Agent skill

design-context-extract

Extract design DNA from existing app screenshots or live URLs using Google Stitch. Produces color palettes, typography specs, spacing tokens, and component patterns as design-tokens.json or Tailwind config. Use when auditing an existing design, creating a design system from a live app, or ensuring new pages match an established visual identity.

View SKILL.md on GitHub Repository
Stars 143
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/yonatangross/orchestkit/tree/main/src/skills/design-context-extract

Metadata

Additional technical details for this skill

category
document-asset-creation
mcp server
stitch

SKILL.md

Design Context Extract

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

bash
/ork:design-context-extract /tmp/screenshot.png       # From screenshot
/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

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. 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
# "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.

For URLs:

python
# If stitch available: use get_screen + get_project
# If not: WebFetch the URL and analyze HTML/CSS

For current project:

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

Step 2: Extract Design Context

If stitch MCP is available:

python
# Use official Stitch MCP tools: get_screen, get_project, list_screens
# Returns structured design data: colors, typography, layout, 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 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:

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 preset code link:
# "Based on extracted design DNA, recommended style: Luma"
# "Configure: https://ui.shadcn.com/create?preset=b2D0xPaDb"

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)

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

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

yonatangross/orchestkit

expect

Diff-aware AI browser testing — analyzes git changes, generates targeted test plans, and executes them via agent-browser. Reads git diff to determine what changed, maps changes to affected pages via route map, generates a test plan scoped to the diff, and runs it with pass/fail reporting. Use when testing UI changes, verifying PRs before merge, running regression checks on changed components, or validating that recent code changes don't break the user-facing experience.

143 15
Explore
yonatangross/orchestkit

github-operations

GitHub CLI operations for issues, PRs, milestones, and Projects v2. Covers gh commands, REST API patterns, and automation scripts. Use when managing GitHub issues, PRs, milestones, or Projects with gh.

143 15
Explore
yonatangross/orchestkit

chain-patterns

Chain patterns for CC 2.1.71 pipelines — MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building multi-phase pipeline skills. Loaded via skills: field by pipeline skills (fix-issue, implement, brainstorm, verify). Not user-invocable.

143 15
Explore
yonatangross/orchestkit

storybook-mcp-integration

Storybook MCP server integration for component-aware AI development. Covers 6 tools across 3 toolsets (dev, docs, testing): component discovery via list-all-documentation/get-documentation, story previews via preview-stories, and automated testing via run-story-tests. Use when generating components that should reuse existing Storybook components, running component tests via MCP, or previewing stories in chat.

143 15
Explore
yonatangross/orchestkit

component-search

Search 21st.dev component registry for production-ready React components. Finds components by natural language description, filters by framework and style system, returns ranked results with install instructions. Use when looking for UI components, finding alternatives to existing components, or sourcing design system building blocks.

143 15
Explore
yonatangross/orchestkit

ai-ui-generation

AI-assisted UI generation patterns for json-render, v0, Bolt, and Cursor workflows. Covers prompt engineering for component generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.

143 15
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results