directory changes and restructuring

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

custom-skills/17-seo-gateway-architect/code/CLAUDE.md

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+# CLAUDE.md
+
+## Overview
+
+SEO gateway page strategist for Korean medical/service websites. Creates keyword strategies, content architecture, and technical SEO plans.
+
+## Quick Start
+
+```bash
+pip install -r scripts/requirements.txt
+
+# Keyword analysis
+python scripts/keyword_analyzer.py --topic "눈 성형" --market "강남"
+```
+
+## Scripts
+
+| Script | Purpose |
+|--------|---------|
+| `keyword_analyzer.py` | Analyze keywords, search volume, competitor gaps |
+
+## Keyword Analyzer
+
+```bash
+# Basic analysis
+python scripts/keyword_analyzer.py --topic "눈 성형"
+
+# With location targeting
+python scripts/keyword_analyzer.py --topic "눈 성형" --market "강남" --output strategy.json
+
+# Competitor analysis
+python scripts/keyword_analyzer.py --topic "눈 성형" --competitors url1,url2
+```
+
+## Output
+
+Generates strategic document with:
+- Primary keyword + monthly search volume
+- LSI keywords (7-10)
+- User intent distribution
+- Competitor gap analysis
+- Content architecture (H1-H3 structure)
+- Technical SEO checklist
+
+## Templates
+
+See `templates/` for:
+- `keyword-research-template.md`
+- `content-architecture-template.md`
+- `seo-checklist-template.md`
+
+## Workflow
+
+1. Run keyword analyzer for target topic
+2. Review search volume and intent data
+3. Use output to plan content architecture
+4. Hand off to `18-seo-gateway-builder` for content generation
+
+## Configuration
+
+```bash
+# Optional: API keys for enhanced data
+GOOGLE_API_KEY=xxx
+NAVER_API_KEY=xxx
+```

custom-skills/17-seo-gateway-architect/code/scripts/keyword_analyzer.py

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+#!/usr/bin/env python3
+"""
+Keyword Analyzer for SEO Gateway Pages
+Analyzes keywords and generates SEO strategy recommendations
+"""
+
+import json
+from typing import Dict, List, Tuple
+from dataclasses import dataclass
+from datetime import datetime
+
+@dataclass
+class KeywordData:
+    """Data structure for keyword information"""
+    keyword: str
+    search_volume: int
+    difficulty: float
+    intent: str
+    cpc: float = 0.0
+    trend: str = "stable"
+
+class KeywordAnalyzer:
+    """Analyzes keywords for SEO gateway pages"""
+    
+    def __init__(self, primary_keyword: str):
+        self.primary_keyword = primary_keyword
+        self.results = {
+            "primary": None,
+            "lsi": [],
+            "long_tail": [],
+            "questions": [],
+            "intent_distribution": {},
+            "recommendations": []
+        }
+    
+    def analyze_primary_keyword(self) -> KeywordData:
+        """
+        Analyzes the primary keyword
+        In production, this would call actual keyword research APIs
+        """
+        # Simulated data - replace with actual API calls
+        keyword_data = {
+            "눈 성형": {"volume": 12000, "difficulty": 65, "intent": "informational", "cpc": 2500},
+            "이마 성형": {"volume": 5500, "difficulty": 55, "intent": "informational", "cpc": 3000},
+            "동안 성형": {"volume": 8000, "difficulty": 70, "intent": "comparative", "cpc": 2800},
+        }
+        
+        data = keyword_data.get(self.primary_keyword, {
+            "volume": 1000, 
+            "difficulty": 50, 
+            "intent": "informational",
+            "cpc": 1000
+        })
+        
+        self.results["primary"] = KeywordData(
+            keyword=self.primary_keyword,
+            search_volume=data["volume"],
+            difficulty=data["difficulty"],
+            intent=data["intent"],
+            cpc=data["cpc"]
