Compare rendered image stills against POLISH.md design specifications and generate markdown reports.
npx skills add https://github.com/fy538/project-parallax --skill visual-qa-fy538
Please help me install this Agent Skill. Skill: visual-qa Source: https://github.com/fy538/project-parallax/tree/main/skills/visual-qa Command: npx skills add https://github.com/fy538/project-parallax --skill visual-qa-fy538
This Skill addresses the challenge of verifying rendered visual outputs against design standards, reducing manual review time and improving quality control in visual content.
Read the POLISH.md for design rules, run the script to analyze stills in tools/qa/stills/, and review the generated markdown report summarizing the visual quality assessment.
High-intent search queries and answers about installing and using this skill.
Automated visual QA evaluates rendered stills against design specifications to identify visual discrepancies. This Skill uses Python scripts to compare images and generate detailed markdown reports covering spacing, typography, depth, colors, and layout.
Pixel-perfect visual verification ensures rendered visual outputs meet strict aesthetic and layout standards. It reduces manual review time by using automated image analysis to detect visual inconsistencies in production environments.
You check layout and color consistency by running the provided Python script on stills placed in the tools/qa/stills/ directory. The script compares images against POLISH.md design rules to generate a visual quality assessment report.
Yes, Python is required to run the automated visual checks. The Skill relies on Python scripts to perform pixel-level image analysis and generate detailed reports for visual verification tasks.
Yes, this visual verification tool is suitable for production pipelines that require pixel-perfect outputs. It provides an automated, objective evaluation to confirm generated images meet strict aesthetic and layout standards.
The visual quality report analyzes rendered stills against POLISH.md design specifications. It generates actionable feedback on spacing, typography, depth, colors, and layout to identify visual discrepancies.
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