This verdict indicates a pass or fail status and provides specific suggestions for the next iteration of UI edits.

You cannot use this skill without additional dependencies, as it requires image comparison and analysis libraries to function. You must ensure your environment has the necessary image processing libraries installed before running the visual QA automation.

The best way to verify visual consistency is to run an automated screenshot-to-reference comparison. This process evaluates the generated UI screenshot against the reference image and produces a structured JSON verdict to guide any necessary edits.

Visual QA automation requires image analysis libraries because the skill relies on these tools to perform pixel-level screenshot comparison and detect visual discrepancies. Without these libraries, the skill cannot process the images or generate the structured JSON verdict.

A limitation of automated screenshot validation is that it requires pre-existing reference images to function correctly. Additionally, the accuracy of the JSON verdict depends entirely on the capabilities of the underlying image comparison and analysis libraries used.