visual-diff-scorer
Testing & QualityMulti-dimensional visual scoring using pixel-diff and structural analysis for design-to-implementation comparison
How to use this skill
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visual-diff-scorer
You are visual-diff-scorer - a specialized skill for multi-dimensional visual scoring using pixel-diff and structural analysis to compare design mocks with implementations.
Overview
This skill enables comprehensive visual comparison between design mocks and implementation screenshots, producing detailed multi-dimensional scores across layout, typography, colors, spacing, components, and decorative elements.
Prerequisites
- Node.js 18+ installed
- Playwright or Puppeteer for screenshot capture
pixelmatchfor pixel-level comparison- Image processing libraries (sharp, jimp)
Capabilities
1. Multi-Dimensional Scoring
Score implementations across 6 dimensions with configurable weights:
const defaultWeights = {
layout: 25, // Structure, positioning, alignment
typography: 20, // Fonts, sizes, weights, spacing
colors: 20, // Colors, gradients, opacity
spacing: 15, // Margins, padding, gaps
components: 10, // Buttons, inputs, cards
decorative: 10 // Icons, illustrations, effects
};
2. Pixel-Diff Analysis
const pixelmatch = require('pixelmatch');
const { PNG } = require('pngjs');
// Compare mock and implementation
const mockImg = PNG.sync.read(fs.readFileSync(mockPath));
const implImg = PNG.sync.read(fs.readFileSync(implPath));
const diff = new PNG({ width, height });
const numDiffPixels = pixelmatch(
mockImg.data,
implImg.data,
diff.data,
width,
height,
{ threshold: 0.1 }
);
const diffPercentage = (numDiffPixels / (width * height)) * 100;
3. Region-Based Analysis
Analyze specific regions for targeted scoring:
const regions = [
{ name: 'header', bounds: { x: 0, y: 0, width: 1920, height: 80 } },
{ name: 'hero', bounds: { x: 0, y: 80, width: 1920, height: 500 } },
{ name: 'content', bounds: { x: 0, y: 580, width: 1920, height: 600 } }
];
for (const region of regions) {
const regionDiff = analyzeRegion(mockImg, implImg, region.bounds);
results.push({ region: region.name, score: regionDiff.score });
}
4. Color Extraction and Comparison
const Vibrant = require('node-vibrant');
// Extract color palette from mock
const mockPalette = await Vibrant.from(mockPath).getPalette();
// Compare with implementation colors
const colorDelta = calculateColorDelta(mockPalette, implPalette);
5. Structural Analysis
// Analyze DOM structure alignment
const mockStructure = await extractStructure(mockAnalysis);
const implStructure = await extractStructure(page);
const structuralScore = compareStructures(mockStructure, implStructure);
Input Schema
{
"type": "object",
"required": ["mockPath", "screenshotPath"],
"properties": {
"mockPath": {
"type": "string",
"description": "Path to design mock image"
},
"screenshotPath": {
"type": "string",
"description": "Path to implementation screenshot"
},
"scoringWeights": {
"type": "object",
"description": "Custom weights for scoring dimensions"
},
"tolerances": {
"type": "object",
"description": "Tolerance thresholds for scoring"
},
"regions": {
"type": "array",
"description": "Specific regions to analyze"
}
}
}
Output Schema
{
"type": "object",
"properties": {
"overallScore": { "type": "number" },
"breakdown": {
"type": "object",
"properties": {
"layout": { "type": "number" },
"typography": { "type": "number" },
"colors": { "type": "number" },
"spacing": { "type": "number" },
"components": { "type": "number" },
"decorative": { "type": "number" }
}
},
"pixelDiff": {
"type": "object",
"properties": {
"percentage": { "type": "number" },
"diffImagePath": { "type": "string" }
}
},
"differences": { "type": "array" },
"feedback": { "type": "array" }
}
}
Process Integration
This skill integrates with:
pixel-perfect-implementation.js- Main convergence processdesign-qa.js- Design QA verificationhifi-prototyping.js- High-fidelity prototype validation
Usage Example
/skill visual-diff-scorer \
--mock designs/homepage-mock.png \
--screenshot artifacts/screenshot.png \
--weights '{"layout":30,"typography":25,"colors":20,"spacing":10,"components":10,"decorative":5}'
Best Practices
- Consistent capture settings - Same viewport, device scale, timing
- Hide dynamic content - Timestamps, animations, ads
- Use appropriate thresholds - Balance precision vs false positives
- Region-based analysis - Focus on critical areas
- Iterative refinement - Track score progression