website-replicator

name	website-replicator
description	Analyze and replicate website UI/animations. Use when asked to clone, replicate, or analyze a website's visual implementation.

Website Replicator

Reconstruct website UI and scroll animations through systematic observation.

Core Principle

Observe effects, don't extract code. We watch what changes and when, then generate equivalent code.

Screenshots are analysis tools, not just artifacts. They help Claude understand structure and animation, feeding back into the exploration strategy. The workflow is a continuous feedback loop:

• Take screenshot → analyze with Gemini → digest results → inform next DOM queries
• Multiple screenshots → compare changes → understand animation → guide sampling strategy
• Not a separate pipeline - screenshots inform and adjust the exploration in real-time

---

WORKFLOW

Phase 1: Initial Load & Overview

Goal: Get oriented, understand what we're working with

1. navigate_page(url, waitUntil: 'networkidle')
2. wait_for(3000)  -- let initial animations settle
3. take_screenshot(fullPage: true) → save as capture/overview.png
4. take_snapshot() → get accessibility tree
5. AUTOMATED: Call Gemini API with overview.png
   python scripts/gemini_analyzer.py overview capture/overview.png > data/gemini-overview.json

Output:

• capture/overview.png → Screenshot captured
• data/gemini-overview.json → Automated Gemini analysis with:
  • Layout structure (sections, pattern, breakpoints)
  • Likely animated elements (type, location, animation style)
  • Design patterns (colors, typography, spacing)
  • Notable elements (graphics, videos, interactive components)
• Accessibility snapshot → DOM structure reference

No manual intervention required - Gemini analysis happens automatically

---

Phase 2: DOM Reconnaissance

Goal: Build watch list of elements likely to animate

Strategy (try in order, use what works):

ATTEMPT 1: Class/attribute heuristics
  evaluate_script → search for elements with:
    - Classes containing: animate, fade, slide, scroll, reveal, parallax, aos, gsap
    - Data attributes: data-aos, data-scroll, data-animate, data-speed
    - will-change set to anything other than 'auto'
  
ATTEMPT 2: Transform/opacity detection
  evaluate_script → find elements where:
    - transform !== 'none'
    - opacity !== '1'
    - These often indicate "starting state" of animations

ATTEMPT 3: Structural analysis (FLEXIBLE - let Chrome tell us what exists)
  evaluate_script → explore actual DOM structure:
    ```javascript
    () => {
      // Don't assume structure - discover it
      const sections = document.querySelectorAll('section, [class*="section"], .w-section');
      const cards = document.querySelectorAll('[class*="card"], [class*="item"]');
      const animated = document.querySelectorAll('[class*="fade"], [class*="slide"], [class*="animate"]');

      // Get real positions and dimensions
      return Array.from(new Set([...sections, ...cards, ...animated])).map(el => ({
        tag: el.tagName,
        classes: el.className,
        offsetTop: el.offsetTop,
        height: el.offsetHeight,
        // Calculate when this element would be interesting to capture
        viewportTrigger: el.offsetTop - window.innerHeight * 0.5
      }));
    }
    ```

  Claude inspects results → decides screenshot positions based on actual element locations
  - If elements clustered: fewer screenshots
  - If elements sparse: more screenshots
  - Adaptive, not programmatic

ATTEMPT 4: Smart scroll sampling (FLEXIBLE - adapt based on what we see)
  Step 1: Ask Chrome what's on the page
    evaluate_script → get element positions (see ATTEMPT 3)

  Step 2: Claude decides scroll positions
    - Around each major element's trigger point
    - Before/during/after viewport intersection
    - Not fixed intervals - based on actual content

  Step 3: For each position, capture with context
    evaluate_script → scroll to position
    evaluate_script → ask "what's visible now?"
      ```javascript
      () => {
        const visible = Array.from(document.querySelectorAll('section, [class*="card"]'))
          .filter(el => {
            const rect = el.getBoundingClientRect();
            return rect.top < window.innerHeight && rect.bottom > 0;
          })
          .map(el => ({
            tag: el.tagName,
            id: el.id,
            classes: el.className.split(' ').slice(0, 3).join(' ')
          }));

        return {
          scrollY: window.scrollY,
          viewportHeight: window.innerHeight,
          visibleElements: visible
        };
      }
      ```
    take_screenshot → save with semantic name based on what's visible

  Claude can compare consecutive screenshots → skip if too similar

  FALLBACK: If DOM analysis unclear, use visual comparison
    take_screenshot at strategic positions (top, 1/4, 1/2, 3/4, bottom)
    AUTOMATED: Call Gemini API to compare screenshots
    python scripts/gemini_analyzer.py compare capture/scroll-*.png > data/gemini-animation-detection.json
    Parse JSON response → extract changed_elements list

FALLBACK: Manual specification
  Ask user: "I couldn't automatically detect animated elements.
            Can you tell me which sections/elements animate on this page?"

