Extract Design System

Extract Design System from Website

Reverse-engineer a complete design system from a live website using two passes:

1. CSS source analysis — fetch and parse stylesheets for declared values
2. Headed Playwright extraction — visually browse the site, interact with elements, extract computed styles, and capture screenshots at real device resolutions

Prerequisites

• Playwright installed (npx playwright install chromium if not set up)
• web_fetch tool for fetching raw HTML/CSS source

Quick Start

Run the extraction script against the target URL:

node scripts/extract.mjs <url> [outDir] [subpage1,subpage2,...]

Examples:

node scripts/extract.mjs https://example.com
node scripts/extract.mjs https://example.com ./my-tokens "/about,/pricing,/docs"
node scripts/extract.mjs http://localhost:3000 extracted "/dashboard,/settings"

The script opens a visible browser (headless: false) so you can watch the extraction. It uses real device profiles for accurate screenshots:

Mobile and tablet use Playwright's built-in device descriptors (correct UA, isMobile, hasTouch) so sites serving different layouts render correctly.

Output Structure

{outDir}/
├── screenshots/
│   ├── pages/               — full-page at 4 breakpoints × each page
│   │   ├── home-mobile.png
│   │   ├── home-desktop.png
│   │   ├── home-dark-mode.png
│   │   └── ...
│   └── components/          — isolated component + hover/focus states
│       ├── nav.png
│       ├── button-primary.png
│       ├── button-primary-hover.png
│       ├── button-primary-focus.png
│       ├── nav-mobile-open.png
│       └── ...
└── raw/
    ├── colors.json          — CSS custom properties + computed colors
    ├── colors-dark.json     — dark mode variables (if detected)
    ├── typography.json      — font families, type styles, @font-face
    ├── spacing.json         — spacing, radii, shadows, borders, max-widths
    └── components.json      — component manifest + responsive layout data

Workflow

Step 1: Run the Extraction Script

node scripts/extract.mjs "TARGET_URL" "extracted-design-system" "/about,/pricing"

The script automatically:

• Extracts CSS custom properties and computed colors from 800 sampled elements
• Captures typography from all semantic elements (h1-h6, p, a, button, etc.)
• Measures spacing, radii, shadows, and borders
• Screenshots every page at 4 breakpoints with real device contexts
• Isolates and screenshots 17 common component types
• Captures hover and focus states on interactive elements
• Tests mobile nav by clicking hamburger-like buttons at iPhone viewport
• Detects dark mode via prefers-color-scheme emulation and toggle sniffing

Step 2: CSS Source Analysis (supplementary)

The script captures computed values. This step catches declared values from CSS source that may not be active on the current page:

# Fetch stylesheets identified in the HTML
web_fetch <stylesheet-url>

# Look for things the computed pass might miss:
# - Media query breakpoints
# - Keyframe animations
# - CSS custom property definitions with fallbacks
# - Commented-out or unused styles that reveal design intent

Step 3: Organize Raw Data into Tokens

Review raw/colors.json, raw/typography.json, raw/spacing.json and the screenshots. Then organize into a structured token file.

Colors → Palette

Group extracted colors into categories:

Deduplicate near-identical colors (ΔE < 3 in OKLCH space). A site with 47 slightly different grays probably intended 5-8.

Typography → Scale

Map extracted font sizes to a named scale:

Spacing → Grid

Snap raw pixel values to the nearest standard grid:

4px grid: 4, 8, 12, 16, 20, 24, 32, 40, 48, 64, 80, 96
8px grid: 8, 16, 24, 32, 40, 48, 56, 64, 80, 96

Pick whichever grid the extracted values align to with the least rounding.

Components → Inventory

For each component in the screenshots, document:

• Visual appearance (size, colors, spacing)
• Variants observed (different sizes, states)
• Interactive behavior (from hover/focus screenshots)
• Closest shadcn-svelte equivalent

Step 4: Compile Token File

Write the organized data to tokens.json:

{
  "$schema": "extracted-design-tokens",
  "source": "https://example.com",
  "extracted": "2026-03-09",
  "framework_detected": "tailwind|bootstrap|custom|none",
  "dark_mode": true,
  "colors": {
    "light": {
      "primary": { "50": "#...", "500": "#...", "900": "#..." },
      "neutral": { "50": "#...", "950": "#..." }
    },
    "dark": { }
  },
  "typography": {
    "fontFamilies": { "sans": "...", "mono": "..." },
    "scale": { "h1": { "size": "36px", "weight": 700, "lineHeight": 1.2 } }
  },
  "spacing": { "grid": "4px", "scale": [0, 4, 8, 12, 16, 24, 32, 48, 64, 96] },
  "radii": { "sm": "4px", "md": "8px", "lg": "12px" },
  "shadows": { "sm": "...", "md": "...", "lg": "..." },
  "components": { "buttons": { "variants": ["primary", "secondary"] } }
}

Step 5: Seed Penpot (optional)

If requested, create a Penpot project from the tokens using pi-penpot:

1. Create project named after the source site
2. Create a "Design System" page with color swatches, type samples, spacing ruler
3. Create pages for each major screen, importing screenshots as references
4. Build components matching the extracted inventory

Step 6: Generate Tailwind Config (optional)

Map tokens to a Tailwind config with CSS custom properties for runtime theming.

Tips

• Pass 3-5 subpages for broader component coverage
• CSS custom properties are gold — sites using them have self-documented tokens
• Watch the browser — headed mode reveals cookie banners, auth walls, and popups that block extraction
• Interactive states matter — hover/focus screenshots catch design intent static captures miss
• Don't over-extract — focus on intentional design decisions, not every computed value
• Remove slowMo in the script for faster extraction once verified
• Check for dark mode — the script detects it automatically via both prefers-color-scheme and toggle buttons