Entity Optimizer

Build entity presence in Knowledge Graph, Wikidata, AI systems for brand recognition and citations. 实体优化/知识图谱

Security Scan

The skill's requirements and runtime instructions are coherent with its stated purpose (entity optimization for Knowledge Graphs/Wikidata/AI), and it is an instruction-only skill that requests no extra binaries, installs, or credentials — but it writes canonical entity profiles to agent memory and handles personal data, which you should review before use.

Purpose & Capability

Name, description, and the provided references all focus on building and auditing entity signals (Wikidata, Knowledge Graph, AI recognition). The skill does not request unrelated credentials or binaries and does not attempt to install external tooling, so its declared capabilities match what it asks for.

Instruction Scope

Runtime instructions read entity context (CLAUDE.md / State Model when available), audit signals, and write canonical profiles and handoff artifacts to agent memory paths (memory/entities/, memory/hot-cache.md, memory/open-loops.md). That behavior is expected for a profile-building skill, but it means the skill will create and persist structured records (including potentially personal data). The SKILL.md also includes guidance about querying AI systems and building Wikidata/Wikipedia entries; it correctly flags COI and includes a GDPR-lawful-basis prompt requirement for persons, which is a prudent but important operational requirement to enforce.

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing is written to disk by an installer and there are no external downloads. This minimizes installer-related risk.

Credentials

The skill declares no required environment variables, no credentials, and no config paths. Its data access is confined to agent memory paths and user-provided inputs, which is proportionate to an entity-audit/creation tool.

Persistence & Privilege

The skill asserts itself as the canonical writer for memory/entities/<name>.md and will persist entity profiles and open-loops into agent memory. It is not marked always:true, and it does not request system-wide privileges, but the ability to create or overwrite canonical profiles is a meaningful privilege — especially for profiles about people. Users should be comfortable with the skill autonomously writing such records and validate memory governance/policies.

Assessment

This skill appears to do what it claims and is low-risk from an install/credential perspective, but it will write canonical entity profiles into your agent's memory (memory/entities/, memory/hot-cache.md, memory/open-loops.md). Before installing or invoking: (1) confirm you trust the skill to author persistent entity records and decide whether those records may contain personal data; (2) enforce the GDPR prompt behavior for profiles of persons and have a process to review/approve created profiles before publishing externally; (3) back up any existing memory/entities/ files if you want to prevent accidental overwrites; and (4) if you plan to use it to edit Wikidata/Wikipedia, follow COI policies and prefer building independent notability rather than directly self-promoting content.

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 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 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. 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.

• Next handoff: 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.

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.

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.

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:

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 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 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.

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 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 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 for entity types with key signals, schemas, and disambiguation strategies by situation.

Knowledge Panel & Wikidata Optimization

Reference: See 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 — Complete signal checklist with verification methods, Step 3 report template, and Tips for Success
• references/knowledge-graph-guide.md — 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).