Output: watchList[] - array of CSS selectors to monitor

• Built from DOM heuristics OR Gemini visual analysis OR user input

---

Phase 3: Animation Capture

Goal: Record how watched elements change during scroll

FLEXIBLE APPROACH - Let Chrome guide the sampling:

1. First, understand the scroll range and element positions
   evaluate_script → get page metrics and element locations:
     ```javascript
     () => {
       const elements = Array.from(document.querySelectorAll('[data-watched]')); // or watchList
       return {
         scrollHeight: document.documentElement.scrollHeight,
         viewportHeight: window.innerHeight,
         elements: elements.map(el => ({
           selector: el.id ? `#${el.id}` : el.className,
           offsetTop: el.offsetTop,
           height: el.offsetHeight
         }))
       };
     }
     ```

2. Claude decides sampling strategy based on results
   - If page is short (< 3000px): sample every 100px
   - If page is long (> 10000px): sample at element trigger points + every 500px
   - Adaptive, not hardcoded

3. For each sample position, ask Chrome what's happening
   evaluate_script → scroll to position
   evaluate_script → sample current state:
     ```javascript
     (selector) => {
       const el = document.querySelector(selector);
       if (!el) return null;
       const cs = getComputedStyle(el);
       const rect = el.getBoundingClientRect();
       return {
         scrollY: window.scrollY,
         transform: cs.transform,
         opacity: cs.opacity,
         rect: { top: rect.top, left: rect.left, width: rect.width, height: rect.height },
         // Let Chrome tell us what's actually set
         willChange: cs.willChange,
         filter: cs.filter
       };
     }
     ```

4. Claude inspects results → decides if more samples needed
   - If values changing rapidly: sample more densely in that range
   - If values stable: skip ahead
   - Flexible, responsive to actual animation behavior

ALTERNATIVE: Inject monitoring script (if page allows)
  - Only if CSP permits and page structure is simple
  - Otherwise, use manual sampling above

Output: Animation data - properties at each scroll position, adaptively sampled

---

Phase 4: CSS Extraction

Goal: Capture static styles for replication

1. Extract computed styles (FLEXIBLE - based on what we found)
   For elements Claude identified as important:
     evaluate_script → getComputedStyle(el)
     → Capture: colors, fonts, spacing, borders, shadows, layout

   Claude decides which elements matter based on Phase 2 discovery

2. AUTOMATED: Extract design tokens via Gemini
   python scripts/gemini_analyzer.py tokens capture/overview.png > data/design-tokens.json
   → Provides: color palette, typography scale, spacing system, effects

3. Capture actual stylesheet content (if accessible)
   list_network_requests → filter for .css files
   → Note URLs for reference

4. Responsive behavior (ADAPTIVE)
   Claude asks: "Does this page look responsive?"
   - Check if viewport meta tag exists
   - Check if CSS has media queries

   If responsive:
     Take screenshots at key breakpoints (Claude decides based on media queries found)
     evaluate_script → sample key element styles at each size

   If not responsive:
     Skip responsive testing

Output:

• data/styles.json - Computed styles from DOM
• data/design-tokens.json - Design system from Gemini analysis
• capture/responsive-*.png - Responsive screenshots

---

Phase 5: Asset Collection

Goal: Identify images, fonts, icons needed

1. list_network_requests → filter by type:
   - Images: .png, .jpg, .jpeg, .webp, .svg, .gif
   - Fonts: .woff, .woff2, .ttf, .otf
   - Icons: svg inline, icon fonts

2. Extract inline SVGs
   evaluate_script → find all <svg> elements
   → Capture outerHTML

3. Extract image references
   evaluate_script → find all <img> elements
   → Capture src, alt, dimensions

4. Note external dependencies
   list_network_requests → identify CDN resources
   → Font services (Google Fonts, Adobe Fonts)
   → Icon libraries (FontAwesome, etc.)

