entity-seo-playbook
关于
This Claude Skill helps developers optimize for AI search visibility by building cross-platform authority for people, organizations, or products. It provides a playbook to audit and establish entities as recognized sources that AI models will cite, moving beyond traditional page SEO. Use it when you need to ensure experts or brands outperform generic pages in AI search results.
快速安装
Claude Code
推荐npx skills add guia-matthieu/clawfu-skills -a claude-code/plugin add https://github.com/guia-matthieu/clawfu-skillsgit clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/entity-seo-playbook在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Entity SEO Playbook
Build entity authority so AI search surfaces your people, brand, and expertise — not just your pages. Based on Lily Ray's entity-first framework.
When to Use This Skill
Use this skill when you need to:
- Establish an expert or brand as a recognized entity in AI search results
- Audit entity presence across platforms (Google Knowledge Panel, Wikidata, LinkedIn, etc.)
- Optimize for AI search where entities outperform keyword-optimized pages
- Build cross-platform authority for a person, product, or organization
- Improve citation probability across ChatGPT, Perplexity, Google AI Overviews
- Develop a digital PR strategy centered on entity recognition
This skill is particularly valuable for:
- Consultants and experts building personal brand visibility
- B2B companies where thought leaders drive trust
- Publishers optimizing for AI-era traffic
- Anyone whose Google Knowledge Panel is missing or incomplete
Methodology Foundation
Source Expert: Lily Ray (VP of SEO Strategy, Amsive Digital; columnist, Search Engine Land)
Core Thesis: Entity-first replaces website-first. AI search models surface entities — people, organizations, concepts — not URLs. Individual experts with cross-platform digital footprints get disproportionately cited by AI.
"The shift isn't from SEO to GEO. It's from page-first to entity-first. AI models don't rank pages — they recognize entities and assess their authority across the entire web." — Lily Ray, The Search Session (Advanced Web Ranking)
Supporting Research:
- Google Search ranking code leak (2024) — site quality score correlates with brand search volume
- Mark Williams-Cook's signal decay thesis — link graphs and click data are eroding, entity signals are strengthening
- Metehan Yeşilyurt's AI bot analysis — different AI products retrieve entity information differently
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Audits entity presence across platforms | Which entities to prioritize |
| Identifies gaps in entity establishment | Content and PR strategy budget |
| Generates structured data recommendations | Which platforms to invest in |
| Creates entity-building content plans | Expert credentials and experience to highlight |
| Maps entity relationships | Strategic positioning decisions |
What This Skill Does
When invoked, I will guide you through:
- Entity Audit — Assess current entity recognition across AI and search
- Entity Establishment — Create or strengthen entity signals
- Cross-Platform Authority — Build consistent entity presence
- Content Strategy — Create entity-reinforcing content
- Measurement — Track entity visibility in AI search
Instructions
Phase 1: Entity Audit
Before building, assess where you stand.
Entity Recognition Checklist
GOOGLE KNOWLEDGE PANEL
[ ] Does the entity have a Knowledge Panel?
[ ] Is the information accurate and complete?
[ ] Are notable works/credentials listed?
[ ] Is the entity connected to related entities?
WIKIDATA / WIKIPEDIA
[ ] Does a Wikidata entry exist? (Q-number)
[ ] Is the entity described with proper claims and references?
[ ] Does a Wikipedia article exist? (if notable enough)
SCHEMA MARKUP
[ ] Is Person/Organization schema present on owned sites?
[ ] Does schema include sameAs links to official profiles?
[ ] Is author markup on all published content?
AI SEARCH TEST
[ ] Ask ChatGPT: "Who is [entity]?" — does it know them?
[ ] Ask Perplexity: "[entity] expertise" — what sources cited?
[ ] Ask Google AI Overview: "[entity] + topic" — are they mentioned?
CROSS-PLATFORM PRESENCE
[ ] LinkedIn profile complete and active?
[ ] Guest appearances on podcasts/webinars?
[ ] Published articles on third-party sites?
[ ] Conference speaking engagements listed?
[ ] Professional directory listings?
