name	design-system-cli
description	Complete Design System Reverse Engineering (DSRE) + Functional Cloning CLI - Extract visual design (tokens, assets) AND functional behavior (workflows, state machines, business rules) from any web application
license	MIT
allowed-tools	Read, Write, Edit, Bash, Grep, Glob

Design System CLI Skill

Complete reverse engineering toolkit for cloning BOTH the visual design AND functional behavior of web applications.

Overview

The design-system-cli provides two complementary capabilities:

Part A: Visual Cloning (Phases 1-8)

Design Tokens: Colors, typography, spacing, border-radius, shadows, breakpoints
Visual Assets: Images, videos, SVG icons, background images with metadata
Asset Prompts: LLM-ready prompts for recreating extracted visual assets
Component Generation: React components from HTML structure
Hard Fork & Rebrand: Transform sites into legally clean templates

Part B: Functional Cloning (Phases 9-12)

Interaction Recording: Capture user sessions with clicks, typing, network calls
Flow Extraction: Identify screens and user journeys
Entity Inference: Extract data models from API payloads
Business Rules: State machines, validation rules, permissions
App Generation: Generate working Next.js applications from behavioral data

Result: Clone BOTH the look AND behavior of any web application

Core Capabilities

Part A: Visual Cloning (Phases 1-8)

1. Design Token Extraction

Command: ds pipeline --url <url> --framework <framework>

Frameworks Supported:

react-tailwind - Tailwind CSS config for React
vue-tailwind - Tailwind CSS config for Vue
react-mui - Material-UI theme for React
angular-material - Angular Material theme
css-variables - Plain CSS custom properties

Output Files:

raw-tokens.json - Raw extracted tokens
normalized-tokens.json - Normalized design system
<framework>.config.js - Framework-specific config

Example:

ds pipeline --url https://labs.google/aifuturesfund/ --framework react-tailwind

2. Visual Asset Extraction

Command: ds pipeline --url <url> --assets-out <path>

Extracts:

Images (<img> tags) with dimensions, alt text, aspect ratio
Videos (<video> tags) with dimensions, controls, autoplay state
SVG icons and illustrations
Background images from computed styles
Dominant colors from each asset

Asset Metadata:

id: Unique identifier (img-1, video-1, svg-1, bg-1)
type: image | video | icon | illustration | animation
role: hero | thumbnail | avatar | background | decoration | content
src: Asset URL
alt: Alternative text (images only)
aspectRatio: Width:height ratio (16:9, 1:1, etc.)
dominantColors: Array of prominent colors
dimensions: {width, height} in pixels

Output:

{
  "page": "https://example.com",
  "capturedAt": "2025-11-19T13:18:46.363Z",
  "assets": [
    {
      "id": "img-1",
      "type": "image",
      "role": "hero",
      "src": "https://example.com/hero.webp",
      "alt": "Hero image description",
      "aspectRatio": "16:9",
      "dominantColors": ["#1a73e8", "#ffffff"],
      "dimensions": {"width": 1920, "height": 1080}
    }
  ]
}

3. Asset Prompt Generation

Command: ds assets-prompts --assets <assets.json> --out <asset-prompts.json>

Generates LLM-ready prompts for image recreation via fal-ai models:

Output Format:

{
  "page": "https://example.com",
  "items": [
    {
      "id": "img-1",
      "type": "image",
      "target_model": "fal-ai/image",
      "prompt": "TODO: describe hero for https://example.com",
      "negative_prompt": "text, watermark, logos, UI chrome",
      "guidance": {
        "aspect_ratio": "16:9",
        "color_palette": ["#1a73e8", "#ffffff"],
        "style_notes": "role: hero",
        "role": "hero"
      }
    }
  ]
}

Workflow:

Extract assets with ds pipeline --assets-out assets.json
Generate prompts with ds assets-prompts --assets assets.json --out asset-prompts.json
Review and enhance prompts with asset context
Use prompts with fal-ai image generation models
Replace original assets with AI-generated versions

4. Figma Integration

Command: ds figma --tokens <normalized-tokens.json> --out <figma-plugin/>

Generates a ready-to-use Figma plugin with:

Color styles from token palette
Text styles from typography tokens
Interactive UI for import confirmation
Complete manifest.json configuration

Plugin Usage:

Generate plugin: ds figma --tokens normalized-tokens.json
Load plugin in Figma: Plugins → Development → Import from manifest
Run plugin to import design tokens as Figma styles

Part B: Functional Cloning (Phases 9-12)

Clone the behavior and workflows of applications, not just the visual design.

