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/28-seo-knowledge-graph/desktop/SKILL.md
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custom-skills/28-seo-knowledge-graph/desktop/SKILL.md
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---
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name: seo-knowledge-graph
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description: |
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Knowledge Graph and entity SEO analysis. Triggers: knowledge panel, entity SEO, knowledge graph, PAA, FAQ schema, Wikipedia, Wikidata, brand entity.
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---
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# Knowledge Graph & Entity SEO
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Analyze brand entity presence in Google Knowledge Graph, Knowledge Panels, People Also Ask (PAA), and FAQ rich results. Check entity attribute completeness, Wikipedia/Wikidata presence, and Korean equivalents (Naver knowledge iN, Naver encyclopedia).
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## Capabilities
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### Knowledge Graph Analysis
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- Knowledge Panel detection and attribute extraction
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- Entity attribute completeness scoring (name, description, logo, type, social profiles, website, founded, CEO)
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- Wikipedia article presence check
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- Wikidata entity presence check (QID lookup)
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- Naver encyclopedia (네이버 백과사전) presence
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- Naver knowledge iN (지식iN) presence
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### Entity SEO Audit
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- People Also Ask (PAA) monitoring for brand-related queries
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- FAQ schema presence tracking (FAQPage schema -> SERP appearance)
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- Entity markup audit (Organization, Person, LocalBusiness schema on website)
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- Social profile linking validation (sameAs in schema)
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- Brand SERP analysis (what appears when you search the brand name)
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- Entity consistency across web properties
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## Workflow
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### Knowledge Graph Analysis
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1. Use **WebSearch** to search for the entity name on Google
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2. Analyze search results for Knowledge Panel indicators
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3. Use **WebFetch** to check Wikipedia article existence
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4. Use **WebFetch** to check Wikidata QID existence
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5. Use **WebFetch** to check Naver encyclopedia and 지식iN
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6. Score entity attribute completeness
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7. Save report to **Notion** SEO Audit Log
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### Entity SEO Audit
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1. Use **WebFetch** to fetch the website and extract JSON-LD schemas
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2. Validate Organization/Person/LocalBusiness schema completeness
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3. Check sameAs links accessibility
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4. Use **WebSearch** to search brand name and analyze SERP features
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5. Monitor PAA questions for brand keywords
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6. Use **Ahrefs serp-overview** for SERP feature detection
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7. Save report to **Notion** SEO Audit Log
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## Tools Used
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| Tool | Purpose |
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|------|---------|
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| WebSearch | Search for entity/brand to detect Knowledge Panel |
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| WebFetch | Fetch Wikipedia, Wikidata, Naver pages, website schemas |
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| Ahrefs `serp-overview` | SERP feature detection for entity keywords |
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| Notion | Save audit reports to SEO Audit Log database |
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## Notion Output
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All reports must be saved to the OurDigital SEO Audit Log database.
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| Field | Value |
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|-------|-------|
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| Database ID | `2c8581e5-8a1e-8035-880b-e38cefc2f3ef` |
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| Category | Knowledge Graph & Entity SEO |
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| Audit ID | KG-YYYYMMDD-NNN |
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Report content should be written in Korean (한국어), keeping technical English terms as-is.
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## Reference Scripts
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Located in `code/scripts/`:
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- `knowledge_graph_analyzer.py` -- Knowledge Panel and entity presence analysis
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- `entity_auditor.py` -- Entity SEO signals and PAA/FAQ audit
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- `base_client.py` -- Shared async client utilities
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custom-skills/28-seo-knowledge-graph/desktop/skill.yaml
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custom-skills/28-seo-knowledge-graph/desktop/skill.yaml
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name: seo-knowledge-graph
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description: |
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Knowledge Graph and entity SEO analysis. Triggers: knowledge panel, entity SEO, knowledge graph, PAA, FAQ schema, Wikipedia, Wikidata, brand entity.
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allowed-tools:
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- mcp__ahrefs__*
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- mcp__notion__*
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- WebSearch
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- WebFetch
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custom-skills/28-seo-knowledge-graph/desktop/tools/ahrefs.md
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custom-skills/28-seo-knowledge-graph/desktop/tools/ahrefs.md
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# Ahrefs MCP Tools
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## serp-overview
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Retrieve SERP feature overview for specified keywords. Used to detect Knowledge Panel, PAA, FAQ rich results, and other SERP features for entity-related queries.