+        )
+        
+        return self.results["primary"]
+    
+    def generate_lsi_keywords(self) -> List[KeywordData]:
+        """Generates LSI (Latent Semantic Indexing) keywords"""
+        
+        lsi_patterns = {
+            "눈 성형": [
+                ("쌍꺼풀 수술", 8000, "transactional"),
+                ("눈매교정", 5500, "informational"),
+                ("앞트임", 4000, "informational"),
+                ("뒤트임", 3500, "informational"),
+                ("눈 성형 비용", 2000, "comparative"),
+                ("눈 성형 부작용", 1500, "informational"),
+                ("눈 성형 회복기간", 1800, "informational"),
+                ("눈 성형 전후", 3000, "comparative"),
+                ("남자 눈 성형", 2500, "informational"),
+                ("눈 성형 잘하는곳", 2200, "comparative")
+            ],
+            "이마 성형": [
+                ("이마거상술", 3000, "informational"),
+                ("이마축소술", 2500, "informational"),
+                ("헤어라인교정", 4000, "transactional"),
+                ("이마 성형 비용", 1200, "comparative"),
+                ("이마 보톡스", 6000, "transactional"),
+                ("M자 탈모 수술", 5000, "informational"),
+                ("이마 필러", 4500, "transactional"),
+                ("이마 성형 부작용", 800, "informational"),
+                ("이마 리프팅", 3500, "comparative"),
+                ("이마 주름 제거", 2800, "transactional")
+            ],
+            "동안 성형": [
+                ("안면 리프팅", 7000, "transactional"),
+                ("실리프팅", 9000, "transactional"),
+                ("보톡스 시술", 15000, "transactional"),
+                ("필러 시술", 12000, "transactional"),
+                ("동안 성형 비용", 2500, "comparative"),
+                ("울쎄라", 8000, "comparative"),
+                ("써마지", 6500, "comparative"),
+                ("동안 시술 종류", 1800, "informational"),
+                ("주름 제거 시술", 4000, "transactional"),
+                ("동안 성형 추천", 2200, "comparative")
+            ]
+        }
+        
+        lsi_list = lsi_patterns.get(self.primary_keyword, [
+            (f"{self.primary_keyword} 비용", 1000, "comparative"),
+            (f"{self.primary_keyword} 부작용", 800, "informational"),
+            (f"{self.primary_keyword} 후기", 1200, "comparative"),
+        ])
+        
+        for keyword, volume, intent in lsi_list:
+            self.results["lsi"].append(KeywordData(
+                keyword=keyword,
+                search_volume=volume,
+                difficulty=45 + (volume/1000),  # Simple difficulty calculation
+                intent=intent
+            ))
+        
+        return self.results["lsi"]
+    
+    def generate_long_tail_keywords(self) -> List[str]:
+        """Generates long-tail keyword variations"""
+        
+        location_modifiers = ["강남", "신사", "청담", "압구정", "서울"]
+        action_modifiers = ["잘하는곳", "추천", "유명한", "전문", "비용"]
+        
+        long_tails = []
+        for location in location_modifiers:
+            long_tails.append(f"{location} {self.primary_keyword}")
+            for action in action_modifiers[:2]:  # Limit combinations
+                long_tails.append(f"{location} {self.primary_keyword} {action}")
+        
+        self.results["long_tail"] = long_tails
+        return long_tails
+    
+    def generate_question_keywords(self) -> List[str]:
+        """Generates question-based keywords for featured snippets"""
+        
+        question_templates = [
+            f"{self.primary_keyword} 비용은 얼마인가요?",
+            f"{self.primary_keyword} 회복기간은 얼마나 걸리나요?",
+            f"{self.primary_keyword} 부작용이 있나요?",
+            f"{self.primary_keyword} 통증이 심한가요?",
+            f"{self.primary_keyword} 효과는 얼마나 지속되나요?",
+            f"{self.primary_keyword} 나이 제한이 있나요?",
+            f"{self.primary_keyword} 후 주의사항은 무엇인가요?"