5. Interaction Recording (Phase 9)

Command: ds trace --url <url> --out trace.json --duration <ms>

Captures:

User interactions (clicks, typing, navigation, scrolls)
Network requests and responses with full payloads
DOM snapshots at each screen transition
Form submissions and API calls
Session replay data

Output: trace.json with complete interaction history

Example:

ds trace --url https://gradual.com --out gradual-trace.json --duration 30000

Use Cases:

Record user workflows for analysis
Document complex application flows
Capture API contracts and data models
Build behavioral test suites

6. Flow Extraction (Phase 10)

Command: ds flows --trace <trace.json> --out-screens <screens.json> --out-flows <flows.json>

Extracts:

Distinct screens from DOM snapshots
User journeys and navigation patterns
Screen transition triggers
Flow confidence scoring

Outputs:

screens.json - Unique screens with metadata
flows.json - User journeys with step-by-step actions

Example:

ds flows --trace gradual-trace.json --out-screens screens.json --out-flows flows.json

Use Cases:

Map application navigation structure
Document user journeys
Identify key user paths
Analyze flow complexity

7. Entity Inference (Phase 11)

Command: ds entities --trace <trace.json> --out entities.json

Infers:

Data models from network payloads
Field types and schemas (string, number, boolean, date-time, array, object)
Required vs optional fields
CRUD operations per entity
Field relationships and metadata

Output: entities.json with complete data model

Example:

ds entities --trace gradual-trace.json --out entities.json

Use Cases:

Reverse engineer database schemas
Document API contracts
Generate TypeScript types
Build data models for new systems

8. Business Rules Extraction (Phase 12)

Command: ds rules --trace <trace.json> --flows <flows.json> --entities <entities.json> --out rules.json

Extracts:

State machines from entity status fields
State transitions from user flows
Validation rules from field types and API errors
Permission rules from CRUD operation patterns
Business rules for workflows and immutability

Output: rules.json with complete rule system

Example:

ds rules \
  --trace gradual-trace.json \
  --flows flows.json \
  --entities entities.json \
  --out rules.json

Rule Types:

State Machines: Lifecycle states and transitions (draft → active → archived)
Validation Rules: Field constraints (required, email, max:500, min:3)
Permission Rules: Authorization requirements (create, update, delete, approve)
Business Rules: Workflow logic and field immutability

9. Functional App Generation (Phase 12)

Command: ds fx-codegen --screens <screens.json> --flows <flows.json> --entities <entities.json> --rules <rules.json> --out <dir>

Generates:

Complete Next.js 14 application with App Router
Typed API clients for each entity
List and detail pages for all entities
TypeScript types from entity schemas
React components with validation
README with functionality spec

Output: Working Next.js application

Example:

ds fx-codegen \
  --screens screens.json \
  --flows flows.json \
  --entities entities.json \
  --rules rules.json \
  --out apps/gradual-clone

Generated Structure:

apps/gradual-clone/
├── app/
│   ├── layout.tsx          # App shell with navigation
│   ├── [entity]s/
│   │   ├── page.tsx       # List page
│   │   └── [id]/page.tsx  # Detail page
├── lib/
│   ├── types.ts           # TypeScript types from entities
│   └── api/
│       └── [entity]s.ts   # CRUD API clients
├── package.json           # Next.js 14 dependencies
└── README.md             # Functionality spec

Complete Workflows

Workflow 1: Complete Functional Clone (Phases 9-12)

