entity-optimizer

name	entity-optimizer
description	Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.
license	Apache-2.0
metadata	{"author":"aaron-he-zhu","version":"2.0.0","geo-relevance":"high","tags":["seo","geo","entity optimization","knowledge graph","knowledge panel","brand entity","entity disambiguation","wikidata","structured entities"],"triggers":["optimize entity presence","build knowledge graph","improve knowledge panel","entity audit","establish brand entity","knowledge panel","entity disambiguation","Google doesn't know my brand","no knowledge panel","establish my brand as an entity"]}

Entity Optimizer

SEO & GEO Skills Library · 20 skills for SEO + GEO · Install all: npx skills add aaron-he-zhu/seo-geo-claude-skills

Browse all 20 skills

Research · keyword-research · competitor-analysis · serp-analysis · content-gap-analysis

Build · seo-content-writer · geo-content-optimizer · meta-tags-optimizer · schema-markup-generator

Optimize · on-page-seo-auditor · technical-seo-checker · internal-linking-optimizer · content-refresher

Monitor · rank-tracker · backlink-analyzer · performance-reporter · alert-manager

Cross-cutting · content-quality-auditor · domain-authority-auditor · entity-optimizer · memory-management

This skill 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 you are and whether to cite you.

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 can't identify your entity, it can't cite you — no matter how good your content is.

When to Use This Skill

• Establishing a new brand/person/product as a recognized entity
• Auditing current entity presence across Knowledge Graph, Wikidata, and AI systems
• Improving or correcting a Knowledge Panel
• Building entity associations (entity ↔ topic, entity ↔ industry)
• Resolving entity disambiguation issues (your entity confused with another)
• Strengthening entity signals for AI citation
• After launching a new brand, product, or organization
• Preparing for a site migration (preserving entity identity)
• Running periodic entity health checks

What This Skill Does

1. Entity Audit: Evaluates current entity presence across search and AI systems
2. Knowledge Graph Analysis: Checks Google Knowledge Graph, Wikidata, and Wikipedia status
3. AI Entity Resolution Test: Queries AI systems to see how they identify and describe the entity
4. Entity Signal Mapping: Identifies all signals that establish entity identity
5. Gap Analysis: Finds missing or weak entity signals
6. Entity Building Plan: Creates actionable plan to establish or strengthen entity presence
7. Disambiguation Strategy: Resolves confusion with similarly-named entities

How to Use

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

Data Sources

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:

1. Entity name, type (Person, Organization, Brand, Product, Creative Work, Event)
2. Primary website / domain
3. Known existing profiles (Wikipedia, Wikidata, social media, industry directories)
4. Top 3-5 topics/industries the entity should be associated with
5. Any known disambiguation issues (other entities with same/similar name)

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.

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 references/entity-signal-checklist.md.

### Entity Signal Audit

#### 1. Structured Data Signals

| Signal | Status | Action Needed |
|--------|--------|--------------|
| Organization/Person schema on homepage | ✅ / ❌ | [action] |
| sameAs links to authoritative profiles | ✅ / ❌ | [action] |
| logo, foundingDate, founder properties | ✅ / ❌ | [action] |
| Consistent @id across pages | ✅ / ❌ | [action] |
| Product/Service schema on relevant pages | ✅ / ❌ | [action] |
| Author schema with sameAs on articles | ✅ / ❌ | [action] |

#### 2. Knowledge Base Signals

| Signal | Status | Action Needed |
|--------|--------|--------------|
| Wikidata entry with complete properties | ✅ / ❌ | [action] |
| Wikipedia article (or notability path) | ✅ / ❌ | [action] |
| CrunchBase profile (organizations) | ✅ / ❌ | [action] |
| Industry directory listings | ✅ / ❌ | [action] |
| Government/official registries | ✅ / ❌ | [action] |

#### 3. Consistent NAP+E Signals (Name, Address, Phone + Entity)

| Signal | Status | Action Needed |
|--------|--------|--------------|
| Consistent entity name across all platforms | ✅ / ❌ | [action] |
| Same description/tagline everywhere | ✅ / ❌ | [action] |
| Matching logos and visual identity | ✅ / ❌ | [action] |
| Social profiles all linked bidirectionally | ✅ / ❌ | [action] |
| Contact info consistent across directories | ✅ / ❌ | [action] |