Entity Authority Score (Informal)
| Level | Description | Signals |
|---|---|---|
| 0 — Unknown | AI doesn't recognize the entity | No Knowledge Panel, no AI mentions |
| 1 — Emerging | AI has partial information | Some mentions, no consistent identity |
| 2 — Established | AI recognizes and can describe | Knowledge Panel exists, AI answers are accurate |
| 3 — Authoritative | AI cites as a source | Consistently referenced in AI responses on topic |
| 4 — Definitive | AI uses as primary reference | Top-of-mind entity for the topic domain |
Phase 2: Entity Establishment
For People (Experts, Consultants, Authors)
Minimum viable entity:
- Consistent name across all platforms (exact same format)
- One authoritative bio page on your own site with Person schema
- LinkedIn profile matching the bio exactly
- Wikidata entry with claims and references (if notable)
- Google Knowledge Panel claimed via Google Search verification
- Author bylines on published content with author schema
Entity-reinforcing actions:
- Publish on recognized third-party platforms (not just your blog)
- Get quoted as an expert in news articles (digital PR)
- Appear on podcasts — AI models digest audio transcripts
- Speak at conferences listed on structured event sites
- Create a consistent "About" statement used everywhere
For Organizations (Companies, Brands)
Minimum viable entity:
- Google Business Profile complete and verified
- Organization schema on website with sameAs to all official profiles
- Wikidata entry with proper organizational claims
- Consistent NAP (Name, Address, Phone) across directories
- Brand search volume — actively build brand awareness outside search
For Products
Minimum viable entity:
- Product schema on product pages
- Reviews on authoritative sites — AI search heavily weights product reviews from trusted publishers
- Comparison content mentioning the product alongside known competitors
- Third-party validation — awards, certifications, expert reviews
"Product review pages on authoritative publishers are MORE valuable now than before AI search. AI Overviews synthesize reviews — if you're not in the review set, you don't exist." — Lily Ray
Phase 3: Cross-Platform Authority
AI models don't just crawl your website. They learn from the entire web. Cross-platform content is structural, not optional.
Platform Priority Matrix
| Platform | Entity Signal Strength | Why |
|---|---|---|
| High | Professional identity, endorsements, content | |
| Podcast appearances | High | AI transcription creates entity co-occurrence |
| Guest articles | High | Third-party validation of expertise |
| Conference talks | Medium-High | Listed on event sites, referenced in recaps |
| YouTube | Medium | Transcripts + visual identity |
| Twitter/X | Medium | Real-time expertise signals |
| Reddit (as expert) | Medium | Cited by Perplexity frequently |
| Professional directories | Low-Medium | Structured entity data |
The Cross-Platform Consistency Rule
Every platform should reinforce the same entity identity:
- Same name format (don't be "John Smith" on LinkedIn and "J. Smith PhD" on your site)
- Same expertise description (consistent topic association)
- Same profile photo (visual entity recognition)
- Cross-linking — every profile links to the canonical entity page
Phase 4: Content Strategy for Entity Authority
Entity-Reinforcing Content Types
| Content Type | Entity Signal | Example |
|---|---|---|
| Original research | Strongest — unique data creates citation necessity | "Our analysis of 10,000 campaigns shows..." |
| Expert interviews | Strong — entity co-occurrence with recognized experts | "I spoke with [Expert] about..." |
| Case studies | Strong — first-hand experience signal | "Here's how we achieved X for [Client]..." |
| Conference recaps | Medium — positions entity in professional context | "At [Conference], the key insight was..." |
| Contrarian analysis | Medium — differentiation from consensus | "The conventional wisdom is wrong because..." |
| How-to guides | Low-Medium — helpful but interchangeable | Commodity content unless paired with unique data |
Favicon & Visual Identity in AI Results
Google AI Mode, AI Overviews, and Google Discover all display favicons prominently. Your favicon is the new meta description — it's the visual identifier in AI-generated results.
Actions:
- Ensure favicon is distinctive at 16x16px and 32x32px
- Use your brand mark, not a generic icon
- Test favicon visibility against competitors in search results
- Consider favicon as brand recognition driver (users scan visually)
Phase 5: Measurement
Entity Visibility Tracking
| Metric | How to Track | Frequency |
|---|---|---|
| Brand search volume | Google Search Console, Google Trends | Monthly |
| Knowledge Panel accuracy | Manual check | Quarterly |
| AI mention test | Ask ChatGPT/Perplexity/Claude about entity | Monthly |
| Citation in AI Overviews | Search "[topic]" and check AI Overview sources | Weekly |
| Cross-platform consistency | Manual audit of all profiles | Quarterly |
| Branded traffic | Analytics — filter for brand terms | Monthly |
Leading Indicators
- Increasing brand search volume = entity recognition growing
- More Knowledge Panel attributes appearing = entity understanding deepening
- AI responses becoming more accurate about entity = training data inclusion
- Third-party mentions increasing = entity authority compounding
Examples
Example 1: Consultant Building Personal Entity
Context: SEO consultant wants AI search to recognize them as an expert on GEO.