Goal: Clone an entire SaaS application (like Gradual, Linear, or Notion)

# Step 1: Record user interactions (30 seconds)
ds trace --url https://gradual.com --out gradual-trace.json --duration 30000

# Step 2: Extract screens and user flows
ds flows --trace gradual-trace.json --out-screens gradual-screens.json --out-flows gradual-flows.json

# Step 3: Infer data models from API calls
ds entities --trace gradual-trace.json --out gradual-entities.json

# Step 4: Extract business rules and state machines
ds rules \
  --trace gradual-trace.json \
  --flows gradual-flows.json \
  --entities gradual-entities.json \
  --out gradual-rules.json

# Step 5: Generate working Next.js application
ds fx-codegen \
  --screens gradual-screens.json \
  --flows gradual-flows.json \
  --entities gradual-entities.json \
  --rules gradual-rules.json \
  --out apps/gradual-clone

# Step 6: Run the cloned application
cd apps/gradual-clone
npm install
npm run dev  # http://localhost:3000

Result: Complete working application with:

All screens and navigation
Full data models and TypeScript types
State machines and lifecycle management
Validation rules and permissions
CRUD operations and API clients

Workflow 2: Combined Visual + Functional Clone

Goal: Clone BOTH the look AND behavior of an application

# Part A: Visual Cloning
ds pipeline --url https://example.com --framework react-tailwind --assets-out assets.json
ds assets-prompts --assets assets.json --out asset-prompts.json

# Part B: Functional Cloning
ds trace --url https://example.com --out trace.json --duration 30000
ds flows --trace trace.json --out-screens screens.json --out-flows flows.json
ds entities --trace trace.json --out entities.json
ds rules --trace trace.json --flows flows.json --entities entities.json --out rules.json
ds fx-codegen --screens screens.json --flows flows.json --entities entities.json --rules rules.json --out apps/full-clone

# Part C: Merge Results
# Copy visual assets into functional app
cp -r design-system/* apps/full-clone/
# Use generated Tailwind config
cp tailwind.config.js apps/full-clone/

# Run complete clone
cd apps/full-clone
npm install
npm run dev

Result: Pixel-perfect visual clone with complete functional behavior

Use Cases

Visual Cloning Use Cases

Full Design System Extraction

# Extract everything from a website
ds pipeline --url https://example.com --framework react-tailwind --assets-out assets.json

# Generate asset recreation prompts
ds assets-prompts --assets assets.json --out asset-prompts.json

# Generate Figma plugin
ds figma --tokens normalized-tokens.json --out figma-plugin/

Functional Cloning Use Cases

Clone SaaS Application Workflows

# Record and clone key user journeys
ds trace --url https://linear.app --out linear-trace.json
ds flows --trace linear-trace.json --out-screens screens.json --out-flows flows.json
ds fx-codegen --screens screens.json --flows flows.json --entities entities.json --rules rules.json --out apps/linear-clone

Use Cases:

Recreate project management workflows (Linear, Asana, Jira)
Clone CRM functionality (Salesforce, HubSpot)
Reverse engineer SaaS features for competitive analysis
Build similar apps with custom branding

Document Legacy Applications

# Record enterprise application behavior
ds trace --url https://sap-system.company.com --out sap-trace.json
ds flows --trace sap-trace.json --out-screens sap-screens.json --out-flows sap-flows.json
ds entities --trace sap-trace.json --out sap-entities.json

Use Cases:

Document undocumented legacy systems
Extract business rules from old applications
Modernize legacy workflows
Create training materials and documentation

Extract API Contracts

# Capture all API calls and payloads
ds trace --url https://api-driven-app.com --out api-trace.json
ds entities --trace api-trace.json --out api-entities.json

Use Cases:

Reverse engineer API specifications
Generate TypeScript types from live APIs
Document third-party integrations
Build API client libraries