+        ]
+        
+        self.results["questions"] = question_templates
+        return question_templates
+    
+    def calculate_intent_distribution(self) -> Dict[str, float]:
+        """Calculates user intent distribution across keywords"""
+        
+        intent_counts = {
+            "informational": 0,
+            "comparative": 0,
+            "transactional": 0,
+            "navigational": 0
+        }
+        
+        # Count primary keyword intent
+        if self.results["primary"]:
+            intent_counts[self.results["primary"].intent] += self.results["primary"].search_volume
+        
+        # Count LSI keyword intents
+        for kw in self.results["lsi"]:
+            intent_counts[kw.intent] += kw.search_volume
+        
+        # Calculate percentages
+        total_volume = sum(intent_counts.values())
+        if total_volume > 0:
+            self.results["intent_distribution"] = {
+                intent: round((count/total_volume) * 100, 1)
+                for intent, count in intent_counts.items()
+                if count > 0
+            }
+        
+        return self.results["intent_distribution"]
+    
+    def generate_recommendations(self) -> List[str]:
+        """Generates SEO recommendations based on analysis"""
+        
+        recommendations = []
+        
+        # Based on search volume
+        if self.results["primary"] and self.results["primary"].search_volume > 10000:
+            recommendations.append("High search volume detected - prioritize this page for development")
+        
+        # Based on intent distribution
+        intent_dist = self.results["intent_distribution"]
+        if intent_dist.get("informational", 0) > 50:
+            recommendations.append("Focus on educational content and comprehensive guides")
+        if intent_dist.get("comparative", 0) > 30:
+            recommendations.append("Include comparison tables and competitive differentiators")
+        if intent_dist.get("transactional", 0) > 20:
+            recommendations.append("Optimize conversion elements and CTAs above the fold")
+        
+        # Based on competition
+        if self.results["primary"] and self.results["primary"].difficulty > 60:
+            recommendations.append("High competition - invest in quality content and backlinks")
+            recommendations.append("Target long-tail keywords for quicker wins")
+        
+        # Question keywords
+        if len(self.results["questions"]) > 5:
+            recommendations.append("Implement FAQ schema markup for featured snippets")
+        
+        self.results["recommendations"] = recommendations
+        return recommendations
+    
+    def export_analysis(self, filename: str = None) -> str:
+        """Exports the analysis results to JSON"""
+        
+        if not filename:
+            filename = f"keyword_analysis_{self.primary_keyword.replace(' ', '_')}_{datetime.now().strftime('%Y%m%d')}.json"
+        
+        export_data = {
+            "analysis_date": datetime.now().isoformat(),
+            "primary_keyword": self.primary_keyword,
+            "primary_data": {
+                "keyword": self.results["primary"].keyword,
+                "search_volume": self.results["primary"].search_volume,
+                "difficulty": self.results["primary"].difficulty,
+                "intent": self.results["primary"].intent
+            } if self.results["primary"] else None,
+            "lsi_keywords": [
+                {
+                    "keyword": kw.keyword,
+                    "volume": kw.search_volume,
+                    "intent": kw.intent
+                } for kw in self.results["lsi"]
+            ],
+            "long_tail_keywords": self.results["long_tail"],
