entity-optimizer▌
aaron-he-zhu/seo-geo-claude-skills · updated Apr 8, 2026
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Audit and strengthen entity recognition across search engines and AI systems.
- ›Evaluates entity presence across Google Knowledge Graph, Wikidata, Wikipedia, and AI systems with a 47-signal checklist covering structured data, knowledge bases, consistency, content authority, third-party mentions, and AI-specific signals
- ›Identifies gaps in entity identity and creates prioritized action plans with specific, timeframed steps for establishing or fixing brand, person, product, or organization e
Entity Optimizer
SEO & GEO Skills Library · 20 skills for SEO + GEO · ClawHub · skills.sh System Mode: This cross-cutting skill is part of the protocol layer and follows the shared Skill Contract and State Model.
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.
System role: Canonical Entity Profile. It acts as the source of truth for entity identity, associations, and disambiguation across the library.
When This Must Trigger
Use this when brand or entity identity needs to be established or verified — even if the user doesn't use entity terminology:
- User says "Google doesn't know my brand" or "no knowledge panel"
- Auto-recommended when
memory/entities/candidates.mdaccumulates 3 or more uncanonized entity candidates from other skills - 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
- Entity Audit: Evaluates current entity presence across search and AI systems
- Knowledge Graph Analysis: Checks Google Knowledge Graph, Wikidata, and Wikipedia status
- AI Entity Resolution Test: Queries AI systems to see how they identify and describe the entity
- Entity Signal Mapping: Identifies all signals that establish entity identity
- Gap Analysis: Finds missing or weak entity signals
- Entity Building Plan: Creates actionable plan to establish or strengthen entity presence
- Disambiguation Strategy: Resolves confusion with similarly-named entities
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
CLAUDE.md,memory/entities/, andmemory/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.
- Next handoff: use the
Next Best Skillbelow once the entity truth is clear.
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:
- Entity name, type (Person, Organization, Brand, Product, Creative Work, Event)
- Primary website / domain
- Known existing profiles (Wikipedia, Wikidata, social media, industry directories)
- Top 3-5 topics/industries the entity should be associated with
- 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.
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 references/entity-signal-checklist.md for the full 47-signal checklist with verification methods for each category.
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] |
How to use entity-optimizer on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add entity-optimizer
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches entity-optimizer from GitHub repository aaron-he-zhu/seo-geo-claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate entity-optimizer. Access the skill through slash commands (e.g., /entity-optimizer) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★28 reviews- ★★★★★Chaitanya Patil· Dec 12, 2024
Solid pick for teams standardizing on skills: entity-optimizer is focused, and the summary matches what you get after install.
- ★★★★★Noah Lopez· Dec 4, 2024
Solid pick for teams standardizing on skills: entity-optimizer is focused, and the summary matches what you get after install.
- ★★★★★Meera Li· Nov 23, 2024
We added entity-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 3, 2024
We added entity-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Shikha Mishra· Oct 22, 2024
entity-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mateo Farah· Oct 14, 2024
entity-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Gonzalez· Sep 21, 2024
Solid pick for teams standardizing on skills: entity-optimizer is focused, and the summary matches what you get after install.
- ★★★★★Yash Thakker· Sep 13, 2024
I recommend entity-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Daniel Diallo· Sep 5, 2024
I recommend entity-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Carlos Nasser· Sep 1, 2024
entity-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
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