Design Context Extract

---
name: design-context-extract
license: MIT
compatibility: "Claude Code 2.1.277+. Optional: stitch (official Google Stitch) MCP server."
description: "Extract design DNA from app screenshots, live URLs, or screen recordings using Google Stitch — color palettes, typography, spacing tokens, component patterns, and motion specs as design-tokens.json or Tailwind config. Use when the user points to a screenshot, URL, or video and asks to extract or audit the design, analyze animations or scroll behavior, or keep new pages matching an established visual identity."
argument-hint: "[screenshot-path | video-path | url | 'current project']"
context: fork
# user-typed commands stay interactive; CC >= 2.1.218 backgrounds forks by default (#3093)
background: false
user-invocable: true
model: sonnet
agent: design-context-extractor
allowed-tools: "Read Write Glob Grep Bash WebFetch AskUserQuestion TaskCreate TaskUpdate TaskList mcp__stitch__list_projects mcp__plugin_hq-ext_stitch__list_projects mcp__stitch__get_project mcp__plugin_hq-ext_stitch__get_project mcp__stitch__list_screens mcp__plugin_hq-ext_stitch__list_screens mcp__stitch__get_screen mcp__plugin_hq-ext_stitch__get_screen mcp__stitch__generate_screen_from_text mcp__plugin_hq-ext_stitch__generate_screen_from_text"
  # Stitch: body names these five (live stitch.googleapis.com/mcp roster).
  # Grant BOTH prefixes: standalone stitch (mcp__stitch__*) and hq-ext plugin
  # (mcp__plugin_hq-ext_stitch__*). See https://code.claude.com/docs/en/mcp-servers
skills:
  - design-system-tokens
  - remember
  - memory
metadata:
  category: document-asset-creation
  mcp-server: stitch
  version: "1.0.1"
  author: "OrchestKit"
  complexity: "medium"
  tags: "design-context, design-tokens, stitch, extraction, colors, typography, audit, visual-identity"
triggers:
  keywords: ["extract design", "design tokens", "color palette", "typography", "design dna", "visual identity", "design system from", "screen recording", "motion spec", "analyze this video"]
  examples:
    - "extract the design tokens from this screenshot"
    - "what colors and fonts does this app use"
    - "create a design system from this live URL"
    - "analyze the animations in this screen recording"
  anti-triggers: [implement, build, component, explore, brainstorm]
---

# Design Context Extract

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

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

## Pipeline

```
Input (screenshot/URL/project)
  │
  ▼
┌──────────────────────────────┐
│ Capture                       │  Screenshot or fetch HTML/CSS
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ Extract                       │  Stitch get_screen + generate_screen_from_text
│                               │  OR multimodal analysis (fallback)
│ → Colors (hex + oklch)        │
│ → Typography (families, scale)│
│ → Spacing (padding, gaps)     │
│ → Components (structure)      │
└──────────┬───────────────────┘
           │
           ▼
┌──────────────────────────────┐
│ Output                        │  Choose format:
│ → design-tokens.json (W3C)    │
│ → @theme (Tailwind v4)        │
│ → tokens.css (CSS variables)  │
│ → Markdown spec               │
└──────────────────────────────┘
```

## Step 0: Detect Input and Context

```python
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. 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 5.5 / 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:**
```python
# If stitch available:
#   list_projects / get_project to locate the project
#   generate_screen_from_text once for the URL goal (can take minutes; DO NOT RETRY;
#     on timeout poll get_screen every 30 s up to 10 times)
#   list_screens, then get_screen(name="projects/{projectId}/screens/{screenId}")
#   fetch download URLs from the response
# If not: WebFetch the URL and analyze HTML/CSS
```

**For current project:**
```python
Grep("@theme", glob="**/*.css")   # Tailwind v4: theme lives in CSS, not a config file
Glob("**/tailwind.config.*")      # Tailwind v3 only (v4 ignores this file)
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).

```bash
# 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:

| Layer | What to capture |
|-------|-----------------|
| Layout | viewport framing, grids, sticky zones, section order |
| Motion | reveal timing, easing curves, parallax, pinned/scrubbed sections, hover states, loops |
| Visual | same token extraction as screenshots (colors, type, spacing) |
| Rebuild | name the mechanism: CSS transition, IntersectionObserver, GSAP ScrollTrigger, `video.currentTime` scrub, WebGL |

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:**
```python
# Live Stitch MCP tools (https://stitch.googleapis.com/mcp), matching
# design-context-extractor agent grants:
#   - list_projects() / get_project(projectId)
#   - list_screens(projectId)
#   - get_screen(name="projects/{projectId}/screens/{screenId}")
#       ONE required param: name. Returns screen details including download URLs
#   - generate_screen_from_text(projectId, prompt)
#       quota-costing write; can take a few minutes. DO NOT RETRY.
#       On timeout: poll get_screen(name=...) every 30 s, up to 10 times.
#
# Flow: generate_screen_from_text once (or reuse an existing screen) →
# list_screens → get_screen(name=...) → fetch download URLs for multimodal extract.
#
# 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):**
```python
# 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:
```json
{
  "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

```python
AskUserQuestion(questions=[{
  "question": "Output format for extracted tokens?",
  "header": "Format",
  "options": [
    {"label": "Tailwind @theme (Recommended)", "description": "@theme block in the CSS entry (app.css) 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
}])
```

Tailwind v4 is CSS-first: theme values go in an `@theme` block, and `tailwind.config.js`
is ignored entirely (see `ui-components/rules/tailwind-v4-patterns.md`).

```css
/* app.css: the recommended Tailwind output */
@import "tailwindcss";

@theme {
  --color-primary: oklch(0.62 0.21 255);
  --font-sans: "Inter", system-ui, sans-serif;
  --spacing: 0.25rem;
}
```

**Legacy (Tailwind v3 only):** if the project pins v3, emit `tailwind.config.ts` with the
same values under `theme.extend`. Offer this only after confirming the v3 pin in
`package.json`. It is never the default.

## 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:

```python
# 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