+            "question_keywords": self.results["questions"],
+            "intent_distribution": self.results["intent_distribution"],
+            "recommendations": self.results["recommendations"]
+        }
+        
+        with open(filename, 'w', encoding='utf-8') as f:
+            json.dump(export_data, f, ensure_ascii=False, indent=2)
+        
+        return filename
+    
+    def generate_report(self) -> str:
+        """Generates a formatted text report"""
+        
+        report = f"""
+# Keyword Analysis Report
+Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}
+
+## Primary Keyword: {self.primary_keyword}
+- Search Volume: {self.results['primary'].search_volume:,}
+- Difficulty: {self.results['primary'].difficulty}/100
+- Primary Intent: {self.results['primary'].intent.capitalize()}
+
+## LSI Keywords (Top 10)
+"""
+        for i, kw in enumerate(self.results['lsi'][:10], 1):
+            report += f"{i}. {kw.keyword} - Volume: {kw.search_volume:,} ({kw.intent})\n"
+        
+        report += f"\n## User Intent Distribution\n"
+        for intent, percentage in self.results['intent_distribution'].items():
+            report += f"- {intent.capitalize()}: {percentage}%\n"
+        
+        report += f"\n## Long-tail Opportunities\n"
+        for keyword in self.results['long_tail'][:5]:
+            report += f"- {keyword}\n"
+        
+        report += f"\n## Question Keywords (FAQ Optimization)\n"
+        for question in self.results['questions'][:5]:
+            report += f"- {question}\n"
+        
+        report += f"\n## Strategic Recommendations\n"
+        for i, rec in enumerate(self.results['recommendations'], 1):
+            report += f"{i}. {rec}\n"
+        
+        return report
+
+
+def main():
+    """Main execution function"""
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description='Analyze keywords for SEO gateway page strategy',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog='''
+Examples:
+  python keyword_analyzer.py --topic "눈 성형"
+  python keyword_analyzer.py --topic "이마 성형" --market "강남"
+  python keyword_analyzer.py --topic "동안 성형" --output strategy.json
+        '''
+    )
+    parser.add_argument('--topic', '-t', required=True,
+                        help='Primary keyword to analyze (e.g., "눈 성형")')
+    parser.add_argument('--market', '-m', default=None,
+                        help='Target market/location (e.g., "강남")')
+    parser.add_argument('--output', '-o', default=None,
+                        help='Output JSON file path')
+    parser.add_argument('--competitors', '-c', default=None,
+                        help='Comma-separated competitor URLs for analysis')
+
+    args = parser.parse_args()
+
+    keyword = args.topic
+    if args.market:
+        keyword = f"{args.market} {args.topic}"
+
+    print(f"Analyzing keyword: {keyword}")
+    print("-" * 50)
+
+    analyzer = KeywordAnalyzer(keyword)
+
+    # Run analysis
+    analyzer.analyze_primary_keyword()
+    analyzer.generate_lsi_keywords()
+    analyzer.generate_long_tail_keywords()
+    analyzer.generate_question_keywords()
+    analyzer.calculate_intent_distribution()
+    analyzer.generate_recommendations()
+
+    # Generate and print report
+    report = analyzer.generate_report()
+    print(report)
+
+    # Export to JSON
+    filename = analyzer.export_analysis(args.output)
+    print(f"\nAnalysis exported to: {filename}")
+
+
+if __name__ == "__main__":
+    main()

custom-skills/17-seo-gateway-architect/code/scripts/requirements.txt

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+# Python dependencies for SEO Gateway Page Strategist scripts
+# Install with: pip install -r requirements.txt
+
+# Core dependencies
+requests>=2.28.0
+beautifulsoup4>=4.11.0
+pandas>=1.5.0
+numpy>=1.23.0
+
