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Entity Optimizer

by @aaron-he-zhu

Use when the user asks to "optimize entity presence"; builds Knowledge Graph, Wikidata, sameAs, and AI recognition signals. 实体优化/知识图谱

Versionv9.9.9
Downloads2,118
Installs3
Stars2
TERMINAL
clawhub install entity-optimizer

📖 About This Skill


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: "9.9.5" license: Apache-2.0 compatibility: "Claude Code, skills.sh, ClawHub, Vercel Labs, Cursor, Windsurf, Codex CLI, Amp, Gemini CLI, Kimi Code, Qwen Code, CodeBuddy" 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: "" metadata: author: aaron-he-zhu version: "9.9.5" 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: # EN-formal - "optimize entity presence" - "build knowledge graph" - "entity audit" - "establish brand entity" - "entity disambiguation" # EN-casual - "Google doesn't know my brand" - "no knowledge panel" - "establish my brand as an entity" - "get a Google knowledge card" # EN-question - "how to get a knowledge panel" - "how to build brand entity" # ZH-pro - "实体优化" - "知识图谱" - "品牌实体" - "知识面板" - "品牌词" - "品牌词优化" # ZH-casual - "品牌搜不到" - "没有知识面板" - "Google不认识我的品牌" # JA - "エンティティ最適化" - "ナレッジパネル" # KO - "엔티티 최적화" - "지식 패널" - "구글이 내 브랜드 모르는데?" - "지식 패널 만들려면?" # ES - "optimización de entidad" - "panel de conocimiento" # PT - "otimização de entidade"

Entity Optimizer

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:

  • SEO: Google's Knowledge Graph powers Knowledge Panels, rich results, and entity-based ranking signals. A well-defined entity earns SERP real estate.
  • GEO: AI systems resolve queries to entities before generating answers. If an AI cannot identify an entity, it cannot cite it — no matter how good the content is.
  • What This Skill Does

    Audits entity presence across Knowledge Graph, Wikidata, Wikipedia, and AI systems; maps all 6 signal categories (47 signals); produces a gap analysis, building plan, and disambiguation strategy.

    Quick Start

    Start with one of these prompts. Finish with a canonical entity profile and a handoff summary using the repository format in Skill Contract.

    Entity Audit

    Audit entity presence for [brand/person/organization]
    

    How well do search engines and AI systems recognize [entity name]?
    

    Build Entity Presence

    Build entity presence for [new brand] in the [industry] space
    

    Establish [person name] as a recognized expert in [topic]
    

    Fix Entity Issues

    My Knowledge Panel shows incorrect information — fix entity signals for [entity]
    

    AI systems confuse [my entity] with [other entity] — help me disambiguate
    

    Skill Contract

    Expected output: an entity audit, a canonical entity profile, and a short handoff summary ready for memory/entities/.

  • Reads: the entity name, primary domain, known profiles, topic associations, and prior brand context from CLAUDE.md and the shared State Model when available.
  • Writes: a user-facing entity report plus a reusable profile that can be stored under memory/entities/.
  • Promotes: canonical names, sameAs links, disambiguation notes, and entity gaps to memory/hot-cache.md, memory/entities/, and memory/open-loops.md.
  • This skill is the sole writer of canonical entity profiles at memory/entities/.md. Other skills write entity candidates to memory/entities/candidates.md only. When 3+ candidates accumulate, this skill should be recommended.

    Profile schema: the frontmatter of every canonical entity profile follows the authoritative contract in references/entity-geo-handoff-schema.md. That schema defines which fields downstream skills (geo-content-optimizer, schema-markup-generator, meta-tags-optimizer, ai-overview-recovery) depend on. Do not omit required fields — the consumers will degrade gracefully to DONE_WITH_CONCERNS and surface an open_loop pointing back here.

  • Next handoff: use the Next Best Skill below once the entity truth is clear.
  • Handoff Summary

    > Emit the standard shape from skill-contract.md §Handoff Summary Format.

    Data Sources

    With tools: query Knowledge Graph API, ~~SEO tool, ~~AI monitor, ~~brand monitor. Without tools: ask the user for entity name/type, domain, profiles, topics, and disambiguation context. See CONNECTORS.md.

