Andrew Yim
eea49f9f8c
Major refactoring of ourdigital-custom-skills with new numbering system: ## Structure Changes - Each skill now has code/ (Claude Code) and desktop/ (Claude Desktop) versions - New progressive numbering: 01-09 General, 10-19 SEO, 20-29 GTM, 30-39 OurDigital, 40-49 Jamie ## Skill Reorganization - 01-notion-organizer (from 02) - 10-18: SEO tools split into focused skills (technical, on-page, local, schema, vitals, gsc, gateway) - 20-21: GTM audit and manager - 30-32: OurDigital designer, research, presentation - 40-41: Jamie brand editor and audit ## New Files - .claude/commands/: Slash command definitions for all skills - CLAUDE.md: Updated with new skill structure documentation - REFACTORING_PLAN.md: Migration documentation - COMPATIBILITY_REPORT.md, SKILLS_COMPARISON.md: Analysis docs ## Removed - Old skill directories (02-05, 10-14, 20-21 old numbering) - Consolidated into new structure with _archive/ for reference 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
335 lines
13 KiB
Python
335 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Keyword Analyzer for SEO Gateway Pages
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Analyzes keywords and generates SEO strategy recommendations
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"""
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import json
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from typing import Dict, List, Tuple
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from dataclasses import dataclass
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from datetime import datetime
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@dataclass
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class KeywordData:
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"""Data structure for keyword information"""
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keyword: str
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search_volume: int
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difficulty: float
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intent: str
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cpc: float = 0.0
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trend: str = "stable"
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class KeywordAnalyzer:
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"""Analyzes keywords for SEO gateway pages"""
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def __init__(self, primary_keyword: str):
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self.primary_keyword = primary_keyword
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self.results = {
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"primary": None,
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"lsi": [],
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"long_tail": [],
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"questions": [],
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"intent_distribution": {},
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"recommendations": []
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}
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def analyze_primary_keyword(self) -> KeywordData:
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"""
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Analyzes the primary keyword
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In production, this would call actual keyword research APIs
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"""
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# Simulated data - replace with actual API calls
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keyword_data = {
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"눈 성형": {"volume": 12000, "difficulty": 65, "intent": "informational", "cpc": 2500},
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"이마 성형": {"volume": 5500, "difficulty": 55, "intent": "informational", "cpc": 3000},
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"동안 성형": {"volume": 8000, "difficulty": 70, "intent": "comparative", "cpc": 2800},
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}
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data = keyword_data.get(self.primary_keyword, {
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"volume": 1000,
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"difficulty": 50,
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"intent": "informational",
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"cpc": 1000
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})
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self.results["primary"] = KeywordData(
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keyword=self.primary_keyword,
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search_volume=data["volume"],
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difficulty=data["difficulty"],
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intent=data["intent"],
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cpc=data["cpc"]
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)
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return self.results["primary"]
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def generate_lsi_keywords(self) -> List[KeywordData]:
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"""Generates LSI (Latent Semantic Indexing) keywords"""
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lsi_patterns = {
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"눈 성형": [
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("쌍꺼풀 수술", 8000, "transactional"),
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("눈매교정", 5500, "informational"),
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("앞트임", 4000, "informational"),
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("뒤트임", 3500, "informational"),
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("눈 성형 비용", 2000, "comparative"),
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("눈 성형 부작용", 1500, "informational"),
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("눈 성형 회복기간", 1800, "informational"),
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("눈 성형 전후", 3000, "comparative"),
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("남자 눈 성형", 2500, "informational"),
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("눈 성형 잘하는곳", 2200, "comparative")
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],
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"이마 성형": [
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("이마거상술", 3000, "informational"),
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("이마축소술", 2500, "informational"),
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("헤어라인교정", 4000, "transactional"),
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("이마 성형 비용", 1200, "comparative"),
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("이마 보톡스", 6000, "transactional"),
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("M자 탈모 수술", 5000, "informational"),
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("이마 필러", 4500, "transactional"),
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("이마 성형 부작용", 800, "informational"),
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("이마 리프팅", 3500, "comparative"),
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("이마 주름 제거", 2800, "transactional")
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],
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"동안 성형": [
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("안면 리프팅", 7000, "transactional"),
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("실리프팅", 9000, "transactional"),
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("보톡스 시술", 15000, "transactional"),
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("필러 시술", 12000, "transactional"),
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("동안 성형 비용", 2500, "comparative"),
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("울쎄라", 8000, "comparative"),
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("써마지", 6500, "comparative"),
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("동안 시술 종류", 1800, "informational"),