Asset Recreation Workflow

# 1. Extract visual assets with metadata
ds pipeline --url https://example.com --assets-out assets.json

# 2. Generate LLM-ready prompts
ds assets-prompts --assets assets.json --out asset-prompts.json

# 3. Review asset-prompts.json and enhance TODO prompts with descriptions
# Example: Change "TODO: describe hero for https://example.com"
#       To: "Modern abstract geometric pattern in blue and white, minimalist design, hero banner"

# 4. Use enhanced prompts with fal-ai for image generation
# 5. Replace original assets with AI-generated versions in your codebase

Token-Only Extraction

# Extract design tokens without assets
ds extract --url https://example.com --out raw-tokens.json
ds normalize --raw raw-tokens.json --out normalized-tokens.json
ds codegen --normalized normalized-tokens.json --framework react-tailwind

Integration with SuperClaude

Auto-Activation Keywords

Visual Cloning:

design system, extract tokens, recreate page
Tailwind config, MUI theme, component library
design tokens, Figma, design analysis
visual assets, asset recreation, image generation
fal-ai, asset prompts, regenerate images

Functional Cloning:

clone application, reverse engineer, extract workflows
trace interactions, record behavior, user flows
state machine, business rules, validation rules
data models, API contracts, TypeScript types
functional clone, behavior extraction, workflow analysis

File Pattern Detection

tailwind.config.*, theme.*, tokens.*
*.design.json
assets.json, asset-prompts.json
trace.json, flows.json, entities.json, rules.json
screens.json

Typical Operations

Visual Operations:

extract - Extract raw design tokens from website
normalize - Normalize tokens to standard format
generate - Generate framework-specific configs
analyze - Analyze design system consistency
recreate - Recreate page with extracted design system
regenerate_assets - Regenerate visual assets with AI
create_prompts - Create LLM-ready asset prompts

Functional Operations:

trace - Record user interactions and network calls
extract_flows - Extract screens and user journeys
infer_entities - Infer data models from API calls
extract_rules - Extract business rules and state machines
generate_app - Generate functional Next.js application
clone_behavior - Clone application workflows
document_system - Document legacy application behavior

Command Integration

Visual Cloning:

/build + design-system-cli → Extract and generate configs
/analyze + design-system-cli → Analyze design consistency
/improve + design-system-cli → Enhance design system
/design + design-system-cli → Design system workflows

Functional Cloning:

/analyze + design-system-cli → Analyze application workflows
/document + design-system-cli → Document application behavior
/build + design-system-cli → Generate functional clone
/troubleshoot + design-system-cli → Debug workflow extraction

Asset Context Schema

Complete schema documentation: docs/asset_context_schema.md

Core Types:

Asset: Individual visual asset with metadata
AssetContext: Complete collection of assets from a page
AssetPromptEntry: LLM-ready prompt for asset recreation
AssetPromptsFile: Collection of prompts for all assets

Asset Roles (auto-detected):

hero: Large prominent images in header/hero sections
thumbnail: Small preview images in grids/lists
avatar: Profile pictures and user avatars
background: Background images and patterns
decoration: Icons, illustrations, SVG graphics
content: Main content images and media

Architecture

Monorepo Structure:

packages/
# Visual Cloning (Phases 1-8)
├── core/                   # Shared TypeScript interfaces
├── extractor/              # Playwright-based token and asset extraction
├── normalizer/             # Token normalization and standardization
├── codegen/                # Framework config generation + asset prompt generation
├── figma-bridge/           # Figma plugin generation

# Functional Cloning (Phases 9-12)
├── functional-trace/       # User interaction and network recording
├── functional-flows/       # Screen and flow extraction
├── functional-entities/    # Data model inference from APIs
├── functional-rules/       # Business rules and state machine extraction
└── functional-codegen/     # Next.js application scaffold generation

Complete Processing Pipeline:

VISUAL CLONING:
Website → Playwright → Raw Extraction → Normalization → Code Generation
                    ↓
                Assets.json → Asset Prompts → fal-ai → Regenerated Assets

FUNCTIONAL CLONING:
Website → User Interaction → Trace Recording → Flow Extraction
                                             ↓
                                    Entity Inference → Rules Extraction
                                             ↓
                                    Next.js App Generation