    Instructions

    When a user requests entity optimization:

    2. GDPR Art 6 lawful-basis prompt (for third-party persons, EU/EEA/UK data subjects) — if the entity being canonicalized is an individual (founder, author, public figure) and may be an EU/EEA/UK resident, the skill MUST prompt the user before writing to memory/entities/: "You are about to create a canonical profile for a person. If this person is or may be an EU/EEA/UK resident, GDPR Art 6 requires a lawful basis: (1) consent, (2) legitimate interest, (3) contract, (4) other. For non-EU subjects, check local regimes (CCPA/CPRA, PIPEDA, LGPD, etc.). If unsure, skip and return NEEDS_INPUT." Only proceed if user confirms a basis. Advisory only — not legal advice. Reference: memory-management §GDPR / Privacy Compliance.

    Step 1: Entity Discovery

    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] |

    Step 2: Entity Signal Audit

    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:

    1. Structured Data Signals -- Organization/Person schema, sameAs links, @id consistency, author schema 2. Knowledge Base Signals -- Wikidata, Wikipedia, CrunchBase, industry directories 3. Consistent NAP+E Signals -- Name/description/logo/social consistency across platforms 4. Content-Based Entity Signals -- About page, author pages, topical authority, branded backlinks 5. Third-Party Entity Signals -- Authoritative mentions, co-citation, reviews, press coverage 6. AI-Specific Entity Signals -- Clear definitions, disambiguation, verifiable claims, crawlability

    > Reference: Use the audit template in references/entity-signal-checklist.md for the full 47-signal checklist with verification methods for each category.

    Step 3: Report & Action Plan

    Produce an Entity Optimization Report with: overview (entity/type/date), signal category summary (6-category ✅/⚠️/❌ table with findings), critical issues, top 5 priority actions (impact × effort), entity building roadmap (Week 1-2 → Month 1 → Month 2-3 → Ongoing), and CORE-EEAT A07/A08 + CITE I01-I10 cross-reference.

    > Reference: See references/entity-signal-checklist.md for the full Step 3 report template.

    Save Results

    Ask "Save these results for future sessions?" — if yes, write the canonical entity profile to memory/entities/.md using the Profile schema above. If the entity is project-critical, also add a 1-3 line pointer to memory/hot-cache.md; do not save canonical profiles to the generic memory/YYYY-MM-DD-.md pattern.

    Example

    User: "Audit entity presence for Acme Analytics, our B2B SaaS analytics platform at acme-analytics.example"

    Output (abbreviated): AI resolution test shows partial recognition — ChatGPT described it as a generic "analytics tool" without B2B specificity; not listed among enterprise analytics players; founder unknown to AI systems. Health summary flags missing Wikidata entry, no Knowledge Panel, and 3 priority actions — Wikidata submission, sameAs links, and a founder-bio page.

    > Reference: See references/example-audit-report.md for the full entity audit report including AI resolution test results, entity health summary, top 3 priority actions, and CORE-EEAT/CITE cross-references.

    Tips for Success

    > Reference: See references/entity-signal-checklist.md for the full 7-item Tips for Success list (start with Wikidata, leverage sameAs, test AI recognition before/after, compounding signals, consistency > completeness, disambiguation-first, pair with CITE I-dimension).

    Entity Type Reference

    > Reference: See references/entity-type-reference.md for entity types with key signals, schemas, and disambiguation strategies by situation.

    Knowledge Panel & Wikidata Optimization

    > 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.

    Reference Materials

    Detailed guides for entity optimization:

  • references/entity-signal-checklist.md — Complete signal checklist with verification methods, Step 3 report template, and Tips for Success
  • references/knowledge-graph-guide.md — Wikidata, Wikipedia, and Knowledge Graph optimization playbook
  • Next Best Skill

    Primary: schema-markup-generator. Also consider: geo-content-optimizer (AI recognition gap) or seo-content-writer (new About/founder page needed).

    💡 Examples

    User: "Audit entity presence for Acme Analytics, our B2B SaaS analytics platform at acme-analytics.example"

    Output (abbreviated): AI resolution test shows partial recognition — ChatGPT described it as a generic "analytics tool" without B2B specificity; not listed among enterprise analytics players; founder unknown to AI systems. Health summary flags missing Wikidata entry, no Knowledge Panel, and 3 priority actions — Wikidata submission, sameAs links, and a founder-bio page.

    > Reference: See references/example-audit-report.md for the full entity audit report including AI resolution test results, entity health summary, top 3 priority actions, and CORE-EEAT/CITE cross-references.