New skills: - Skill 33: Site migration planner with redirect mapping and monitoring - Skill 34: Reporting dashboard with HTML charts and Korean executive reports Bug fixes (Skill 34 - report_aggregator.py): - Add audit_type fallback for skill identification (was only using audit_id prefix) - Extract health scores from nested data dict (technical_score, onpage_score, etc.) - Support subdomain matching in domain filter (blog.ourdigital.org matches ourdigital.org) - Skip self-referencing DASH- aggregated reports Bug fixes (Skill 20 - naver_serp_analyzer.py): - Remove VIEW tab selectors (removed by Naver in 2026) - Add new section detectors: books (도서), shortform (숏폼), influencer (인플루언서) Improvements (Skill 34 - dashboard/executive report): - Add Korean category labels for Chart.js charts (기술 SEO, 온페이지, etc.) - Add Korean trend labels (개선 중 ↑, 안정 →, 하락 중 ↓) - Add English→Korean issue description translation layer (20 common patterns) Documentation improvements: - Add Korean triggers to 4 skill descriptions (19, 25, 28, 31) - Expand Skill 32 SKILL.md from 40→143 lines (was 6/10, added workflow, output format, limitations) - Add output format examples to Skills 27 and 28 SKILL.md - Add limitations sections to Skills 27 and 28 - Update README.md, CLAUDE.md, AGENTS.md for skills 33-34 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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4.5 KiB
name, description
| name | description |
|---|---|
| seo-knowledge-graph | Knowledge Graph and entity SEO analysis. Triggers: knowledge panel, entity SEO, knowledge graph, PAA, FAQ schema, Wikipedia, Wikidata, brand entity, 지식 그래프, 엔티티 SEO, 지식 패널, 브랜드 엔티티, 위키데이터. |
Knowledge Graph & Entity SEO
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).
Capabilities
Knowledge Graph Analysis
- Knowledge Panel detection and attribute extraction
- Entity attribute completeness scoring (name, description, logo, type, social profiles, website, founded, CEO)
- Wikipedia article presence check
- Wikidata entity presence check (QID lookup)
- Naver encyclopedia (네이버 백과사전) presence
- Naver knowledge iN (지식iN) presence
Entity SEO Audit
- People Also Ask (PAA) monitoring for brand-related queries
- FAQ schema presence tracking (FAQPage schema -> SERP appearance)
- Entity markup audit (Organization, Person, LocalBusiness schema on website)
- Social profile linking validation (sameAs in schema)
- Brand SERP analysis (what appears when you search the brand name)
- Entity consistency across web properties
Workflow
Knowledge Graph Analysis
- Use WebSearch to search for the entity name on Google
- Analyze search results for Knowledge Panel indicators
- Use WebFetch to check Wikipedia article existence
- Use WebFetch to check Wikidata QID existence
- Use WebFetch to check Naver encyclopedia and 지식iN
- Score entity attribute completeness
- Save report to Notion SEO Audit Log
Entity SEO Audit
- Use WebFetch to fetch the website and extract JSON-LD schemas
- Validate Organization/Person/LocalBusiness schema completeness
- Check sameAs links accessibility
- Use WebSearch to search brand name and analyze SERP features
- Monitor PAA questions for brand keywords
- Use Ahrefs serp-overview for SERP feature detection
- Save report to Notion SEO Audit Log
Tools Used
| Tool | Purpose |
|---|---|
| WebSearch | Search for entity/brand to detect Knowledge Panel |
| WebFetch | Fetch Wikipedia, Wikidata, Naver pages, website schemas |
Ahrefs serp-overview |
SERP feature detection for entity keywords |
| Notion | Save audit reports to SEO Audit Log database |
Notion Output
All reports must be saved to the OurDigital SEO Audit Log database.
| Field | Value |
|---|---|
| Database ID | 2c8581e5-8a1e-8035-880b-e38cefc2f3ef |
| Category | Knowledge Graph & Entity SEO |
| Audit ID | KG-YYYYMMDD-NNN |
Report content should be written in Korean (한국어), keeping technical English terms as-is.
Output Format
{
"entity_name": "OurDigital",
"knowledge_panel": {
"present": false,
"attributes": {}
},
"entity_presence": {
"wikipedia": false,
"wikidata": false,
"wikidata_qid": null,
"naver_encyclopedia": false,
"naver_knowledge_in": false,
"google_knowledge_panel": false
},
"entity_schema": {
"organization_count": 2,
"person_count": 1,
"same_as_links": ["https://linkedin.com/...", "https://facebook.com/..."],
"same_as_count": 2,
"issues": [
"Duplicate Organization schemas with inconsistent names",
"Placeholder image in Organization schema",
"Only 2 sameAs links (recommend 6+)"
]
},
"paa_questions": [],
"faq_schema_present": false,
"entity_completeness_score": 12,
"recommendations": [
"Create Wikidata entity for brand recognition",
"Add 4-6 more sameAs social profile links",
"Replace placeholder image with actual brand logo",
"Consolidate duplicate Organization schemas",
"Add FAQPage schema to relevant pages"
],
"audit_id": "KG-20250115-001",
"timestamp": "2025-01-15T14:30:00"
}
Limitations
- Google Knowledge Panel detection via search results is not guaranteed (personalization, location-based)
- Direct Google scraping may be blocked (403/429); prefer WebSearch tool
- Wikipedia/Wikidata creation requires meeting notability guidelines
- PAA questions vary by location and device
- Entity completeness scoring is heuristic-based
Reference Scripts
Located in code/scripts/:
knowledge_graph_analyzer.py— Knowledge Panel and entity presence analysisentity_auditor.py— Entity SEO signals and PAA/FAQ auditbase_client.py— Shared async client utilities