#### 4. Content-Based Entity Signals

| Signal | Status | Action Needed |
|--------|--------|--------------|
| About page with entity-rich structured content | ✅ / ❌ | [action] |
| Author pages with credentials and sameAs | ✅ / ❌ | [action] |
| Topical authority (content depth in target topics) | ✅ / ❌ | [action] |
| Entity mentions in content (natural co-occurrence) | ✅ / ❌ | [action] |
| Branded anchor text in backlinks | ✅ / ❌ | [action] |

#### 5. Third-Party Entity Signals

| Signal | Status | Action Needed |
|--------|--------|--------------|
| Mentions on authoritative sites (news, industry) | ✅ / ❌ | [action] |
| Co-citation with established entities | ✅ / ❌ | [action] |
| Reviews and ratings on third-party platforms | ✅ / ❌ | [action] |
| Speaking engagements, awards, publications | ✅ / ❌ | [action] |
| Press coverage with entity name | ✅ / ❌ | [action] |

#### 6. AI-Specific Entity Signals

| Signal | Status | Action Needed |
|--------|--------|--------------|
| Clear entity definition in opening paragraphs | ✅ / ❌ | [action] |
| Unambiguous entity name (or disambiguation strategy) | ✅ / ❌ | [action] |
| Factual claims about entity are verifiable | ✅ / ❌ | [action] |
| Entity appears in AI training data sources | ✅ / ❌ | [action] |
| Entity's content is crawlable by AI systems | ✅ / ❌ | [action] |

Step 3: Report & Action Plan

## 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](../content-quality-auditor/)
- For domain-level audit: [domain-authority-auditor](../domain-authority-auditor/)

Validation Checkpoints

Input Validation

• Entity name and type identified
• Primary domain/website confirmed
• Target topics/industries specified
• Disambiguation context provided (if entity name is common)

Output Validation

• All 6 signal categories evaluated
• AI entity resolution tested with at least 3 queries
• Knowledge Panel status checked
• Wikidata/Wikipedia status verified
• Schema.org markup on primary site audited
• Every recommendation is specific and actionable
• Roadmap includes concrete steps with timeframes
• Cross-reference with CORE-EEAT A07/A08 and CITE I01-I10 noted

Example

User: "Audit entity presence for CloudMetrics, our B2B SaaS analytics platform at cloudmetrics.io"

Output:

## Entity Optimization Report

### Entity Profile

**Entity Name**: CloudMetrics
**Entity Type**: Organization (B2B SaaS)
**Primary Domain**: cloudmetrics.io
**Target Topics**: analytics platform, business intelligence, enterprise analytics

### AI Entity Resolution Test

Queries tested with results reported by user:

| Query | Result | Assessment |
|-------|--------|------------|
| "What is CloudMetrics?" | Described as "an analytics tool" with no further detail | Partial recognition -- generic description, no mention of B2B focus or key features |
| "Best analytics platforms for enterprises" | CloudMetrics not mentioned in any AI response | Not recognized as a player in the enterprise analytics space |
| "CloudMetrics vs Datadog" | Correctly identified as a competitor to Datadog, but feature comparison was incomplete and partially inaccurate | Partial -- entity is associated with the right category but attributes are thin |
| "Who founded CloudMetrics?" | No answer found by any AI system tested | Entity leadership not present in AI knowledge bases |

### Entity Health Summary

| Signal Category | Status | Key Findings |
|-----------------|--------|--------------|
| Knowledge Graph | ❌ Missing | No Wikidata entry exists; no Google Knowledge Panel triggers for branded queries |
| Structured Data | ⚠️ Partial | Organization schema present on homepage with name, url, and logo; missing Person schema for CEO and leadership team; no sameAs links to external profiles |
| Web Presence | ✅ Strong | Consistent NAP across LinkedIn, Twitter/X, G2, and Crunchbase; social profiles link back to cloudmetrics.io; branded search returns owned properties in top 5 |
| Content-Based | ⚠️ Partial | About page exists but opens with marketing copy rather than an entity-defining statement; no dedicated author pages for leadership |
| Third-Party | ⚠️ Partial | Listed on G2 and Crunchbase; 2 industry publication mentions found; no awards or analyst coverage |
| AI-Specific | ❌ Weak | AI systems have only surface-level awareness; entity definition is not quotable from any authoritative source |

