# Entity Optimizer > Use when the user asks to "optimize entity presence"; builds Knowledge Graph, Wikidata, sameAs, and AI recognition signals for a canonical entity identity. Not for page-level AI-citation readiness — use geo-content-optimizer. 实体优化/知识图谱 - Skill: `tuyv/entity-optimizer` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add tuyv/entity-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tuyv/entity-optimizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - License: Apache-2.0 - Author: tuyv (https://skillmd.com/u/tuyv) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tuyv/entity-optimizer --- # 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](../../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. - **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`. - **Done when**: the 6 signal categories are each scored Pass/Fail/Partial, the AI-resolution test is run (or flagged as user-to-run), and a canonical profile plus top-5 priority actions are produced. 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 [Entity-GEO Handoff Schema](../../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](../../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](../../CONNECTORS.md). **Zero-dependency local helper** (keyless): `python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/kg.py" reconcile ""` resolves the name to a Wikidata QID with a confidence score (does the open KG that feeds Knowledge Panels & AI answers recognize it?); `kg.py entity ` returns claims + sameAs. See [scripts/connectors/README.md](../../scripts/connectors/README.md). ## Decision Gates **Stop and ask the user when:** - No entity name is provided and none is inferable from project context — ask for the entity name and type before auditing. - The entity is an individual (founder, author, public figure) who may be an EU/EEA/UK resident, before writing to `memory/entities/` — prompt: "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 once the user confirms a basis. Advisory only — not legal advice. Reference: [Memory Management — GDPR / Privacy Compliance](../memory-management/SKILL.md). **Continue silently (never stop for):** - Missing ~~AI monitor or ~~knowledge graph tool access — mark those rows as user-to-run and proceed with user-provided observations. - Individual signals being unknown — score them Partial with a verification action and continue. ## Instructions When a user requests entity optimization: ### 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 [Entity Signal Checklist](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 [Entity Signal Checklist](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 [Entity Signal Checklist](references/entity-signal-checklist.md) for the full Step 3 report template. ### Save Results Ask "Save these results for future sessions?" (see [Skill Contract](../../references/skill-contract.md) §Save Results Template) — 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. Before writing any canonical profile, check `memory/audits/gdpr-purges.md` for a prior purge of this entity (by redacted label or domain). If one exists, do not silently recreate the profile; return `NEEDS_INPUT` and ask the user to confirm the entity should be re-added. ## 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 a missing Wikidata entry and no Knowledge Panel, with priority actions covering Wikidata submission, sameAs links, and a founder-bio page. > **Reference**: See [Example Audit Report](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. ## Entity Type Reference > **Reference**: See [Entity Type Reference](references/entity-type-reference.md) for entity types with key signals, schemas, and disambiguation strategies by situation. ## Knowledge Panel & Wikidata Optimization > **Reference**: See [Knowledge Panel & Wikidata Guide](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: - [Entity Signal Checklist](references/entity-signal-checklist.md) — Complete signal checklist with verification methods, Step 3 report template, and Tips for Success - [Knowledge Graph Guide](references/knowledge-graph-guide.md) — Wikidata, Wikipedia, and Knowledge Graph optimization playbook ## Next Best Skill Primary: [schema-markup-generator](../../build/schema-markup-generator/SKILL.md). Also consider: [geo-content-optimizer](../../build/geo-content-optimizer/SKILL.md) (AI recognition gap) or [seo-content-writer](../../build/seo-content-writer/SKILL.md) (new About/founder page needed).