---

Phase 6: Analysis & Generation

Goal: Transform captured data into usable code

Animation analysis (FLEXIBLE - Claude interprets the data):

Claude examines animation data for each watched element:
  - Identifies when values start changing (animation start)
  - Identifies when values stabilize (animation end)
  - Determines what properties changed (opacity, translateY, scale, etc.)
  - Infers trigger type by comparing scroll position to element position:
    * Immediate (starts at scroll 0)
    * Viewport-based (starts when element enters view)
    * Fixed position (starts at specific scroll value)
  - Infers easing by examining the curve of value changes

Claude adapts analysis based on what it actually sees in the data

Component structure (AUTOMATED):

OPTIONAL: For detailed component breakdown
  python scripts/gemini_analyzer.py components capture/overview.png main > data/components.json
  → Provides component hierarchy for code generation

Generate code:

Output in preferred format:
  - GSAP + ScrollTrigger (default)
  - CSS scroll-driven animations
  - Framer Motion (if React project)
  - Plain CSS transitions + IntersectionObserver

Include:
  - Initial state (CSS)
  - Animation code (JS or CSS)
  - Trigger configuration

Verification (OPTIONAL):

After generating output:
  1. Render generated code locally
  2. take_screenshot → capture/generated-output.png
  3. AUTOMATED: Compare with original
     python scripts/gemini_analyzer.py verify capture/overview.png capture/generated-output.png > data/verification.json
  4. Review priority_fixes from JSON response
  5. Iterate on fixes

---

DECISION POINTS

When something doesn't work:

Situation	Action
Can't find animated elements	Try structural analysis → Try visual comparison → Ask user
Script injection blocked	Use manual sampling approach
getComputedStyle returns unexpected values	Take screenshot, send to Gemini for visual analysis
Scroll doesn't work	Check for scroll hijacking, try scrollTo on body/documentElement
Elements not found by selector	Re-analyze DOM, try different selector strategy
Page requires interaction first	Look for modals/cookies, dismiss them, retry

When to use Gemini:

AUTOMATED - No manual intervention:

• Initial overview understanding (Phase 1)
• Visual comparison between scroll states (Phase 2)
• Design token extraction (Phase 4)
• Component breakdown (Phase 6, optional)
• Output verification (Phase 6, optional)

How Gemini integrates with the workflow:

1. Claude takes screenshot(s) at strategic moments
2. Calls Python helper: python scripts/gemini_analyzer.py <command> <args>
3. Gemini returns structured analysis (JSON)
4. Claude digests the response - extracts key insights
5. Claude merges insights into current understanding - adjusts strategy
6. Claude proceeds with informed DOM queries or next steps

Example flow:

• Screenshot overview → Gemini says "3 card sections likely animate"
• Claude searches DOM for cards → finds .card-item at specific positions
• Claude takes screenshots at those positions → Gemini confirms "fade + slide up"
• Claude samples opacity/transform at those positions → generates code

All Gemini calls happen via Python helper:

python scripts/gemini_analyzer.py <command> <args>

See docs/gemini-integration.md for implementation details.

---

TOOLS USAGE

Chrome DevTools MCP tools used:

navigate_page     - Load the target URL
wait_for          - Allow page/animations to settle  
take_screenshot   - Visual capture for Gemini analysis
take_snapshot     - Accessibility tree for structure
evaluate_script   - All DOM inspection and monitoring
resize_page       - Responsive testing
list_network_requests - Find CSS/assets

Key evaluate_script patterns:

// Get unique selector for element
function getSelector(el) {
  if (el.id) return '#' + el.id;
  if (el.className) {
    const classes = [...el.classList].join('.');
    const siblings = document.querySelectorAll(el.tagName + '.' + classes);
    if (siblings.length === 1) return el.tagName.toLowerCase() + '.' + classes;
  }
  // Fall back to nth-child
  const parent = el.parentElement;
  const index = [...parent.children].indexOf(el);
  return getSelector(parent) + ' > :nth-child(' + (index + 1) + ')';
}

// Sample element state
function sampleElement(selector) {
  const el = document.querySelector(selector);
  if (!el) return null;
  const cs = getComputedStyle(el);
  const rect = el.getBoundingClientRect();
  return {
    transform: cs.transform,
    opacity: cs.opacity,
    top: rect.top,
    left: rect.left,
    width: rect.width,
    height: rect.height
  };
}

Note: These are starting points. Adapt based on what the actual page structure looks like.

---

OUTPUT STRUCTURE

/output/
├── capture/
│   ├── overview.png
│   ├── responsive-375.png
│   ├── responsive-768.png
│   ├── responsive-1024.png
│   ├── responsive-1440.png
│   └── scroll-samples/
│       └── scroll-{n}.png (if needed)
├── data/
│   ├── dom-structure.json
│   ├── animation-data.json
│   ├── styles.json
│   └── assets.json
├── analysis/
│   └── animation-breakdown.md
└── generated/
    ├── components/
    ├── styles/
    └── animations/

---

ADAPTABILITY

This skill should adapt, not fail. When the "standard" approach doesn't work:

1. Observe more - Take more screenshots, sample more positions
2. Try alternatives - Different selectors, different timing, different tools
3. Ask for help - Use Gemini for visual analysis, ask user for hints
4. Partial success is fine - Capture what you can, note what you couldn't

The goal is useful output, not perfect automation.