entity-optimizer by aaron-he-zhu/seo-geo-claude-skills
npx skills add https://github.com/aaron-he-zhu/seo-geo-claude-skills --skill entity-optimizerSEO& GEO Skills Library · 20 个 SEO + GEO 技能 · 一键安装所有:
npx skills add aaron-he-zhu/seo-geo-claude-skills
研究 · keyword-research · competitor-analysis · serp-analysis · content-gap-analysis
构建 · seo-content-writer · geo-content-optimizer · meta-tags-optimizer · schema-markup-generator
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跨领域 · content-quality-auditor · domain-authority-auditor · entity-optimizer · memory-management
审核、构建和维护跨搜索引擎和人工智能系统的实体身份。实体——搜索引擎和人工智能系统识别为独立事物的人、组织、产品和概念——是谷歌和大型语言模型决定 一个品牌是什么 以及 是否引用它 的基础。
为什么实体对 SEO + GEO 很重要:
Audit entity presence for [brand/person/organization]
How well do search engines and AI systems recognize [entity name]?
Build entity presence for [new brand] in the [industry] space
Establish [person name] as a recognized expert in [topic]
My Knowledge Panel shows incorrect information — fix entity signals for [entity]
AI systems confuse [my entity] with [other entity] — help me disambiguate
有关工具类别占位符,请参阅 CONNECTORS.md。
连接了 ~~knowledge graph + ~~SEO tool + ~~AI monitor + ~~brand monitor 时: 查询知识图谱 API 获取实体状态,从 ~~SEO tool 拉取品牌搜索数据,使用 ~~AI monitor 测试人工智能引用,使用 ~~brand monitor 跟踪品牌提及。
仅使用手动数据时: 要求用户提供:
在没有工具的情况下,Claude 会根据用户提供的信息提供实体优化策略和建议。用户必须运行搜索查询、检查知识面板并测试人工智能响应,以提供分析的原始数据。
使用公开搜索结果、人工智能查询测试和 SERP 分析进行审计。注意哪些项目需要工具访问才能进行全面评估。
当用户请求实体优化时:
确定实体在所有系统中的当前状态。
### 实体档案
**实体名称**:[name]
**实体类型**:[Person / Organization / Brand / Product / Creative Work / Event]
**主域名**:[URL]
**目标主题**:[topic 1, topic 2, topic 3]
#### 当前实体存在情况
| 平台 | 状态 | 详情 |
|----------|--------|---------|
| Google 知识面板 | ✅ 存在 / ❌ 缺失 / ⚠️ 不正确 | [details] |
| Wikidata | ✅ 已列出 / ❌ 未列出 | [QID if exists] |
| 维基百科 | ✅ 有文章 / ⚠️ 仅提及 / ❌ 缺失 | [notability assessment] |
| Google 知识图谱 API | ✅ 找到实体 / ❌ 未找到 | [entity ID, types, score] |
| 网站上的 Schema.org 标记 | ✅ 完整 / ⚠️ 部分 / ❌ 缺失 | [Organization/Person/Product schema] |
#### AI 实体解析测试
**注意**:在没有工具访问权限的情况下,Claude 无法直接查询其他人工智能系统或执行实时网络搜索。在没有 ~~AI monitor 或 ~~knowledge graph 工具的情况下运行时,请要求用户运行这些测试查询并报告结果,或使用用户提供的信息来评估实体存在情况。
通过查询以下内容来测试人工智能系统如何识别此实体:
- "What is [entity name]?"
- "Who founded [entity name]?" (for organizations)
- "What does [entity name] do?"