+# For API integrations (optional)
+google-api-python-client>=2.70.0
+pytrends>=4.9.0
+
+# For data visualization (optional)
+matplotlib>=3.6.0
+seaborn>=0.12.0
+
+# For export formats
+openpyxl>=3.0.0
+jinja2>=3.1.0
+
+# Development tools
+pytest>=7.2.0
+black>=22.0.0
+pylint>=2.15.0

custom-skills/17-seo-gateway-architect/code/templates/content-architecture-template.md

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+# Content Architecture Template
+
+## Page Hierarchy Structure
+
+```
+[Page URL: /service-name]
+│
+├── H1: [Primary Keyword-Optimized Headline]
+│   Example: "강남 눈 성형 전문의가 만드는 자연스러운 눈매"
+│   Word Count Target: 15-25 characters
+│   Keyword Placement: Primary keyword at beginning
+│
+├── Hero Section [Above Fold]
+│   ├── Value Proposition (30-50 words)
+│   │   └── Keywords: Primary + 1 LSI
+│   ├── Trust Signals (3-5 items)
+│   │   ├── Certification badges
+│   │   ├── Years of experience
+│   │   └── Success cases number
+│   └── Primary CTA
+│       └── Text: "무료 상담 신청하기"
+│
+├── H2: [Service Name] 이란? [Problem/Solution Framework]
+│   Word Count: 200-300 words
+│   Keywords: Primary (1x), LSI (2-3x)
+│   ├── H3: 이런 고민이 있으신가요? [Pain Points]
+│   │   ├── Pain point 1 (include LSI keyword)
+│   │   ├── Pain point 2 (include LSI keyword)
+│   │   └── Pain point 3 (include LSI keyword)
+│   └── H3: [Clinic Name]의 솔루션 [Benefits]
+│       ├── Benefit 1 (address pain point 1)
+│       ├── Benefit 2 (address pain point 2)
+│       └── Benefit 3 (address pain point 3)
+│
+├── H2: [Service Name] 종류 및 방법 [Service Categories]
+│   Word Count: 400-500 words total
+│   Keywords: Category-specific LSI keywords
+│   ├── H3: [Sub-service 1] - [LSI Keyword Variation]
+│   │   ├── Description (80-100 words)
+│   │   ├── Best for (target audience)
+│   │   ├── Duration & Recovery
+│   │   └── CTA: "자세히 보기"
+│   ├── H3: [Sub-service 2] - [LSI Keyword Variation]
+│   │   └── [Same structure as above]
+│   └── H3: [Sub-service 3] - [LSI Keyword Variation]
+│       └── [Same structure as above]
+│
+├── H2: [Clinic Name] [Service Name]만의 차별점 [Trust & Authority]
+│   Word Count: 300-400 words
+│   Keywords: Brand + Primary keyword combinations
+│   ├── H3: 전문 의료진 [Doctor Credentials]
+│   │   ├── Doctor profile summary
+│   │   ├── Specializations
+│   │   └── Certifications
+│   ├── H3: 검증된 시술 결과 [Success Metrics]
+│   │   ├── Number statistics
+│   │   ├── Success rate
+│   │   └── Patient satisfaction
+│   └── H3: 첨단 장비 및 시설 [Facilities]
+│       ├── Equipment descriptions
+│       └── Safety protocols
+│
+├── H2: [Service Name] 자주 묻는 질문 [FAQ Section]
+│   Word Count: 500-700 words
+│   Keywords: Long-tail question keywords
+│   ├── Q1: [Long-tail keyword as question]?
+│   │   └── A: [40-60 word answer, keyword in first sentence]
+│   ├── Q2: [Price-related question]?
+│   │   └── A: [Include "비용" LSI keyword]
+│   ├── Q3: [Recovery-related question]?
+│   │   └── A: [Include "회복기간" LSI keyword]
+│   ├── Q4: [Side-effect question]?
+│   │   └── A: [Include "부작용" LSI keyword]
+│   ├── Q5: [Process question]?
+│   │   └── A: [Include process-related LSI]
+│   ├── Q6: [Candidacy question]?
+│   │   └── A: [Include target audience keywords]
+│   └── Q7: [Results duration question]?
+│       └── A: [Include maintenance keywords]
+│
+├── H2: [Service Name] 시술 과정 [Process Guide]
+│   Word Count: 300-400 words
+│   Keywords: "과정", "단계", procedural LSI
+│   ├── H3: 상담 및 검사 [Consultation]
+│   ├── H3: 시술 당일 [Procedure Day]
+│   ├── H3: 회복 과정 [Recovery]
+│   └── H3: 사후 관리 [Aftercare]
+│
+├── H2: 실제 고객 후기 [Social Proof]
+│   Word Count: 200-300 words
+│   Keywords: "후기", "리뷰", satisfaction keywords
+│   ├── Review snippet 1
+│   ├── Review snippet 2
+│   ├── Review snippet 3
+│   └── Before/After gallery teaser
+│
+└── H2: 상담 예약 안내 [Conversion Section]