COMBINED:
Visual Design System + Functional Application = Complete Clone

Examples

Example 1: Extract from Google AI Futures Fund

ds pipeline --url https://labs.google/aifuturesfund/ --framework react-tailwind --assets-out assets.json

Output:

tailwind.config.js with extracted color palette, typography, spacing
assets.json with 92 visual assets (images, videos, SVGs)

Example 2: Generate Asset Recreation Prompts

ds assets-prompts --assets assets.json --out asset-prompts.json

Output:

asset-prompts.json with 92 LLM-ready prompts for fal-ai

Example 3: Full Workflow

# Extract design system and assets
ds pipeline --url https://carbondesi

gnsystem.com --framework react-tailwind --assets-out carbon-assets.json

# Generate asset prompts
ds assets-prompts --assets carbon-assets.json --out carbon-prompts.json

# Generate Figma plugin
ds figma --tokens normalized-tokens.json --out figma-plugin/

# Result:
# - tailwind.config.js (design tokens)
# - carbon-assets.json (92 assets with metadata)
# - carbon-prompts.json (LLM-ready recreation prompts)
# - figma-plugin/ (ready-to-use Figma plugin)

Known Limitations

Asset Extraction:
- SVG className handling (fixed with getClassName() helper)
- Background images only from computed styles
- No lazy-loaded image detection
Prompt Generation:
- Generic TODO placeholders require manual enhancement
- No automatic prompt quality assessment
- Single target model (fal-ai/image)
Token Extraction:
- JavaScript-rendered content requires headless browser
- Dynamic CSS-in-JS may be missed
- Some design tokens may be inferred incorrectly

Testing

E2E Test:

# Full pipeline test with real website
node bin/ds pipeline --url https://labs.google/aifuturesfund/ --framework react-tailwind --assets-out test-assets.json

# Verify outputs
ls -lh tailwind.config.js test-assets.json

# Generate and verify prompts
node bin/ds assets-prompts --assets test-assets.json --out test-prompts.json
ls -lh test-prompts.json

Documentation

README.md - Project overview and getting started
docs/asset_context_schema.md - Complete asset schema documentation
SKILL.md - This file (SuperClaude integration guide)

Contributing

When enhancing this skill:

Update SKILL.md with new capabilities
Add examples for new workflows
Update ORCHESTRATOR.md keywords if needed
Maintain backward compatibility with existing outputs

Resources

GitHub: (Repository URL)
Documentation: docs/
Examples: See Examples section above
fal-ai Models: https://fal.ai/models (for asset recreation)

Install Skill

SKILL.md

Design System CLI Skill

Overview

Part A: Visual Cloning (Phases 1-8)

Part B: Functional Cloning (Phases 9-12)

Core Capabilities

Part A: Visual Cloning (Phases 1-8)

1. Design Token Extraction

2. Visual Asset Extraction

3. Asset Prompt Generation

4. Figma Integration

Part B: Functional Cloning (Phases 9-12)

5. Interaction Recording (Phase 9)

6. Flow Extraction (Phase 10)

7. Entity Inference (Phase 11)

8. Business Rules Extraction (Phase 12)

9. Functional App Generation (Phase 12)

Complete Workflows

Workflow 1: Complete Functional Clone (Phases 9-12)

Workflow 2: Combined Visual + Functional Clone

Use Cases

Visual Cloning Use Cases

Full Design System Extraction

Functional Cloning Use Cases

Clone SaaS Application Workflows

Document Legacy Applications

Extract API Contracts

Asset Recreation Workflow

Token-Only Extraction

Integration with SuperClaude

Auto-Activation Keywords

File Pattern Detection

Typical Operations

Command Integration

Asset Context Schema

Architecture

Examples

Example 1: Extract from Google AI Futures Fund

Example 2: Generate Asset Recreation Prompts

Example 3: Full Workflow

Known Limitations

Testing

Documentation

Contributing

Resources