### Top 3 Priority Actions

1. **Create Wikidata entry** with key properties: instance of (P31: business intelligence software company), official website (P856: cloudmetrics.io), inception (P571), country (P17)
   - Impact: High | Effort: Low
   - Why: Wikidata is the foundational knowledge base that feeds Google Knowledge Graph, Bing, and AI training pipelines; without it, the entity cannot be formally resolved

2. **Add Person schema for leadership team** on the About/Team page, including name, jobTitle, sameAs links to LinkedIn profiles, and worksFor pointing to the Organization entity
   - Impact: High | Effort: Low
   - Why: Addresses the "Who founded CloudMetrics?" gap directly; Person schema for key people creates bidirectional entity associations that strengthen organizational identity

3. **Build Wikipedia notability through independent press coverage** -- target 3-5 articles in industry publications (TechCrunch, VentureBeat, Analytics India Magazine) that mention CloudMetrics by name with verifiable claims
   - Impact: High | Effort: High
   - Why: Wikipedia notability requires coverage in independent reliable sources; press mentions simultaneously feed AI training data, build third-party entity signals, and create the citation foundation for a future Wikipedia article

### Cross-Reference

- **CORE-EEAT**: A07 (Knowledge Graph Presence) scored Fail, A08 (Entity Consistency) scored Pass -- entity optimization should focus on knowledge base gaps rather than consistency
- **CITE**: I-dimension weakest area is I01 (Knowledge Graph Presence) -- completing Wikidata entry and earning Knowledge Panel directly improves domain identity score

Tips for Success

1. Start with Wikidata — It's the single most influential editable knowledge base; a complete Wikidata entry with references often triggers Knowledge Panel creation within weeks
2. sameAs is your most powerful Schema.org property — It directly tells search engines "I am this entity in the Knowledge Graph"; always include Wikidata URL first
3. Test AI recognition before and after — Query ChatGPT, Claude, Perplexity, and Google AI Overview before optimizing, then again after; this is the most direct GEO metric
4. Entity signals compound — Unlike content SEO, entity signals from different sources reinforce each other; 5 weak signals together are stronger than 1 strong signal alone
5. Consistency beats completeness — A consistent entity name and description across 10 platforms beats a perfect profile on just 2
6. Don't neglect disambiguation — If your entity name is shared with anything else, disambiguation is the first priority; all other signals are wasted if they're attributed to the wrong entity
7. Pair with CITE I-dimension for domain context — Entity audit tells you how well the entity is recognized; CITE Identity (I01-I10) tells you how well the domain represents that entity; use both together

Entity Type Reference

Entity Types and Key Signals

Entity Type	Primary Signals	Secondary Signals	Key Schema
Person	Author pages, social profiles, publication history	Speaking, awards, media mentions	Person, ProfilePage
Organization	Registration records, Wikidata, industry listings	Press coverage, partnerships, awards	Organization, Corporation
Brand	Trademark, branded search volume, social presence	Reviews, brand mentions, visual identity	Brand, Organization
Product	Product pages, reviews, comparison mentions	Awards, expert endorsements, market share	Product, SoftwareApplication
Creative Work	Publication record, citations, reviews	Awards, adaptations, cultural impact	CreativeWork, Book, Movie
Event	Event listings, press coverage, social buzz	Sponsorships, speaker profiles, attendance	Event

Disambiguation Strategy by Situation

Situation	Strategy
Common name, unique entity	Strengthen all signals; let signal volume resolve ambiguity
Name collision with larger entity	Add qualifier consistently (e.g., "Acme Software" not just "Acme"); use sameAs extensively; build topic-specific authority that differentiates
Name collision with similar entity	Geographic, industry, or product qualifiers; ensure Schema @id is unique and consistent; prioritize Wikidata disambiguation
Abbreviation/acronym conflict	Prefer full name in structured data; use abbreviation only in contexts where entity is already established
Merged or renamed entity	Redirect old entity signals; update all structured data; create explicit "formerly known as" content; update Wikidata

Knowledge Panel Optimization

Claiming and Editing

1. Google Knowledge Panel: Claim via Google's verification process (search for entity → click "Claim this knowledge panel")
2. Bing Knowledge Panel: Driven by Wikidata and LinkedIn — update those sources
3. AI Knowledge: Driven by training data — ensure authoritative sources describe entity correctly