- "[entity name] vs [competitor]"
| AI 系统 | 识别实体? | 描述准确性 | 引用实体的内容? |
|-----------|-------------------|---------------------|------------------------|
| 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] |
评估 6 个类别的实体信号。有关包含验证方法的详细 47 项信号清单,请参阅 references/entity-signal-checklist.md。
将每个信号评估为通过 / 失败 / 部分,并为每个差距制定具体行动。这 6 个类别是:
参考:使用 references/entity-signal-checklist.md 中的审计模板,获取包含每个类别验证方法的完整 47 项信号清单。
## 实体优化报告
### 概述
- **实体**:[name]
- **实体类型**:[type]
- **审计日期**:[date]
### 信号类别摘要
| 类别 | 状态 | 关键发现 |
|----------|--------|-------------|
| 结构化数据 | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
| 知识库 | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
| 一致性 (NAP+E) | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
| 基于内容 | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
| 第三方 | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
| AI 特定 | ✅ 强 / ⚠️ 有差距 / ❌ 缺失 | [key findings] |
### 关键问题
[列出任何严重影响实体识别的问题——歧义问题、知识面板不正确、完全未出现在知识图谱中]
### 前 5 项优先行动
排序依据:对实体识别的影响 × 所需工作量
1. **[信号]** — [具体行动]
- 影响:[高/中] | 工作量:[低/中/高]
- 原因:[解释此操作如何改进实体识别]
2. **[信号]** — [具体行动]
- 影响:[高/中] | 工作量:[低/中/高]
- 原因:[解释]
3–5. [相同格式]
### 实体构建路线图
#### 第 1-2 周:基础(结构化数据 + 一致性)
- [ ] 实施/修复具有完整属性的 Organization 或 Person 模式
- [ ] 为所有权威资料添加 sameAs 链接
- [ ] 审计并修复所有平台上的 NAP+E 一致性
- [ ] 确保关于页面内容丰富且结构良好
#### 第 1 个月:知识库
- [ ] 创建或更新具有完整属性的 Wikidata 条目
- [ ] 确保 CrunchBase / 行业目录资料完整
- [ ] 建立维基百科知名度(或规划实现知名度的路径)
- [ ] 提交到相关权威目录
#### 第 2-3 个月:权威性建设
- [ ] 在权威行业网站上获得提及
- [ ] 与已建立的实体建立共引信号
- [ ] 创建强化实体-主题关联的主题内容集群
- [ ] 寻求能产生实体提及的公关机会
#### 持续进行:AI 特定优化
- [ ] 每季度测试 AI 实体解析
- [ ] 更新事实声明以保持时效性和可验证性
- [ ] 监控 AI 系统中的不正确实体信息
- [ ] 确保新内容能强化实体身份信号
### 交叉参考
- **CORE-EEAT 相关性**:项目 A07(知识图谱存在)和 A08(实体一致性)直接重叠——实体优化加强了权威性维度
- **CITE 相关性**:CITE I01-I10(身份维度)在域名级别衡量实体信号——实体优化有助于提高这些分数
- 如需内容级别审计:[content-quality-auditor](../content-quality-auditor/)
- 如需域名级别审计:[domain-authority-auditor](../domain-authority-auditor/)
参考:有关 B2B SaaS 公司(CloudMetrics)的完整实体审计报告示例,包括 AI 实体解析测试结果、实体健康摘要、前 3 项优先行动以及 CORE-EEAT/CITE 交叉参考,请参阅 references/example-audit-report.md。
参考:有关不同情境下按实体类型划分的关键信号、模式和歧义消除策略,请参阅 references/entity-type-reference.md。
参考:有关知识面板认领/编辑、常见问题及修复、Wikidata 条目创建、按实体类型划分的关键属性以及 AI 实体解析优化,请参阅 references/knowledge-panel-wikidata-guide.md。
实体优化的详细指南:
每周安装次数
633
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GitHub 星标数
352
首次出现
Feb 11, 2026
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SEO& GEO Skills Library · 20 skills for SEO + GEO · Install all:
npx skills add aaron-he-zhu/seo-geo-claude-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
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:
Audit entity presence for [brand/person/organization]
How well do search engines and AI systems recognize [entity name]?
Build entity presence for [new brand] in the [industry] space
Establish [person name] as a recognized expert in [topic]
My Knowledge Panel shows incorrect information — fix entity signals for [entity]
AI systems confuse [my entity] with [other entity] — help me disambiguate
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:
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.
When a user requests entity optimization:
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] |
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:
Reference : Use the audit template in references/entity-signal-checklist.md for the full 47-signal checklist with verification methods for each category.
## 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/)
Reference : See references/example-audit-report.md for a complete example entity audit report for a B2B SaaS company (CloudMetrics), including AI entity resolution test results, entity health summary, top 3 priority actions, and CORE-EEAT/CITE cross-references.
Reference : See references/entity-type-reference.md for entity types with key signals, schemas, and disambiguation strategies by situation.
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.
Detailed guides for entity optimization:
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39,200 周安装