メニュー
Build entity presence in Knowledge Graph, Wikidata, AI systems for brand recognition and citations. 实体优化/知识图谱
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/MODSetter/SurfSense --skill entity-optimizerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.
Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.
Structured approach to finding and synthesizing information from the user's knowledge base
SOC 職業分類に基づく
SKILL.md を表示中
| name | entity-optimizer |
| description | Build entity presence in Knowledge Graph, Wikidata, AI systems for brand recognition and citations. 实体优化/知识图谱 |
| version | 6.0.0 |
| license | Apache-2.0 |
| compatibility | Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations. |
| homepage | https://github.com/aaron-he-zhu/seo-geo-claude-skills |
| when_to_use | Use when optimizing entity presence for Knowledge Graph, Wikidata, or AI engine disambiguation. Also for brand entity canonicalization. |
| argument-hint | <entity name or brand> |
| metadata | {"author":"aaron-he-zhu","version":"6.0.0","geo-relevance":"high","tags":["seo","geo","entity-optimization","knowledge-graph","knowledge-panel","brand-entity","wikidata","entity-disambiguation","实体优化","エンティティ","엔티티","entidad-seo"],"triggers":["optimize entity presence","build knowledge graph","improve knowledge panel","entity audit","establish brand entity","entity disambiguation","Google doesn't know my brand","no knowledge panel","establish my brand","establish my brand as an entity","get a Google knowledge card","no Wikipedia entry","how to get a knowledge panel","how to build brand entity","实体优化","知识图谱","品牌实体","知识面板","品牌词","品牌词优化","品牌搜不到","没有知识面板","Google不认识我的品牌","エンティティ最適化","ナレッジパネル","엔티티 최적화","지식 패널","구글이 내 브랜드 모르는데?","지식 패널 만들려면?","optimización de entidad","panel de conocimiento","otimização de entidade","knowlege panel","enity optimization"]} |
SEO & GEO Skills Library · 20 skills for SEO + GEO · ClawHub · skills.sh System Mode: This cross-cutting skill is part of the protocol layer and follows the shared Skill Contract and State Model.
Audits, builds, and maintains entity identity across search engines and AI systems. Entities — the people, organizations, products, and concepts that search engines and AI systems recognize as distinct things — are the foundation of how both Google and LLMs decide what a brand is and whether to cite it.
Why entities matter for SEO + GEO:
System role: Canonical Entity Profile. It acts as the source of truth for entity identity, associations, and disambiguation across the library.
Use this when brand or entity identity needs to be established or verified — even if the user doesn't use entity terminology:
memory/entities/candidates.md accumulates 3 or more uncanonized entity candidates from other skillsStart with one of these prompts. Finish with a canonical entity profile and a handoff summary using the repository format in Skill Contract.
Audit entity presence for [brand/person/organization]
How well do search engines and AI systems recognize [entity name]?
Build entity presence for [new brand] in the [industry] space
Establish [person name] as a recognized expert in [topic]
My Knowledge Panel shows incorrect information — fix entity signals for [entity]
AI systems confuse [my entity] with [other entity] — help me disambiguate
Expected output: an entity audit, a canonical entity profile, and a short handoff summary ready for memory/entities/.
memory/entities/.CLAUDE.md, memory/entities/, and memory/open-loops.md.This skill is the sole writer of canonical entity profiles at memory/entities/<name>.md. Other skills write entity candidates to memory/entities/candidates.md only. When 3+ candidates accumulate, this skill should be recommended.
Next Best Skill below once the entity truth is clear.See CONNECTORS.md for tool category placeholders.
With ~~knowledge graph + ~~SEO tool + ~~AI monitor + ~~brand monitor connected: Query Knowledge Graph API for entity status, pull branded search data from ~~SEO tool, test AI citation with ~~AI monitor, track brand mentions with ~~brand monitor.
With manual data only: Ask the user to provide:
Without tools, Claude provides entity optimization strategy and recommendations based on information the user provides. The user must run search queries, check Knowledge Panels, and test AI responses to supply the raw data for analysis.
Proceed with the audit using public search results, AI query testing, and SERP analysis. Note which items require tool access for full evaluation.
When a user requests entity optimization:
Establish the entity's current state across all systems.
### Entity Profile
**Entity Name**: [name]
**Entity Type**: [Person / Organization / Brand / Product / Creative Work / Event]
**Primary Domain**: [URL]
**Target Topics**: [topic 1, topic 2, topic 3]
#### Current Entity Presence
| Platform | Status | Details |
|----------|--------|---------|
| Google Knowledge Panel | ✅ Present / ❌ Absent / ⚠️ Incorrect | [details] |
| Wikidata | ✅ Listed / ❌ Not listed | [QID if exists] |
| Wikipedia | ✅ Article / ⚠️ Mentioned only / ❌ Absent | [notability assessment] |
| Google Knowledge Graph API | ✅ Entity found / ❌ Not found | [entity ID, types, score] |
| Schema.org on site | ✅ Complete / ⚠️ Partial / ❌ Missing | [Organization/Person/Product schema] |
#### AI Entity Resolution Test
**Note**: Claude cannot directly query other AI systems or perform real-time web searches without tool access. When running without ~~AI monitor or ~~knowledge graph tools, ask the user to run these test queries and report the results, or use the user-provided information to assess entity presence.
