Design To Code logo

Design To Code

Community
yonatangross
design-to-code

Mockup-to-component pipeline using Google Stitch, 21st.dev, and Storybook MCP. Accepts a screenshot, a description, or a URL and produces production-ready React components, checking existing Storybook components before generating anything new. Use when implementing UI from a mockup or screenshot. To call the MCP tool surface on its own, with no design to convert, use storybook-mcp-integration.

Overview

Publisheryonatangross
Repositoryorchestkit
Skill namedesign-to-code
Stars
298
Forks
35
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yonatangross on GitHub. Read the source before you install it.

Installation

Install the Design To Code AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/design-to-code
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yonatangross/orchestkit.git /tmp/orchestkit
mkdir -p .claude/skills
cp -r /tmp/orchestkit/plugins/ork/skills/design-to-code .claude/skills/design-to-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design To Code in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Design To Code on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Design To Code is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design to Code

Convert visual designs into production-ready React components using a four-stage pipeline: Extract, Match, Adapt, Render.

bash
design-to-code screenshot of hero section    # From description
design-to-code /tmp/mockup.png               # From screenshot
design-to-code https://example.com/pricing    # From URL

Pipeline Overview

Input (screenshot/description/URL)
  │
  ▼
┌─────────────────────────┐
│ Stage 1: EXTRACT         │  Stitch MCP → screen details + URLs
│ generate_screen_from_text│  Create screens from a text prompt
│ get_screen               │  Screen details with download URLs
└─────────┬───────────────┘
          │
          ▼
┌─────────────────────────┐
│ Stage 2: MATCH           │  1. Storybook MCP → check existing
│ Storybook-first lookup   │  2. 21st.dev → search public registry
│ Then 21st.dev fallback   │  3. Filesystem → grep codebase
└─────────┬───────────────┘
          │
          ▼
┌─────────────────────────┐
│ Stage 3: ADAPT           │  Merge extracted design + matched
│ Apply project tokens     │  components into final implementation
│ Customize to codebase    │  Tests + types included
└─────────┬───────────────┘
          │
          ▼
┌─────────────────────────┐
│ Stage 4: RENDER          │  Register as json-render catalog entry
│ Generate Zod schema      │  Same component → PDF, email, video
│ Add to defineCatalog()   │  Multi-surface reuse via MCP output
└─────────┬───────────────┘
          │
          ▼
┌─────────────────────────┐
│ Stage 4b: VERIFY         │  Storybook MCP → self-healing loop
│ run-story-tests(a11y)    │  Fix violations, retry (max 3)
│ preview-stories          │  Embed live preview in chat
└─────────────────────────┘

Argument Resolution

python
INPUT = ""  # Full argument string
# Detect input type:
# - Starts with "/" or "~" or contains ".png"/".jpg" → screenshot file path
# - Starts with "http" → URL to screenshot or live page
# - Otherwise → natural language description

Step 0: Detect Input Type and Project Context

python
# 1. Create main task IMMEDIATELY
TaskCreate(subject="Design to code: {INPUT}", description="Four-stage pipeline: extract, match, adapt, render", activeForm="Converting design to code")

# 2. Create subtasks for each stage
TaskCreate(subject="Extract design context", activeForm="Extracting design context")               # id=2
TaskCreate(subject="Match components (Storybook-first)", activeForm="Matching components")         # id=3
TaskCreate(subject="Adapt to project tokens and conventions", activeForm="Adapting to project")    # id=4
TaskCreate(subject="Register in json-render catalog", activeForm="Registering in catalog")         # id=5
TaskCreate(subject="Verify with Storybook self-healing", activeForm="Verifying component")         # id=6

# 3. Set dependencies for sequential stages
TaskUpdate(taskId="3", addBlockedBy=["2"])  # Match needs extracted design context
TaskUpdate(taskId="4", addBlockedBy=["3"])  # Adapt needs matched components
TaskUpdate(taskId="5", addBlockedBy=["4"])  # Render needs adapted component
TaskUpdate(taskId="6", addBlockedBy=["5"])  # Verify needs rendered component

# 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

# Detect project's design system
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("**/.tokens.json")
# Read existing tokens if found → used in Stage 3

# Detect shadcn/ui style (v4 style system)
Glob("**/components.json")
# Read → style field (e.g., "radix-luma", "base-nova")
# Determines class names: Luma=rounded-4xl, Nova=compact, Lyra=sharp
# Store: SHADCN_STYLE for Stage 2 filtering + Stage 3 adaptation

Stage 1: Extract Design Context

If stitch MCP is available:

python
# Live Stitch MCP tools (https://stitch.googleapis.com/mcp), matching
# design-context-extractor / design-system-architect 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 (fetch those URLs; no separate code/image export tool)
#   - 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.
#
# For screenshot/URL input:
#   1. list_projects / get_project to locate the Stitch project
#   2. generate_screen_from_text with the extraction goal (or use an
#      existing screen if the project already has one). Never retry.
#   3. list_screens, then get_screen(name="projects/{projectId}/screens/{screenId}")
#   4. Fetch the download URLs (or Read multimodal) for token extraction
#
# For description input:
#   1. generate_screen_from_text(projectId, prompt=<description>) once
#   2. On timeout, poll get_screen(name=...) every 30 s up to 10 times
#   3. Otherwise get_screen(name="projects/{projectId}/screens/{screenId}")
#
# DESIGN.md import (Stitch Pro, Mar 2026+):
#   Prefer upload_design_md / create_design_system_from_design_md when
#   those tools are available on the live server; otherwise regenerate
#   via generate_screen_from_text from the DESIGN.md contents (once, no retry).

If stitch MCP is NOT available (fallback):

python
# For screenshot: Read the image file directly (Claude is multimodal)
# Analyze layout, colors, typography, spacing from the image
# For URL: WebFetch the page, extract HTML structure
# For description: Skip extraction, proceed to Stage 2 with description

Resolution budget (Opus 5.5 / CC 2.1.111+): Mockups up to 2,576 px on the long edge (~3.75 MP, 3× prior ceiling) produce better component boundaries and spacing extraction. Full-page desktop mockups at native resolution are now in-budget; previously they had to be resized down and lost fine detail. Only downscale inputs exceeding 2,576 px.

Extract and produce:

  • Color palette (hex/oklch values)
  • Typography (font families, sizes, weights)
  • Spacing patterns (padding, margins, gaps)
  • Component structure (headers, cards, buttons, etc.)
  • Layout pattern (grid, flex, sidebar, etc.)

Stage 2: Match Components (Storybook-First)

Priority 1 — Check project's own Storybook (if storybook-mcp available):

python
# Search the project's existing component library first
inventory = list-all-documentation()  # Full component + docs manifest
for component in inventory.components:
    if component matches extracted_description:
        details = get-documentation(id=component.id)
        # Returns: props schema, stories, usage patterns
        # → Existing component found — skip external search

Priority 2 — Search 21st.dev (if no Storybook match and 21st-dev-magic available):

python
# Search 21st.dev for matching components
# Use the component descriptions from Stage 1
# Example: "animated pricing table with toggle"
# Filter: React, Tailwind CSS, shadcn/ui compatible
# If SHADCN_STYLE detected, prefer components matching style's visual language
# (e.g., Luma → rounded/pill-shaped, Nova → compact/dense, Lyra → sharp/boxy)

Priority 3 — Filesystem fallback (if no MCP servers available):

python
# Search for components in the project's existing codebase
Grep(pattern="export.*function|export.*const", glob="**/*.tsx")
# Check for shadcn/ui components
Glob("**/components/ui/*.tsx")
# Generate from scratch if no matches found

Present matches to user:

python
AskUserQuestion(questions=[{
  "question": "Which component approach for {component_name}?",
  "header": "Component",
  "options": [
    {"label": "Reuse from Storybook", "description": "{existing_component} — props: {prop_list}"},
    {"label": "Use 21st.dev match", "description": "{matched_component_name} — {match_score}% match"},
    {"label": "Adapt from codebase", "description": "Modify existing {existing_component}"},
    {"label": "Generate from scratch", "description": "Build new component from extracted design"}
  ],
  "multiSelect": false
}])

Stage 3: Adapt to Project

Merge the extracted design context with matched/generated components:

  1. Apply project tokens — Replace hardcoded colors/spacing with project's design tokens
  2. Apply shadcn style classes — If SHADCN_STYLE detected, use style-correct class names:
    • Luma: rounded-4xl buttons/cards, shadow-md + ring-1 ring-foreground/5, gap-6 py-6
    • Nova: compact px-2 py-1, reduced margins, tight spacing
    • Lyra: rounded-none, sharp edges, monospace-friendly
    • Mira: ultra-dense, minimal padding
  3. Match naming conventions — Follow project's component naming patterns
  4. Add TypeScript types — Full type safety with Zod validation for any data props
  5. Include tests — MSW handlers for API-backed components, render tests for static
  6. Responsive — Mobile-first with breakpoints matching project's system

Output Structure

src/components/
  └── {ComponentName}/
      ├── {ComponentName}.tsx       # Main component
      ├── {ComponentName}.test.tsx  # Tests
      └── index.ts                 # Barrel export

Stage 4: Register in json-render Catalog

After ADAPT produces a working React component, register it as a json-render catalog entry for multi-surface reuse.

  1. Generate Zod schema — Derive a Zod schema from the component's TypeScript props
  2. Add catalog entry — Register in the project's defineCatalog() call with props schema and children declaration
  3. Verify rendering — Confirm the component renders correctly through <Render catalog={catalog} /> path
typescript
import { z } from 'zod'

// Zod schema derived from {ComponentName}Props
const componentSchema = z.object({
  title: z.string().max(100),
  variant: z.enum(['default', 'featured']).default('default'),
  // ... props from the adapted component
})

// Add to project catalog
import { defineCatalog } from '@json-render/core'
import { existingCatalog } from './catalog'

export const catalog = {
  ...existingCatalog,
  {ComponentName}: {
    props: componentSchema,
    children: true, // or false for leaf components
  },
}

Enables: same component output to PDF, email, video, or MCP response — no reimplementation needed.

Skip condition: If the project has no json-render dependency or catalog, inform the user and skip catalog registration. The component from Stage 3 is still fully usable standalone.

Stage 4b: Self-Healing Verification (if storybook-mcp available)

After generating the component, verify it with Storybook MCP:

python
# 1. Write CSF3 story for the new component
Write("src/components/{Name}/{Name}.stories.tsx", story_code)

# 2. Run tests via MCP (component + a11y)
results = run-story-tests(
    stories=[{ "storyId": "{name}--default" }],
    a11y=True
)

# 3. Handle failures — self-heal up to 3 attempts
if not results.all_passed:
    for failure in results.failures:
        # Read violation details, fix the component
        Edit("src/components/{Name}/{Name}.tsx", fix)
    # Re-run failing tests
    results = run-story-tests(stories=[...], a11y=True)

# 4. Preview — embed live story in chat for user confirmation
previews = preview-stories(stories=[
    { "absoluteStoryPath": "src/components/{Name}/{Name}.stories.tsx",
      "exportName": "Default" }
])
# Include preview URLs in response for visual confirmation

Skip condition: If storybook-mcp is not available, skip verification. The component is still usable — just not auto-verified.

Graceful Degradation

stitch21st-dev-magicstorybook-mcpBehavior
AvailableAvailableAvailableFull pipeline: extract + Storybook-first match + adapt + render + self-heal verify
AvailableAvailableUnavailableExtract + 21st.dev match + adapt + render (no verification)
AvailableUnavailableAvailableExtract + Storybook match + adapt + verify
UnavailableAvailableAvailableDescription-based search + Storybook check + adapt + verify
UnavailableUnavailableAvailableFilesystem search + Storybook verify only
UnavailableUnavailableUnavailableManual analysis + generate from scratch (still works)

The skill ALWAYS produces output regardless of MCP availability. Storybook MCP adds component reuse (Stage 2) and self-healing verification (Stage 4b) — skipping them still yields a working component.

Anti-Patterns

  • NEVER output components with hardcoded colors — use design tokens
  • NEVER skip TypeScript types — all props must be typed
  • NEVER generate without checking existing project patterns first
  • NEVER ignore the project's existing component library structure

Quality Bar

Done means all of these hold:

  • Generated components carry no hardcoded colors or literal spacing — every visual value resolves through project design tokens.
  • Every prop is TypeScript-typed; data-bearing props carry Zod validation.
  • Stage 2 checked existing components (Storybook, then 21st.dev, then filesystem) before generating from scratch, and the chosen match source is named.
  • When storybook-mcp is available, the component passes run-story-tests (component + a11y) before handoff, self-healing within at most 3 attempts.
  • Output follows the {ComponentName}/ structure — component, test, and barrel export.
  • Each skipped stage (json-render catalog, Storybook verify) is taken ONLY when its dependency is absent, and the skip is stated with its reason.

Related Skills

  • storybook-mcp-integration — Storybook MCP tools: component discovery, testing, previews
  • component-search — Search 21st.dev registry standalone
  • design-context-extract — Extract design DNA from screenshots
  • design-stylecards — named aesthetic recipes (shadow stacks, glass surfaces, gradients, type scales) for the polish pass on generated components
  • design-system-tokens — Token architecture and management
  • storybook-testing — CSF3 patterns, Vitest integration, Chromatic TurboSnap
  • json-render-catalog — Catalog definition, Zod schemas, defineCatalog patterns
  • multi-surface-render — Render catalog entries to PDF, email, video, MCP

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Design To Code AI skill do?

Mockup-to-component pipeline using Google Stitch, 21st.dev, and Storybook MCP. Accepts a screenshot, a description, or a URL and produces production-ready React components, checking existing Storybook components before generating anything new. Use when implementing UI from a mockup or screenshot. To call the MCP tool surface on its own, with no design to convert, use storybook-mcp-integration.

Why use Design To Code on TypingMind?

Because you install it once and use it with any model. Design To Code is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Design To Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/design-to-code. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Design To Code?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Design To Code?

As many as you like. As long as a model supports skills, you can use Design To Code with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Design To Code AI skill free?

Yes. It is published on GitHub by yonatangross under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