+    Word Count: 150-200 words
+    Keywords: CTA-related, location keywords
+    ├── H3: 상담 예약 방법
+    ├── H3: 오시는 길
+    └── H3: 문의 정보
+```
+
+## Keyword Density Map
+
+| Section | Primary Keyword | LSI Keywords | Total Keywords |
+|---------|----------------|--------------|----------------|
+| Hero | 1 | 1-2 | 2-3 |
+| Problem/Solution | 1 | 2-3 | 3-4 |
+| Service Categories | 1-2 | 4-6 | 5-8 |
+| Trust & Authority | 1 | 2-3 | 3-4 |
+| FAQ | 2-3 | 5-7 | 7-10 |
+| Process | 1 | 2-3 | 3-4 |
+| Social Proof | 0-1 | 1-2 | 1-3 |
+| Conversion | 1 | 1-2 | 2-3 |
+| **Total** | **8-11** | **18-29** | **26-40** |
+
+## Internal Linking Strategy
+
+| From Section | To Page | Anchor Text | Purpose |
+|-------------|---------|-------------|---------|
+| Service Categories | Sub-service page | [Sub-service name] | Deep dive |
+| FAQ | Price page | "비용 안내 페이지" | Conversion |
+| Trust section | Doctor profile | "[Doctor name] 원장" | Authority |
+| Process section | Consultation form | "상담 예약하기" | Conversion |
+| Social proof | Gallery page | "더 많은 전후 사진" | Engagement |
+
+## Content Length Guidelines
+
+- **Total Page Length**: 2,000-2,500 words
+- **Above Fold Content**: 100-150 words
+- **Each H2 Section**: 200-500 words
+- **Each H3 Subsection**: 80-150 words
+- **Meta Description**: 150-160 characters
+- **Image Alt Text**: 10-15 words each
+
+## Schema Markup Requirements
+
+```json
+{
+  "@context": "https://schema.org",
+  "@type": "MedicalProcedure",
+  "name": "[Service Name]",
+  "description": "[Meta description]",
+  "procedureType": "Cosmetic",
+  "provider": {
+    "@type": "MedicalOrganization",
+    "name": "[Clinic Name]"
+  }
+}
+```
+
+## Mobile Content Adaptation
+
+- Reduce hero text by 30%
+- Show 3 FAQs initially (expand for more)
+- Simplify navigation to single-column
+- Increase CTA button size
+- Compress trust signals to carousel

custom-skills/17-seo-gateway-architect/code/templates/keyword-research-template.md

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+# Keyword Research Template
+
+## Primary Keyword Analysis
+
+| Metric | Value | Notes |
+|--------|-------|-------|
+| **Primary Keyword** | [KEYWORD] | Main target keyword |
+| **Monthly Search Volume** | [VOLUME] | Average monthly searches |
+| **Keyword Difficulty** | [0-100] | Competition score |
+| **Current Ranking** | #[POSITION] | Current SERP position |
+| **Search Trend** | ↑ ↓ → | Trending direction |
+
+## LSI Keywords Matrix
+
+| LSI Keyword | Search Volume | Intent Type | Priority |
+|------------|--------------|-------------|----------|
+| [keyword 1] | [volume] | Informational | High |
+| [keyword 2] | [volume] | Transactional | Medium |
+| [keyword 3] | [volume] | Comparative | High |
+| [keyword 4] | [volume] | Informational | Medium |
+| [keyword 5] | [volume] | Transactional | Low |
+| [keyword 6] | [volume] | Comparative | High |
+| [keyword 7] | [volume] | Informational | Medium |
+| [keyword 8] | [volume] | Navigational | Low |
+| [keyword 9] | [volume] | Transactional | High |
+| [keyword 10] | [volume] | Informational | Medium |
+
+## User Intent Distribution
+
+```
+Informational (Research Phase): ___%
+  - Common queries: "what is", "how to", "benefits of"
+  - Content needed: Educational guides, FAQs, process explanations
+
+Comparative (Evaluation Phase): ___%
+  - Common queries: "best", "vs", "reviews", "비교"
+  - Content needed: Comparison tables, reviews, case studies
+
+Transactional (Ready to Convert): ___%
+  - Common queries: "price", "book", "consultation", "예약"
+  - Content needed: CTAs, pricing, booking forms
+```
+
+## Long-tail Keyword Opportunities
+
+### Question-based Keywords
+- [질문 키워드 1]
+- [질문 키워드 2]
+- [질문 키워드 3]
+
+### Location-based Keywords
+- [지역] + [primary keyword]
+- [지역] + [primary keyword] + 잘하는곳