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### Parameters
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| Parameter | Type | Required | Description |
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|-----------|------|----------|-------------|
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| keyword | string | Yes | Keyword to check SERP features for |
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| country | string | No | Country code (default: us) |
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### Usage
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```
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mcp__ahrefs__serp-overview(keyword="Samsung Electronics", country="us")
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```
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### Relevant SERP Features
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- Knowledge Panel
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- People Also Ask (PAA)
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- FAQ Rich Results
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- Featured Snippet
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- Sitelinks
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- Image Pack
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- Video Carousel
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- Local Pack
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custom-skills/28-seo-knowledge-graph/desktop/tools/notion.md
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custom-skills/28-seo-knowledge-graph/desktop/tools/notion.md
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# Notion MCP Tools
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## Save Audit Report
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Save Knowledge Graph & Entity SEO audit reports to the OurDigital SEO Audit Log database.
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### Database Configuration
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| Field | Value |
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|-------|-------|
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| Database ID | `2c8581e5-8a1e-8035-880b-e38cefc2f3ef` |
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| URL | https://www.notion.so/dintelligence/2c8581e58a1e8035880be38cefc2f3ef |
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### Required Properties
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| Property | Type | Description |
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|----------|------|-------------|
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| Issue | Title | Report title (Korean + date) |
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| Site | URL | Entity website URL |
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| Category | Select | Knowledge Graph & Entity SEO |
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| Priority | Select | Critical / High / Medium / Low |
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| Found Date | Date | Audit date (YYYY-MM-DD) |
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| Audit ID | Rich Text | Format: KG-YYYYMMDD-NNN |
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### Priority Mapping
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| Overall Score | Priority |
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|---------------|----------|
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| 0-25 | Critical |
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| 26-50 | High |
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| 51-75 | Medium |
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| 76-100 | Low |
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### Example Usage
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```
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mcp__notion__notion-create-pages({
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"parent": {"database_id": "2c8581e5-8a1e-8035-880b-e38cefc2f3ef"},
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"properties": {
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"Issue": {"title": [{"text": {"content": "Knowledge Graph 엔티티 분석 - 2025-01-15"}}]},
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"Site": {"url": "https://example.com"},
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"Category": {"select": {"name": "Knowledge Graph & Entity SEO"}},
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"Priority": {"select": {"name": "High"}},
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"Found Date": {"date": {"start": "2025-01-15"}},
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"Audit ID": {"rich_text": [{"text": {"content": "KG-20250115-001"}}]}
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}
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})
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```
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### Language Guidelines
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- Report content in Korean (한국어)
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- Keep technical English terms as-is (Knowledge Panel, Knowledge Graph, PAA, FAQ, schema)
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- URLs and code remain unchanged
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# WebSearch & WebFetch Tools
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## WebSearch
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Search the web for entity/brand information to detect Knowledge Panel signals, PAA questions, and SERP features.
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### Usage Patterns
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#### Knowledge Panel Detection
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```
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WebSearch("Samsung Electronics")
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WebSearch("삼성전자")
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```
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#### PAA Question Discovery
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```
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WebSearch("what is Samsung Electronics")
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WebSearch("Samsung Electronics reviews")
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WebSearch("Samsung Electronics alternatives")
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```
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#### Brand SERP Analysis
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```
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WebSearch("Brand Name official")
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WebSearch("Brand Name site:wikipedia.org")
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```
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## WebFetch
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Fetch and analyze specific pages for entity presence data.
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### Usage Patterns
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#### Wikipedia Check
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```
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WebFetch("https://en.wikipedia.org/wiki/Samsung_Electronics", "Check if article exists and extract entity information")
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```
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#### Wikidata Check
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```
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WebFetch("https://www.wikidata.org/w/api.php?action=wbsearchentities&search=Samsung&language=en&format=json", "Find Wikidata QID for entity")
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```
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#### Naver Encyclopedia
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```
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WebFetch("https://terms.naver.com/search.naver?query=삼성전자", "Check Naver encyclopedia entry")
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```
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#### Naver Knowledge iN
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```
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WebFetch("https://kin.naver.com/search/list.naver?query=삼성전자", "Check knowledge iN entries count")
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```
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#### Website Schema Extraction
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```
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WebFetch("https://example.com", "Extract JSON-LD structured data, especially Organization, Person, LocalBusiness schemas and sameAs links")
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```
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#### FAQ Schema Check
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```
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WebFetch("https://example.com/faq", "Check for FAQPage JSON-LD schema markup")
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```
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