Entity Audit Results:
- No Knowledge Panel
- LinkedIn complete but not active
- 3 blog posts on own site
- ChatGPT doesn't know them
Action Plan:
- Create Person schema on About page with sameAs to LinkedIn, Twitter
- Publish 2 guest articles on Search Engine Land or Search Engine Journal
- Appear on 3 SEO podcasts as guest expert
- Create Wikidata entry with references to published work
- Write original research: "Analysis of 500 AI Overview citations in [niche]"
- Post weekly LinkedIn analysis (build content entity co-occurrence)
Expected Timeline: 3-6 months to reach Level 2 (Established)
Example 2: B2B SaaS Building Product Entity
Context: Marketing analytics tool wants AI to recommend them when users ask about marketing attribution.
Entity Audit Results:
- Organization schema exists but incomplete
- No product reviews on authoritative sites
- ChatGPT mentions 3 competitors but not them
- Brand search volume: 200/month (competitors: 5,000+)
Action Plan:
- Complete Organization schema with sameAs, foundingDate, founders
- Pitch product review to G2, Capterra + 2 industry publications
- Create comparison content: "[Product] vs [Competitor A] vs [Competitor B]"
- Launch original research: "State of Marketing Attribution 2026"
- Get CEO on 5 industry podcasts discussing attribution methodology
- Build brand awareness: sponsor 2 niche conferences, run LinkedIn ads
Expected Timeline: 6-12 months to appear in AI recommendations
Checklists & Templates
Entity Establishment Checklist
PHASE 1: FOUNDATION (Week 1-2)
[ ] Canonical entity page created on owned site
[ ] Person/Organization schema deployed
[ ] All platform profiles consistent (name, photo, description)
[ ] sameAs links connecting all profiles
[ ] Wikidata entry created (if applicable)
PHASE 2: THIRD-PARTY SIGNALS (Month 1-3)
[ ] 2+ guest articles published on authoritative sites
[ ] 3+ podcast appearances completed
[ ] Professional directory listings updated
[ ] Expert quotes obtained in news/industry articles
PHASE 3: CONTENT AUTHORITY (Month 2-6)
[ ] Original research published
[ ] 5+ entity-reinforcing content pieces live
[ ] Cross-platform content calendar running
[ ] Case studies with named clients published
PHASE 4: MEASUREMENT (Ongoing)
[ ] Monthly AI mention test
[ ] Brand search volume tracked
[ ] Knowledge Panel monitored
[ ] Citation tracking in AI Overviews
Skill Boundaries
What This Skill Does Well
- Auditing entity presence across platforms and AI search
- Creating structured plans for entity establishment
- Identifying gaps in cross-platform authority
- Recommending content strategies that build entity signals
What This Skill Cannot Do
- Create Wikipedia articles (notability requirements are external)
- Guarantee Knowledge Panel creation (Google's decision)
- Access live AI search results to test citations
- Replace genuine expertise (entity signals must reflect real authority)
References
Primary Sources:
- Lily Ray — "How Publishers Can Survive in the Age of AI Search" (The Search Session, Advanced Web Ranking)
- Mark Williams-Cook — "From People Also Ask to AI Search" (The Search Session)
- Google Search ranking code leak analysis (2024) — site quality score
Related Concepts:
- E-E-A-T (Google Search Quality Rater Guidelines)
- Knowledge Graph Optimization
- Digital PR for entity authority
- Schema.org structured data (Person, Organization, Product)
Related Skills
- llm-optimized-content — Content optimization for AI citations
- schema-markup — Implementing JSON-LD structured data
- seo-content-writer — SEO content with E-E-A-T compliance
- brand-voice-learner — Establishing consistent brand identity
- skyscraper-technique — Building linkable authority content
Skill Metadata
name: entity-seo-playbook
category: seo-tools
subcategory: geo
version: 1.0.0
author: GUIA
source_expert: Lily Ray (VP SEO Strategy, Amsive Digital) — The Search Session podcast
difficulty: intermediate
mode: centaur
tags: [entity-seo, geo, ai-search, knowledge-graph, cross-platform, digital-pr, e-e-a-t, lily-ray]
created: 2026-02-10
updated: 2026-02-10
GitHub 仓库
相关推荐技能
content-collections
元Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。
polymarket
元这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。
creating-opencode-plugins
元该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。
sglang
元SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。