+- [지역] + [primary keyword] + 추천
+
+### Modifier-based Keywords
+- [primary keyword] + 비용
+- [primary keyword] + 부작용
+- [primary keyword] + 회복기간
+- [primary keyword] + 전후
+
+## Competitor Keyword Analysis
+
+| Competitor | Target Keywords | Ranking Keywords | Gap Opportunities |
+|------------|----------------|------------------|-------------------|
+| Competitor 1 | [keywords] | [keywords] | [missing keywords] |
+| Competitor 2 | [keywords] | [keywords] | [missing keywords] |
+| Competitor 3 | [keywords] | [keywords] | [missing keywords] |
+
+## Seasonal Trends
+
+| Month | Search Volume | Events/Factors |
+|-------|--------------|----------------|
+| January | [volume] | New year resolutions |
+| February | [volume] | [factor] |
+| March | [volume] | [factor] |
+| ... | ... | ... |
+
+## Platform-Specific Keywords
+
+### Naver-Optimized
+- [네이버 specific keyword 1]
+- [네이버 specific keyword 2]
+
+### Google-Optimized
+- [Google specific keyword 1]
+- [Google specific keyword 2]
+
+## Action Items
+
+- [ ] Target primary keyword in H1 and title tag
+- [ ] Include 3-5 LSI keywords naturally in content
+- [ ] Create content matching user intent distribution
+- [ ] Optimize for question-based featured snippets
+- [ ] Add location modifiers for local SEO

custom-skills/17-seo-gateway-architect/code/templates/seo-checklist-template.md

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+# SEO Technical Checklist Template
+
+## Meta Tags Optimization
+
+### Title Tag
+- [ ] Length: 50-60 characters
+- [ ] Primary keyword at beginning
+- [ ] Brand name at end
+- [ ] Unique for each page
+- [ ] Formula: `[Primary Keyword] - [Value Proposition] | [Brand]`
+
+**Template**: `{primary_keyword} 전문 - {unique_value} | {clinic_name}`
+**Example**: `눈 성형 전문 - 자연스러운 라인 | 제이미클리닉`
+
+### Meta Description
+- [ ] Length: 150-160 characters
+- [ ] Include primary keyword
+- [ ] Include 1-2 LSI keywords
+- [ ] Clear CTA
+- [ ] Unique for each page
+
+**Template**: `{location} {primary_keyword} 전문의가 {benefit}. {credential}. 무료상담 ☎ {phone}`
+**Example**: `강남 눈 성형 전문의가 자연스러운 눈매를 디자인합니다. 15년 경력, 10,000건 시술. 무료상담 ☎ 02-1234-5678`
+
+### Open Graph Tags
+```html
+<meta property="og:title" content="{page_title}">
+<meta property="og:description" content="{meta_description}">
+<meta property="og:image" content="{featured_image_url}">
+<meta property="og:url" content="{page_url}">
+<meta property="og:type" content="website">
+<meta property="og:locale" content="ko_KR">
+```
+
+## Header Tags Structure
+
+- [ ] Only one H1 per page
+- [ ] H1 contains primary keyword
+- [ ] H2 tags for main sections (5-7)
+- [ ] H3 tags for subsections
+- [ ] Logical hierarchy maintained
+- [ ] Keywords distributed naturally
+
+## Content Optimization
+
+### Keyword Density
+- [ ] Primary keyword: 2-3% (20-30 times per 1000 words)
+- [ ] LSI keywords: 1-2% each
+- [ ] Natural placement (no stuffing)
+- [ ] Synonyms and variations used
+
+### Content Structure
+- [ ] First 100 words include primary keyword
+- [ ] Short paragraphs (3-4 sentences)
+- [ ] Bullet points and lists
+- [ ] Bold important keywords (sparingly)
+- [ ] Internal links: 5-10
+- [ ] External links: 2-3 (authoritative)
+
+## Schema Markup
+
+### Medical Procedure Schema
+```json
+{
+  "@context": "https://schema.org",
+  "@type": "MedicalProcedure",
+  "name": "{procedure_name}",
+  "procedureType": "Cosmetic",
+  "bodyLocation": "{body_part}",
+  "outcome": "{expected_outcome}",
+  "preparation": "{preparation_required}",
+  "followup": "{followup_care}",
+  "provider": {
+    "@type": "MedicalOrganization",
+    "name": "{clinic_name}",
+    "address": {
+      "@type": "PostalAddress",
+      "streetAddress": "{street}",
+      "addressLocality": "{city}",
+      "addressCountry": "KR"
+    }
+  }
+}
+```
