Skill: aaron-he-zhu/entity-optimizer (version 9.9.9)

--- name: entity-optimizer description: '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' version: "9.9.9" 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: "<entity name or brand>" metadata: author: aaron-he-zhu version: "9.9.9" 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). ### 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/CLAUDE.md) and the shared [State Model](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/state-model.md) 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/<name>.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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/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. - **Primary next skill**: 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/references/skill-contract.md). ## 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/memory-management/SKILL.md). ### Step 1: Entity Discovery Establish the entity's current state across all systems. ```markdown ### 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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/<entity-slug>.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-<topic>.md` pattern. Before writing any canonical profile, check `memory/privacy/tombstones.md` for a matching salted fingerprint or redacted label. If `reingest_blocked: true`, do not recreate the profile; return `NEEDS_INPUT` and ask the user to resolve the privacy block. ## 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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/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](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/entity-signal-checklist.md) — Complete signal checklist with verification methods, Step 3 report template, and Tips for Success - [references/knowledge-graph-guide.md](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/cross-cutting/entity-optimizer/references/knowledge-graph-guide.md) — Wikidata, Wikipedia, and Knowledge Graph optimization playbook ## Next Best Skill Primary: [schema-markup-generator](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/schema-markup-generator/SKILL.md). Also consider: [geo-content-optimizer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/geo-content-optimizer/SKILL.md) (AI recognition gap) or [seo-content-writer](https://github.com/aaron-he-zhu/seo-geo-claude-skills/blob/main/build/seo-content-writer/SKILL.md) (new About/founder page needed).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)