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("주름 제거 시술", 4000, "transactional"),
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("동안 성형 추천", 2200, "comparative")
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]
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}
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lsi_list = lsi_patterns.get(self.primary_keyword, [
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(f"{self.primary_keyword} 비용", 1000, "comparative"),
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(f"{self.primary_keyword} 부작용", 800, "informational"),
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(f"{self.primary_keyword} 후기", 1200, "comparative"),
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])
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for keyword, volume, intent in lsi_list:
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self.results["lsi"].append(KeywordData(
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keyword=keyword,
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search_volume=volume,
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difficulty=45 + (volume/1000), # Simple difficulty calculation
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intent=intent
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))
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return self.results["lsi"]
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def generate_long_tail_keywords(self) -> List[str]:
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"""Generates long-tail keyword variations"""
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location_modifiers = ["강남", "신사", "청담", "압구정", "서울"]
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action_modifiers = ["잘하는곳", "추천", "유명한", "전문", "비용"]
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long_tails = []
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for location in location_modifiers:
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long_tails.append(f"{location} {self.primary_keyword}")
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for action in action_modifiers[:2]: # Limit combinations
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long_tails.append(f"{location} {self.primary_keyword} {action}")
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self.results["long_tail"] = long_tails
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return long_tails
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def generate_question_keywords(self) -> List[str]:
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"""Generates question-based keywords for featured snippets"""
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question_templates = [
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f"{self.primary_keyword} 비용은 얼마인가요?",
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f"{self.primary_keyword} 회복기간은 얼마나 걸리나요?",
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f"{self.primary_keyword} 부작용이 있나요?",
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f"{self.primary_keyword} 통증이 심한가요?",
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f"{self.primary_keyword} 효과는 얼마나 지속되나요?",
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f"{self.primary_keyword} 나이 제한이 있나요?",
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f"{self.primary_keyword} 후 주의사항은 무엇인가요?"
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]
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self.results["questions"] = question_templates
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return question_templates
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def calculate_intent_distribution(self) -> Dict[str, float]:
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"""Calculates user intent distribution across keywords"""
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intent_counts = {
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"informational": 0,
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"comparative": 0,
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"transactional": 0,
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"navigational": 0
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}
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# Count primary keyword intent
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if self.results["primary"]:
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intent_counts[self.results["primary"].intent] += self.results["primary"].search_volume
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# Count LSI keyword intents
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for kw in self.results["lsi"]:
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intent_counts[kw.intent] += kw.search_volume
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# Calculate percentages
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total_volume = sum(intent_counts.values())
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if total_volume > 0:
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self.results["intent_distribution"] = {
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intent: round((count/total_volume) * 100, 1)
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for intent, count in intent_counts.items()
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if count > 0
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}
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return self.results["intent_distribution"]
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def generate_recommendations(self) -> List[str]:
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"""Generates SEO recommendations based on analysis"""
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recommendations = []
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# Based on search volume
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if self.results["primary"] and self.results["primary"].search_volume > 10000:
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recommendations.append("High search volume detected - prioritize this page for development")
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# Based on intent distribution
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intent_dist = self.results["intent_distribution"]
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if intent_dist.get("informational", 0) > 50:
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recommendations.append("Focus on educational content and comprehensive guides")
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if intent_dist.get("comparative", 0) > 30:
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recommendations.append("Include comparison tables and competitive differentiators")
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if intent_dist.get("transactional", 0) > 20:
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recommendations.append("Optimize conversion elements and CTAs above the fold")
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# Based on competition
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if self.results["primary"] and self.results["primary"].difficulty > 60:
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recommendations.append("High competition - invest in quality content and backlinks")
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recommendations.append("Target long-tail keywords for quicker wins")
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# Question keywords
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if len(self.results["questions"]) > 5:
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recommendations.append("Implement FAQ schema markup for featured snippets")
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self.results["recommendations"] = recommendations
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return recommendations
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def export_analysis(self, filename: str = None) -> str:
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"""Exports the analysis results to JSON"""
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if not filename:
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filename = f"keyword_analysis_{self.primary_keyword.replace(' ', '_')}_{datetime.now().strftime('%Y%m%d')}.json"
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export_data = {
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"analysis_date": datetime.now().isoformat(),
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"primary_keyword": self.primary_keyword,
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"primary_data": {
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"keyword": self.results["primary"].keyword,
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"search_volume": self.results["primary"].search_volume,
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"difficulty": self.results["primary"].difficulty,
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"intent": self.results["primary"].intent
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} if self.results["primary"] else None,
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"lsi_keywords": [
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{
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"keyword": kw.keyword,
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"volume": kw.search_volume,
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"intent": kw.intent
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} for kw in self.results["lsi"]
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],
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"long_tail_keywords": self.results["long_tail"],
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"question_keywords": self.results["questions"],
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"intent_distribution": self.results["intent_distribution"],
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"recommendations": self.results["recommendations"]
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}
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(export_data, f, ensure_ascii=False, indent=2)
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return filename
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def generate_report(self) -> str:
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"""Generates a formatted text report"""
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report = f"""
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# Keyword Analysis Report
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Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}
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## Primary Keyword: {self.primary_keyword}
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- Search Volume: {self.results['primary'].search_volume:,}
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- Difficulty: {self.results['primary'].difficulty}/100
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- Primary Intent: {self.results['primary'].intent.capitalize()}
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## LSI Keywords (Top 10)
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"""
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for i, kw in enumerate(self.results['lsi'][:10], 1):
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report += f"{i}. {kw.keyword} - Volume: {kw.search_volume:,} ({kw.intent})\n"
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report += f"\n## User Intent Distribution\n"
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for intent, percentage in self.results['intent_distribution'].items():
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report += f"- {intent.capitalize()}: {percentage}%\n"
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report += f"\n## Long-tail Opportunities\n"
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for keyword in self.results['long_tail'][:5]:
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report += f"- {keyword}\n"
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report += f"\n## Question Keywords (FAQ Optimization)\n"
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for question in self.results['questions'][:5]:
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report += f"- {question}\n"
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report += f"\n## Strategic Recommendations\n"
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for i, rec in enumerate(self.results['recommendations'], 1):
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report += f"{i}. {rec}\n"
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return report
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def main():
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"""Main execution function"""
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import argparse
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parser = argparse.ArgumentParser(
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description='Analyze keywords for SEO gateway page strategy',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog='''
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Examples:
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python keyword_analyzer.py --topic "눈 성형"
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python keyword_analyzer.py --topic "이마 성형" --market "강남"
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python keyword_analyzer.py --topic "동안 성형" --output strategy.json
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'''
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)
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parser.add_argument('--topic', '-t', required=True,
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help='Primary keyword to analyze (e.g., "눈 성형")')
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parser.add_argument('--market', '-m', default=None,
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help='Target market/location (e.g., "강남")')
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parser.add_argument('--output', '-o', default=None,
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help='Output JSON file path')
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parser.add_argument('--competitors', '-c', default=None,
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help='Comma-separated competitor URLs for analysis')
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args = parser.parse_args()
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keyword = args.topic
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if args.market:
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keyword = f"{args.market} {args.topic}"
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print(f"Analyzing keyword: {keyword}")
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print("-" * 50)
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analyzer = KeywordAnalyzer(keyword)
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# Run analysis
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analyzer.analyze_primary_keyword()
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analyzer.generate_lsi_keywords()
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analyzer.generate_long_tail_keywords()
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analyzer.generate_question_keywords()
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analyzer.calculate_intent_distribution()
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analyzer.generate_recommendations()
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# Generate and print report
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report = analyzer.generate_report()
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print(report)
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# Export to JSON
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filename = analyzer.export_analysis(args.output)
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print(f"\nAnalysis exported to: {filename}")
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if __name__ == "__main__":
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main()
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