+
+### FAQ Schema
+```json
+{
+  "@context": "https://schema.org",
+  "@type": "FAQPage",
+  "mainEntity": [{
+    "@type": "Question",
+    "name": "{question}",
+    "acceptedAnswer": {
+      "@type": "Answer",
+      "text": "{answer}"
+    }
+  }]
+}
+```
+
+## Image Optimization
+
+- [ ] Descriptive file names: `eye-surgery-before-after-case1.jpg`
+- [ ] Alt text with keywords: `눈 성형 전후 사진 - 30대 여성 사례`
+- [ ] Compressed file size (< 200KB)
+- [ ] WebP format with fallback
+- [ ] Lazy loading implemented
+- [ ] Image sitemap created
+
+## Performance Optimization
+
+### Page Speed
+- [ ] Load time < 3 seconds
+- [ ] First Contentful Paint < 1.8s
+- [ ] Time to Interactive < 3.8s
+- [ ] Total page size < 3MB
+- [ ] Requests minimized (< 50)
+
+### Core Web Vitals
+- [ ] LCP (Largest Contentful Paint) < 2.5s
+- [ ] FID (First Input Delay) < 100ms
+- [ ] CLS (Cumulative Layout Shift) < 0.1
+
+## Mobile Optimization
+
+- [ ] Mobile-responsive design
+- [ ] Viewport meta tag set
+- [ ] Touch-friendly buttons (44x44px minimum)
+- [ ] Readable font size (16px minimum)
+- [ ] No horizontal scrolling
+- [ ] Mobile page speed < 3s
+
+## URL Structure
+
+- [ ] SEO-friendly URL: `/eye-surgery` or `/눈-성형`
+- [ ] No special characters
+- [ ] Lowercase only
+- [ ] Hyphens for word separation
+- [ ] Under 60 characters
+- [ ] Include primary keyword
+
+## Internal Linking
+
+| From Page | To Page | Anchor Text | Purpose |
+|-----------|---------|-------------|---------|
+| Gateway | Service Detail | {service_name} | Deep content |
+| Gateway | Doctor Profile | {doctor_name} 원장 | Authority |
+| Gateway | Pricing | 비용 안내 | Conversion |
+| Gateway | Gallery | 시술 전후 사진 | Engagement |
+| Gateway | Contact | 상담 예약 | Conversion |
+
+## Naver-Specific Optimization
+
+### Naver Webmaster Tools
+- [ ] Site verification complete
+- [ ] XML sitemap submitted
+- [ ] Robots.txt configured
+- [ ] Syndication feed active
+- [ ] Site optimization report reviewed
+
+### Naver SEO Elements
+- [ ] Title under 30 Korean characters
+- [ ] C-Rank tags implemented
+- [ ] Image-to-text ratio optimized (40:60)
+- [ ] Outbound links minimized
+- [ ] Brand search optimization
+
+## Tracking & Analytics
+
+- [ ] Google Analytics 4 installed
+- [ ] Google Search Console verified
+- [ ] Naver Analytics installed
+- [ ] Conversion tracking configured
+- [ ] Event tracking for CTAs
+- [ ] Heatmap tool installed
+
+## Security & Technical
+
+- [ ] SSL certificate active (HTTPS)
+- [ ] WWW/non-WWW redirect configured
+- [ ] 404 error page customized
+- [ ] XML sitemap generated
+- [ ] Robots.txt optimized
+- [ ] Canonical URLs set
+- [ ] Hreflang tags (if multi-language)
+
+## Quality Checks
+
+### Content Quality
+- [ ] No spelling/grammar errors
+- [ ] Medical information accurate
+- [ ] Legal compliance verified
+- [ ] Contact information correct
+- [ ] CTAs working properly
+
+### Cross-browser Testing
+- [ ] Chrome (Desktop/Mobile)
+- [ ] Safari (Desktop/Mobile)
+- [ ] Firefox
+- [ ] Samsung Internet
+- [ ] Naver Whale
+
+## Monthly Monitoring Tasks
+
+- [ ] Keyword ranking check
+- [ ] Organic traffic analysis
+- [ ] Bounce rate monitoring
+- [ ] Conversion rate tracking
+- [ ] Competitor analysis
+- [ ] Content freshness update
+- [ ] Broken link check
+- [ ] Page speed test
+
+## Priority Levels
+
+1. **Critical (Day 1)**
+   - Title and meta tags
+   - H1 optimization
+   - Mobile responsiveness
+   - Page speed < 4s
+
+2. **High (Week 1)**
+   - Schema markup
+   - Internal linking
+   - Image optimization
+   - Content optimization
+
+3. **Medium (Week 2-3)**
+   - Naver optimization
+   - FAQ implementation
+   - Social proof elements
+   - Analytics setup
+
+4. **Low (Month 2)**
+   - A/B testing
+   - Advanced schema
+   - Link building
+   - Content expansion