Add SEO skills 19-28, 31-32 with full Python implementations
12 new skills: Keyword Strategy, SERP Analysis, Position Tracking, Link Building, Content Strategy, E-Commerce SEO, KPI Framework, International SEO, AI Visibility, Knowledge Graph, Competitor Intel, and Crawl Budget. ~20K lines of Python across 25 domain scripts. Updated skill 11 pipeline table and repo CLAUDE.md. Enhanced skill 18 local SEO workflow from jamie.clinic audit. Note: Skill 26 hreflang_validator.py pending (content filter block). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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custom-skills/19-seo-keyword-strategy/desktop/SKILL.md
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name: seo-keyword-strategy
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description: |
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Keyword strategy and research for SEO campaigns. Triggers: keyword research, keyword analysis, keyword gap, search volume, keyword clustering, intent classification.
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---
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# SEO Keyword Strategy & Research
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## Purpose
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Expand seed keywords, classify search intent, cluster topics, and identify competitor keyword gaps for comprehensive keyword strategy development.
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## Core Capabilities
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1. **Keyword Expansion** - Matching terms, related terms, search suggestions
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2. **Korean Market** - Suffix expansion, Naver autocomplete, Korean intent patterns
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3. **Intent Classification** - Informational, navigational, commercial, transactional
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4. **Topic Clustering** - Group keywords into semantic clusters
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5. **Gap Analysis** - Find competitor keywords missing from target site
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## MCP Tool Usage
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### Ahrefs for Keyword Data
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```
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mcp__ahrefs__keywords-explorer-overview: Get keyword metrics
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mcp__ahrefs__keywords-explorer-matching-terms: Find keyword variations
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mcp__ahrefs__keywords-explorer-related-terms: Discover related keywords
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mcp__ahrefs__keywords-explorer-search-suggestions: Autocomplete suggestions
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mcp__ahrefs__keywords-explorer-volume-by-country: Country volume comparison
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mcp__ahrefs__site-explorer-organic-keywords: Competitor keyword rankings
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```
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### Web Search for Naver Discovery
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```
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WebSearch: Naver autocomplete and trend discovery
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```
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## Workflow
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### 1. Seed Keyword Expansion
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1. Input seed keyword (Korean or English)
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2. Query Ahrefs matching-terms and related-terms
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3. Get search suggestions for long-tail variations
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4. Apply Korean suffix expansion if Korean market
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5. Deduplicate and merge results
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### 2. Intent Classification & Clustering
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1. Classify each keyword by search intent
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2. Group keywords into topic clusters
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3. Identify pillar topics and supporting terms
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4. Calculate cluster-level metrics (total volume, avg KD)
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### 3. Gap Analysis
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1. Pull organic keywords for target and competitors
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2. Identify keywords present in competitors but missing from target
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3. Score opportunities by volume/difficulty ratio
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4. Prioritize by intent alignment with business goals
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## Output Format
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```markdown
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## Keyword Strategy Report: [seed keyword]
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### Overview
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- Total keywords discovered: [count]
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- Topic clusters: [count]
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- Total search volume: [sum]
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### Top Clusters
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| Cluster | Keywords | Total Volume | Avg KD |
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|---------|----------|-------------|--------|
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| ... | ... | ... | ... |
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### Top Opportunities
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| Keyword | Volume | KD | Intent | Cluster |
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|---------|--------|-----|--------|---------|
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| ... | ... | ... | ... | ... |
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### Keyword Gaps (vs competitors)
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| Keyword | Volume | Competitor Position | Opportunity Score |
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|---------|--------|-------------------|-------------------|
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| ... | ... | ... | ... |
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```
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## Notion Output (Required)
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All audit reports MUST be saved to OurDigital SEO Audit Log:
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- **Database ID**: `2c8581e5-8a1e-8035-880b-e38cefc2f3ef`
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- **Properties**: Issue (title), Site (url), Category, Priority, Found Date, Audit ID
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- **Language**: Korean with English technical terms
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- **Audit ID Format**: KW-YYYYMMDD-NNN
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