🌍 Universal Deep Accessibility Analyzer Skill

Skill Definition

Name: deep-accessibility-analyzer Version: 2.0.0 Description: Enterprise-grade WCAG 2.2 deep analysis with VoiceOver simulation, visual analysis, screenshot-based color detection, semantic analysis, and multi-page crawling (40+ pages)

Capabilities

1. VoiceOver Deep Simulation

• Real macOS VoiceOver integration via Guidepup
• Keyboard navigation testing (Tab, Shift+Tab, Arrow keys)
• Landmark navigation (R, C, F, H keys)
• Heading hierarchy navigation (1-6 keys)
• Form interaction testing
• Modal dialog testing
• Focus trap detection
• Screen reader announcements validation

2. Visual Analysis (Full-Page Screenshot)

• Full-page color screenshot capture (not grayscale)
• Color contrast analysis (WCAG 1.4.3)
• Visual hierarchy detection
• Layout breakage detection at different viewports
• Text clipping/overflow detection
• Interactive element visibility check
• Focus indicator visibility validation

3. Semantic & Meaning Analysis

• Content meaning coherence
• Link context appropriateness
• Image alt text relevance (AI-powered)
• Form label clarity
• Error message helpfulness
• Navigation logic flow
• Cognitive load assessment

4. Multi-Disability Coverage

• Blind users: Screen reader compatibility, keyboard navigation
• Low vision: Color contrast, zoom 200%/400%, text spacing
• Motor impairments: Keyboard accessibility, timing adjustments
• Cognitive: Clear language, consistent navigation, error prevention
• Hearing: Captions, transcripts, visual alternatives

5. Intelligent Multi-Page Crawling

• Minimum 40 pages per scan
• Same-domain only (no external links)
• Depth-first + breadth-first hybrid
• Loop prevention with visited set
• Dynamic route discovery (SPA support)
• Priority pages: Forms, Products, Checkout, Navigation
• Rate limiting: 3-5 seconds between pages (human-like)

6. Security Stealth Mode

• Human-like browsing patterns
• Random delays between actions
• Natural scroll behavior
• Realistic mouse movements
• Proper User-Agent rotation
• No automation detection flags
• Cloudflare/WAF bypass

7. AI Strategy (Gemini 2.5 Flash)

• Token-efficient analysis
• Smart batching (group similar issues)
• Progressive analysis (critical first)
• Context-aware prompting
• No full DOM sending (snippets only)
• Cache results to avoid re-analysis
• Limit: ~50,000 tokens per page max

Output Requirements

Detailed Issue Reports (NOT summaries)

For EACH issue:

1. Exact location: URL + CSS selector + XPath
2. Screenshot: Annotated with issue highlighted
3. Code snippet: Actual HTML from page
4. WCAG mapping: Criterion + Level + Success/Failure
5. Disability impact: Which user groups affected
6. Root cause: Why this fails
7. Technical solution: Copy-paste ready code fix
8. Priority: Critical/Serious/Moderate/Minor
9. Effort estimate: Dev hours to fix
10. Business impact: Legal/UX/SEO impact

Process Analysis

• Scan timeline (start/end per page)
• Pages discovered vs scanned
• Issues per page breakdown
• Trend analysis (improving/worsening)
• Comparison with industry benchmarks

Final Deliverables

1. HTML Report: Professional, accessible, with charts
2. JSON Report: Machine-readable, API-ready
3. Markdown Report: Human-readable summary
4. Jira Tickets: One per issue, ready to import
5. CSV Export: For Excel analysis
6. Screenshots Folder: Annotated images per issue

Technical Stack

• Browser: Playwright (Chromium + WebKit for Safari simulation)
• Screen Reader: Guidepup (macOS VoiceOver)
• AI: Gemini 2.5 Flash (Google AI Studio)
• Screenshots: Playwright full-page + element screenshots
• Color Analysis: node-color-contrast + custom algorithms
• Crawling: Custom BFS/DFS hybrid with priority queue
• Storage: Local filesystem + optional S3

Performance Targets

• Pages per hour: 40-60 (with deep analysis)
• Token usage: <100k tokens per 10 pages average
• False positive rate: <5%
• Issue detection accuracy: >95%
• Report generation: <2 minutes after scan complete

Error Handling

• Retry failed pages (max 3 attempts)
• Skip inaccessible pages (log reason)
• Continue on AI API errors (use deterministic fallback)
• Graceful degradation (partial reports OK)
• Detailed error logging for debugging

Usage Example

# Full deep scan (40+ pages)
node deep-accessibility-analyzer.js https://www.arcelik.com.tr --pages=40 --depth=5

# Quick scan (10 pages)
node deep-accessibility-analyzer.js https://example.com --pages=10

# Single page deep dive
node deep-accessibility-analyzer.js https://example.com/product/123 --single

# With VoiceOver (requires macOS)
node deep-accessibility-analyzer.js https://example.com --voiceover

# Export formats
node deep-accessibility-analyzer.js https://example.com --format=html,json,md,jira,csv

Configuration

const CONFIG = {
  // Scan settings
  minPages: 40,
  maxPages: 100,
  maxDepth: 5,
  timeout: 60000,
  delayBetweenPages: 4000,
  
  // AI settings
  geminiModel: 'gemini-2.5-flash',
  maxTokensPerPage: 50000,
  tokenBudget: 500000, // Total per scan
  
  // Screenshot settings
  fullPageScreenshot: true,
  elementScreenshots: true,
  annotateIssues: true,
  
  // VoiceOver settings
  enableVoiceOver: true, // macOS only
  voiceOverRate: 300, // Words per minute
  
  // Output
  outputDir: './audits',
  formats: ['html', 'json', 'md', 'jira', 'csv'],
  
  // Stealth
  stealthMode: true,
  randomDelays: true,
  humanScrolling: true
};

Success Criteria

✅ Minimum 40 pages scanned ✅ Full-page color screenshots for all pages ✅ VoiceOver simulation completed ✅ Color contrast analysis for all text elements ✅ Semantic coherence validated by AI ✅ No security triggers (WAF/Cloudflare bypassed) ✅ Detailed issue reports (not summaries) ✅ Copy-paste ready code fixes ✅ Jira tickets generated ✅ Process timeline documented ✅ Under token budget

This skill replaces all previous WCAG scanning scripts. Default behavior: Deep, comprehensive, production-ready analysis.