Test how AI systems identify this entity by querying:
- "What is [entity name]?"
- "Who founded [entity name]?" (for organizations)
- "What does [entity name] do?"
- "[entity name] vs [competitor]"
| AI System | Recognizes Entity? | Description Accuracy | Cites Entity's Content? |
|-----------|-------------------|---------------------|------------------------|
| ChatGPT | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] |
| Claude | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] |
| Perplexity | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] |
| Google AI Overview | ✅ / ⚠️ / ❌ | [accuracy notes] | [yes/no/partially] |
Evaluate entity signals across 6 categories. For the detailed 47-signal checklist with verification methods, see references/entity-signal-checklist.md.
Evaluate each signal as Pass / Fail / Partial with a specific action for each gap. The 6 categories are:
Reference: Use the audit template in references/entity-signal-checklist.md for the full 47-signal checklist with verification methods for each category.
## Entity Optimization Report
### Overview
- **Entity**: [name]
- **Entity Type**: [type]
- **Audit Date**: [date]
### Signal Category Summary
| Category | Status | Key Findings |
|----------|--------|-------------|
| Structured Data | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
| Knowledge Base | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
| Consistency (NAP+E) | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
| Content-Based | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
| Third-Party | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
| AI-Specific | ✅ Strong / ⚠️ Gaps / ❌ Missing | [key findings] |
### Critical Issues
[List any issues that severely impact entity recognition — disambiguation problems, incorrect Knowledge Panel, missing from Knowledge Graph entirely]
### Top 5 Priority Actions
Sorted by: impact on entity recognition × effort required
1. **[Signal]** — [specific action]
- Impact: [High/Medium] | Effort: [Low/Medium/High]
- Why: [explanation of how this improves entity recognition]
2. **[Signal]** — [specific action]
- Impact: [High/Medium] | Effort: [Low/Medium/High]
- Why: [explanation]
3–5. [Same format]
### Entity Building Roadmap
#### Week 1-2: Foundation (Structured Data + Consistency)
- [ ] Implement/fix Organization or Person schema with full properties
- [ ] Add sameAs links to all authoritative profiles
- [ ] Audit and fix NAP+E consistency across all platforms
- [ ] Ensure About page is entity-rich and well-structured
#### Month 1: Knowledge Bases
- [ ] Create or update Wikidata entry with complete properties
- [ ] Ensure CrunchBase / industry directory profiles are complete
- [ ] Build Wikipedia notability (or plan path to notability)
- [ ] Submit to relevant authoritative directories
#### Month 2-3: Authority Building
- [ ] Secure mentions on authoritative industry sites
- [ ] Build co-citation signals with established entities
- [ ] Create topical content clusters that reinforce entity-topic associations
- [ ] Pursue PR opportunities that generate entity mentions
#### Ongoing: AI-Specific Optimization
- [ ] Test AI entity resolution quarterly
- [ ] Update factual claims to remain current and verifiable
- [ ] Monitor AI systems for incorrect entity information
- [ ] Ensure new content reinforces entity identity signals
### Cross-Reference
- **CORE-EEAT relevance**: Items A07 (Knowledge Graph Presence) and A08 (Entity Consistency) directly overlap — entity optimization strengthens Authority dimension
- **CITE relevance**: CITE I01-I10 (Identity dimension) measures entity signals at domain level — entity optimization feeds these scores
- For content-level audit: `content-quality-auditor`
- For domain-level audit: `domain-authority-auditor`
After delivering findings to the user, ask:
"Save these results for future sessions?"
If yes, write a dated summary to the appropriate memory/ path using filename YYYY-MM-DD-<topic>.md containing:
If any veto-level issue was found (CORE-EEAT T04, C01, R10 or CITE T03, T05, T09), also append a one-liner to memory/hot-cache.md without asking.
Reference: See references/example-audit-report.md for a complete example entity audit report for a B2B SaaS company (CloudMetrics), including AI entity resolution test results, entity health summary, top 3 priority actions, and CORE-EEAT/CITE cross-references.
Reference: See references/entity-type-reference.md for entity types with key signals, schemas, and disambiguation strategies by situation.
Reference: See references/knowledge-panel-wikidata-guide.md for Knowledge Panel claiming/editing, common issues and fixes, Wikidata entry creation, key properties by entity type, and AI entity resolution optimization.
Detailed guides for entity optimization: