By name: entity-optimizer
description: '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'
version: "9.9.10"
license: Apache-2.0
compatibility: "Claude Code and compatible agent-skill hosts"
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 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.
- 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/, andmemory/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/<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 Entity-GEO Handoff Schema. 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 Skillbelow 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.
Zero-dependency local helper (keyless): python3 scripts/connectors/kg.py reconcile "<entity>" 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 <QID> returns claims + sameAs. See 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.
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.
### 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.
Evaluate each signal as Pass / Fail / Partial with a specific action for each gap. The 6 categories are:
- Structured Data Signals — Organization/Person schema, sameAs links, @id consistency, author schema
- Knowledge Base Signals — Wikidata, Wikipedia, CrunchBase, industry directories
- Consistent NAP+E Signals — Name/description/logo/social consistency across platforms
- Content-Based Entity Signals — About page, author pages, topical authority, branded backlinks
- Third-Party Entity Signals — Authoritative mentions, co-citation, reviews, press coverage
- AI-Specific Entity Signals — Clear definitions, disambiguation, verifiable claims, crawlability
Reference: Use the audit template in Entity Signal Checklist 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 for the full Step 3 report template.
Save Results
Ask "Save these results for future sessions?" (see Skill Contract §Save Results Template) — 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 Example Audit Report 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 Entity Signal Checklist 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 Entity Type Reference for entity types with key signals, schemas, and disambiguation strategies by situation.
Knowledge Panel & Wikidata Optimization
Reference: See Knowledge Panel & Wikidata Guide 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 — Complete signal checklist with verification methods, Step 3 report template, and Tips for Success
- Knowledge Graph Guide — 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).