design-context-extract

Design Context Extract

Extract the "Design DNA" from existing applications — colors, typography, spacing, and component patterns — and output as structured tokens.

/ork:design-context-extract /tmp/screenshot.png       # From screenshot
/ork:design-context-extract /tmp/recording.mp4         # From screen recording (motion spec)
/ork:design-context-extract https://example.com        # From live URL
/ork:design-context-extract current project            # Scan project's existing styles

Pipeline

Input (screenshot/URL/project)
  │
  ▼
┌──────────────────────────────┐
│ Capture                       │  Screenshot or fetch HTML/CSS
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ Extract                       │  Stitch extract_design_context
│                               │  OR multimodal analysis (fallback)
│ → Colors (hex + oklch)        │
│ → Typography (families, scale)│
│ → Spacing (padding, gaps)     │
│ → Components (structure)      │
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ Output                        │  Choose format:
│ → design-tokens.json (W3C)    │
│ → tailwind.config.ts          │
│ → tokens.css (CSS variables)  │
│ → Markdown spec               │
└──────────────────────────────┘

Step 0: Detect Input and Context

INPUT = ""

# 1. Create main task IMMEDIATELY
TaskCreate(subject="Extract design context: {INPUT}", description="Extract design DNA", activeForm="Extracting design from {INPUT}")

# 2. Create subtasks for each phase
TaskCreate(subject="Detect input type and context", activeForm="Detecting input type")             # id=2
TaskCreate(subject="Capture source material", activeForm="Capturing source")                       # id=3
TaskCreate(subject="Extract design tokens", activeForm="Extracting tokens")                        # id=4
TaskCreate(subject="Choose output format and generate", activeForm="Generating output")            # id=5
TaskCreate(subject="Recommend shadcn/ui style", activeForm="Recommending style")                   # id=6

# 3. Set dependencies for sequential phases
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Capture needs input type detected
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Extraction needs captured source
TaskUpdate(taskId="5", addBlockedBy=["4"])  # Output needs extracted tokens
TaskUpdate(taskId="6", addBlockedBy=["5"])  # Style recommendation needs output

# 4. Before starting each task, verify it's unblocked
task = TaskGet(taskId="2")  # Verify blockedBy is empty

# 5. Update status as you progress
TaskUpdate(taskId="2", status="in_progress")  # When starting
TaskUpdate(taskId="2", status="completed")    # When done — repeat for each subtask

# Determine input type
# "/path/to/file.png" → screenshot
# "/path/to/file.mp4|.mov|.webm|.gif" → screen recording (video pipeline)
# "http..." → URL
# "current project" → scan project styles

Step 1: Capture Source

For screenshots: Read the image directly (Claude is multimodal). Pasted/attached images are compressed to the same token budget as Read tool images (CC 2.1.97), so both workflows are equally efficient.

Resolution budget (Opus 4.8 / CC 2.1.111+): Max input is 2,576 px on the long edge (~3.75 MP) — roughly 3× the Opus 4.6 ceiling. Dense dashboards, dark-mode UIs, and technical diagrams benefit the most from the higher ceiling; extraction reads tiny labels, spacing ticks, and component boundaries that were previously blurred. Below 1,024 px, don't upscale — the source bitmap is the ceiling. Resize only when input exceeds 2,576 px.

For URLs:

# If stitch available: call build_site(prompt=<url + extraction goal>)
#   then get_screen_code / get_screen_image per generated screen
# If not: WebFetch the URL and analyze HTML/CSS

For current project:

Glob("**/tailwind.config.*")
Glob("**/tokens.css")
Glob("**/*.css")  # Look for design token files
Glob("**/theme.*")
# Read and analyze existing style definitions

For screen recordings (video): the only input mode that carries motion — easing, scroll choreography, transitions. Requires ffmpeg/ffprobe (skip with an install hint if missing).

# 1. Probe: duration, dimensions, frame rate
ffprobe -v error -show_entries format=duration,size:stream=width,height,r_frame_rate -of json "$VIDEO"

# 2. Extract frames at timeline beats — NOT uniform thumbnails.
#    Pass A: 1fps sweep to locate transitions; Pass B: re-extract around detected beats.
mkdir -p "$SCRATCHPAD/video-frames"
ffmpeg -y -i "$VIDEO" -vf fps=1 "$SCRATCHPAD/video-frames/frame-%03d.jpg"
# For scroll-heavy or long videos also grab start / middle / end explicitly.

Then Read the extracted frames (multimodal) and analyze in layers:

Video inputs additionally emit a motion-spec.md alongside the token output: per-interaction durations (ms), easing, trigger (scroll/hover/load), and a reduced-motion fallback for each entry. Never describe motion as "smooth" or "nice" — convert taste into mechanism + numbers.

