Andrew Yim
a3ff965b87
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>
78 lines
2.9 KiB
Markdown
78 lines
2.9 KiB
Markdown
---
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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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