a5c-ai/mock-spec-extractor

mock-spec-extractor

You are mock-spec-extractor - a specialized skill for extracting comprehensive design specifications from mock images.

Overview

This skill analyzes design mock images to extract structured specifications including colors, typography, spacing patterns, and component details that serve as the source of truth for pixel-perfect implementation.

Prerequisites

• Node.js 18+ installed
• Image processing libraries (sharp, jimp)
• Color extraction libraries (node-vibrant, color-thief)
• OCR capabilities for text analysis (optional)

Capabilities

1. Color Palette Extraction

const Vibrant = require('node-vibrant');

async function extractColors(mockPath) {
  const palette = await Vibrant.from(mockPath).getPalette();

  return {
    primary: palette.Vibrant?.hex,
    secondary: palette.Muted?.hex,
    accent: palette.DarkVibrant?.hex,
    background: palette.LightMuted?.hex,
    text: palette.DarkMuted?.hex,
    allColors: Object.entries(palette)
      .filter(([_, swatch]) => swatch)
      .map(([name, swatch]) => ({
        name,
        hex: swatch.hex,
        rgb: swatch.rgb,
        population: swatch.population
      }))
  };
}

2. Layout Structure Analysis

async function analyzeLayout(mockPath) {
  const image = await sharp(mockPath).metadata();

  // Detect major sections through edge detection
  const edges = await detectEdges(mockPath);

  // Identify grid patterns
  const gridAnalysis = await detectGridPattern(edges);

  return {
    dimensions: { width: image.width, height: image.height },
    sections: identifySections(edges),
    grid: gridAnalysis,
    hierarchy: buildHierarchy(sections)
  };
}

3. Typography Detection

async function detectTypography(mockPath, regions) {
  const textStyles = [];

  for (const region of regions) {
    // Extract text regions
    const textAreas = await findTextAreas(mockPath, region);

    for (const area of textAreas) {
      textStyles.push({
        region: region.name,
        estimatedSize: estimateFontSize(area),
        estimatedWeight: estimateWeight(area),
        color: extractDominantColor(area),
        position: area.bounds
      });
    }
  }

  return deduplicateStyles(textStyles);
}

4. Spacing Pattern Recognition

async function analyzeSpacing(mockPath, elements) {
  const spacingValues = [];

  // Analyze gaps between elements
  for (let i = 0; i < elements.length - 1; i++) {
    const gap = calculateGap(elements[i], elements[i + 1]);
    spacingValues.push(gap);
  }

  // Identify spacing scale
  const scale = identifySpacingScale(spacingValues);

  return {
    scale,
    patterns: groupByPattern(spacingValues),
    recommendations: suggestCSSVariables(scale)
  };
}

5. Component Detection

async function detectComponents(mockPath) {
  const components = [];

  // Detect buttons
  const buttons = await detectButtons(mockPath);
  components.push(...buttons.map(b => ({ type: 'button', ...b })));

  // Detect cards
  const cards = await detectCards(mockPath);
  components.push(...cards.map(c => ({ type: 'card', ...c })));

  // Detect inputs
  const inputs = await detectInputs(mockPath);
  components.push(...inputs.map(i => ({ type: 'input', ...i })));

  return components;
}

Input Schema

{
  "type": "object",
  "required": ["mockSource"],
  "properties": {
    "mockSource": {
      "type": "object",
      "properties": {
        "type": { "type": "string", "enum": ["image", "figma", "url"] },
        "path": { "type": "string" }
      }
    },
    "analysisDepth": {
      "type": "string",
      "enum": ["basic", "detailed", "comprehensive"],
      "default": "detailed"
    },
    "focusAreas": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
}

Output Schema

{
  "type": "object",
  "properties": {
    "success": { "type": "boolean" },
    "designSpec": {
      "type": "object",
      "properties": {
        "layout": { "type": "object" },
        "typography": { "type": "object" },
        "colorPalette": { "type": "object" },
        "spacing": { "type": "object" },
        "components": { "type": "array" },
        "decorativeElements": { "type": "array" }
      }
    },
    "cssVariables": { "type": "object" },
    "implementationNotes": { "type": "array" }
  }
}

Process Integration

This skill integrates with:

• pixel-perfect-implementation.js - Provides mock analysis for convergence
• design-system.js - Extracts design tokens
• component-library.js - Identifies component patterns

Usage Example

/skill mock-spec-extractor \
  --mock designs/dashboard-mock.png \
  --depth comprehensive \
  --focus "header,sidebar,cards"

Best Practices

1. High-resolution mocks - Use 2x resolution for better analysis
2. Clean backgrounds - Solid backgrounds improve detection
3. Consistent naming - Name regions consistently for tracking
4. Validate extractions - Review and refine extracted specs
5. Iterate with feedback - Use scoring feedback to improve extraction