Step 2: Extract Design Context

If stitch MCP is available:

# Official Stitch MCP tools (stitch.withgoogle.com/docs/mcp):
#   - build_site(prompt)          → generates the target design
#   - get_screen_code(screenId)   → React/HTML output per screen
#   - get_screen_image(screenId)  → PNG rasterization per screen
#
# Also consider Figma Dev Mode MCP as a complementary extraction path
# when the source is a Figma file:
#   - get_variable_defs    → design tokens straight from Figma variables
#   - get_design_context   → layout + typography + spacing
#   - search_design_system → locate existing tokens/components

If stitch MCP is NOT available (fallback):

# Multimodal analysis of screenshot:
# - Identify dominant colors (sample from regions)
# - Detect font families and size hierarchy
# - Measure spacing patterns
# - Catalog component types (cards, buttons, headers, etc.)
#
# For URLs: parse CSS custom properties, Tailwind config, computed styles

Extracted data structure:

{
  "colors": {
    "primary": { "hex": "#3B82F6", "oklch": "oklch(0.62 0.21 255)" },
    "secondary": { "hex": "#10B981", "oklch": "oklch(0.69 0.17 163)" },
    "background": { "hex": "#FFFFFF" },
    "text": { "hex": "#1F2937" },
    "muted": { "hex": "#9CA3AF" }
  },
  "typography": {
    "heading": { "family": "Inter", "weight": 700 },
    "body": { "family": "Inter", "weight": 400 },
    "scale": [12, 14, 16, 18, 24, 30, 36, 48]
  },
  "spacing": {
    "base": 4,
    "scale": [4, 8, 12, 16, 24, 32, 48, 64]
  },
  "components": ["navbar", "hero", "card", "button", "footer"]
}

Step 3: Choose Output Format

AskUserQuestion(questions=[{
  "question": "Output format for extracted tokens?",
  "header": "Format",
  "options": [
    {"label": "Tailwind config (Recommended)", "description": "tailwind.config.ts with extracted theme values"},
    {"label": "W3C Design Tokens", "description": "design-tokens.json following W3C DTCG spec"},
    {"label": "CSS Variables", "description": "tokens.css with CSS custom properties"},
    {"label": "Markdown spec", "description": "Human-readable design specification document"}
  ],
  "multiSelect": false
}])

Step 4: Generate Output

Write the extracted tokens in the chosen format. If the project already has tokens, show a diff of what's new vs existing.

Step 5: Recommend Best-Fit shadcn/ui Style

After extracting design DNA, map the extracted characteristics to the best-fit shadcn/ui v4 style:

# Map extracted design DNA → shadcn style recommendation
radius = extracted["radius"]      # e.g., "large", "pill", "none", "small"
density = extracted["spacing"]    # e.g., "generous", "balanced", "compact", "dense"
elevation = extracted["shadows"]  # e.g., "layered", "subtle", "none"

STYLE_MAP = {
    # (radius, density, elevation) → style
    ("pill/large", "generous", "layered"):  "Luma — polished, macOS-like",
    ("medium",     "balanced", "subtle"):   "Vega — general purpose",
    ("medium",     "compact",  "subtle"):   "Nova — dense dashboards",
    ("large",      "generous", "subtle"):   "Maia — soft, consumer-facing",
    ("none/sharp", "balanced", "none"):     "Lyra — editorial, dev tools",
    ("small",      "dense",    "none"):     "Mira — ultra-dense data",
}
# Present recommendation with the style picker URL:
# "Based on extracted design DNA, recommended style: Luma"
# "Pick and install: https://ui.shadcn.com/create  (select 'Luma' style)"
# Apply to existing project (CLI v4 apply command, Apr 2026):
# "$ npx shadcn@latest apply luma"

Skip condition: If the user only needs raw tokens (not a shadcn project), skip this step.

Anti-Patterns

• NEVER guess colors without analyzing the actual source — use precise extraction
• NEVER skip the oklch conversion — all colors must have oklch equivalents
• NEVER output flat token structures — use three-tier hierarchy (global/alias/component)

Quality Bar

Done means all of these hold:

• Every color was sampled from the actual source, has an oklch equivalent, and carries a role name
• Typography includes family, weight, and the observed size scale — not "modern sans-serif"
• Output file written in the chosen format and verified to parse (JSON/TS/CSS)
• Video inputs: motion-spec.md names mechanism + duration + easing + reduced-motion fallback per interaction
• If the project already had tokens, the diff of new-vs-existing was shown

Related Skills

• ork:design-to-code — Full pipeline that uses this as Stage 1
• ork:design-system-tokens — Token architecture and W3C spec compliance
• ork:component-search — Find components that match extracted patterns