Common Knowledge Panel Issues

Issue	Root Cause	Fix
No panel appears	Entity not in Knowledge Graph	Build Wikidata entry + structured data + authoritative mentions
Wrong image	Image sourced from incorrect page	Update Wikidata image; ensure preferred image on About page and social profiles
Wrong description	Description pulled from wrong source	Edit Wikidata description; ensure About page has clear entity description in first paragraph
Missing attributes	Incomplete structured data	Add properties to Schema.org markup and Wikidata entry
Wrong entity shown	Disambiguation failure	Strengthen unique signals; add qualifiers; resolve Wikidata disambiguation
Outdated info	Source data not updated	Update Wikidata, About page, and all profile pages

Wikidata Best Practices

Creating a Wikidata Entry

1. Check notability: Entity must have at least one authoritative reference
2. Create item: Add label, description, and aliases in relevant languages
3. Add statements: instance of, official website, social media links, founding date, founders, industry
4. Add identifiers: official website (P856), social media IDs, CrunchBase ID, ISNI, VIAF
5. Add references: Every statement should have a reference to an authoritative source

Important: Wikipedia's Conflict of Interest (COI) policy prohibits individuals and organizations from creating or editing articles about themselves. Instead of directly editing Wikipedia: (1) Focus on building notability through independent reliable sources (press coverage, industry publications, academic citations); (2) If you believe a Wikipedia article is warranted, consider engaging an independent Wikipedia editor through the Requested Articles process; (3) Ensure all claims about the entity are verifiable through third-party sources before any Wikipedia involvement.

Key Wikidata Properties by Entity Type

Property	Code	Person	Org	Brand	Product
instance of	P31	human	organization type	brand	product type
official website	P856	yes	yes	yes	yes
occupation / industry	P106/P452	yes	yes	—	—
founded by	P112	—	yes	yes	—
inception	P571	—	yes	yes	yes
country	P17	yes	yes	—	—
social media	various	yes	yes	yes	yes
employer	P108	yes	—	—	—
developer	P178	—	—	—	yes

AI Entity Optimization

How AI Systems Resolve Entities

User query → Entity extraction → Entity resolution → Knowledge retrieval → Answer generation

AI systems follow this pipeline:

1. Extract entity mentions from the query
2. Resolve each mention to a known entity (or fail → "I'm not sure")
3. Retrieve associated knowledge about the entity
4. Generate response citing sources that confirmed the entity's attributes

Signals AI Systems Use for Entity Resolution

Signal Type	What AI Checks	How to Optimize
Training data presence	Was entity in pre-training corpus?	Get mentioned in high-quality, widely-crawled sources
Retrieval augmentation	Does entity appear in live search results?	Strong SEO presence for branded queries
Structured data	Can entity be matched to Knowledge Graph?	Complete Wikidata + Schema.org
Contextual co-occurrence	What topics/entities appear alongside?	Build consistent topic associations across content
Source authority	Are sources about entity trustworthy?	Get mentioned by authoritative, well-known sources
Recency	Is information current?	Keep all entity profiles and content updated

Entity-Specific GEO Tactics

1. Define clearly: First paragraph of About page and key pages should define the entity in a way AI can quote directly
2. Be consistent: Use identical entity description across all platforms
3. Build associations: Create content that explicitly connects entity to target topics
4. Earn mentions: Third-party authoritative mentions are stronger entity signals than self-description
5. Stay current: Outdated entity information causes AI to lose confidence and stop citing

Reference Materials

Detailed guides for entity optimization:

• references/entity-signal-checklist.md — Complete signal checklist with verification methods
• references/knowledge-graph-guide.md — Wikidata, Wikipedia, and Knowledge Graph optimization playbook

Related Skills

• content-quality-auditor — CORE-EEAT items A07 (Knowledge Graph Presence) and A08 (Entity Consistency) directly relate
• domain-authority-auditor — CITE I01-I10 (Identity dimension) measures entity signals at domain level
• schema-markup-generator — Generate Organization, Person, Product, and other entity schema
• geo-content-optimizer — Entity signals feed AI citation probability
• competitor-analysis — Compare entity presence against competitors
• backlink-analyzer — Branded backlinks strengthen entity signals
• performance-reporter — Track branded search and Knowledge Panel metrics
• memory-management — Store entity audit results for tracking over time