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AEO/GEO Intelligence

AEO/GEO智能优化

When to Use This Skill

何时使用该技能

Activate this module when the user's request involves any of the following:

AI Visibility: Questions about how a brand, product, or person appears in AI-generated answers (ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Copilot, Gemini, Claude)
Answer Engine Optimization (AEO): Optimizing content so it gets selected as a source for AI-generated answers
Generative Engine Optimization (GEO): Structuring content and entities so generative AI platforms accurately represent a brand
Citation Tracking: Monitoring which sources AI models cite when answering queries related to a brand or industry
Entity Consistency: Ensuring brand information is uniform across all knowledge sources that AI models train on or retrieve from
Knowledge Graph Optimization: Improving how a brand is represented in Google Knowledge Graph, Wikidata, and other structured knowledge bases
Structured Data for AI: Implementing schema markup and structured data specifically to improve AI comprehension and citation likelihood

Trigger phrases: "AI visibility," "how does ChatGPT describe my brand," "Perplexity results," "AI Mode optimization," "AI Overview optimization," "answer engine," "generative engine," "LLM optimization," "AI citations," "entity consistency," "Knowledge Graph"

Google AI Mode (May 2026 — treat as a distinct surface): At Google I/O on 19 May 2026 AI Mode became the default search experience for opted-in users, crossed ~1B MAUs, and switched to Gemini 3.5 Flash as the base model. AI Mode is not the same as AI Overviews — it is a separate conversational tab with deeper reasoning, multi-turn follow-ups, and a citation pattern that frequently diverges from AI Overviews for the same query. Brands must audit AI Mode independently. Practical implication: an AEO program that only tests AI Overviews + ChatGPT + Perplexity now has a measurable blind spot.

当用户的请求涉及以下任一场景时，激活此模块：

AI可见性：关于品牌、产品或个人在AI生成答案（ChatGPT、Perplexity、Google AI Mode、Google AI概览、Copilot、Gemini、Claude）中的呈现方式的问题
答案引擎优化（AEO）：优化内容以使其被选为AI生成答案的来源
生成引擎优化（GEO）：构建内容和实体，使生成式AI平台能准确呈现品牌信息
引用追踪：监控AI模型在回答与品牌或行业相关的查询时引用的来源
实体一致性：确保品牌信息在AI模型训练或检索的所有知识源中保持统一
知识图谱优化：提升品牌在Google知识图谱、Wikidata及其他结构化知识库中的呈现效果
AI结构化数据：实施Schema标记和结构化数据，专门用于提升AI的理解能力和被引用的可能性

触发短语："AI可见性"、"ChatGPT如何描述我的品牌"、"Perplexity结果"、"AI Mode优化"、"AI概览优化"、"答案引擎"、"生成引擎"、"LLM优化"、"AI引用"、"实体一致性"、"知识图谱"

Google AI Mode（2026年5月——视为独立场景）：在2026年5月19日的Google I/O大会上，AI Mode成为选择加入用户的默认搜索体验，月活跃用户突破约10亿，并切换为以Gemini 3.5 Flash为基础模型。AI Mode 并非AI概览——它是一个独立的对话标签页，具备更深入的推理能力、多轮跟进功能，且其引用模式常与同一查询的AI概览有所不同。品牌必须独立审核AI Mode。实际影响：仅测试AI概览+ChatGPT+Perplexity的AEO项目存在可衡量的盲区。

Brand Context (Auto-Applied)

品牌上下文（自动应用）

Before producing any marketing output from this module:

Check session context — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.

If you need the full profile, read:

~/.claude-marketing/brands/{slug}/profile.json

Apply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choices
Check compliance — Auto-apply rules for brand's target_markets and industry using
```
skills/context-engine/compliance-rules.md
```
Reference industry benchmarks — Consult
```
skills/context-engine/industry-profiles.md
```
for the brand's industry
Use platform specs — Reference
```
skills/context-engine/platform-specs.md
```
for character limits and format requirements

Check campaign history — Run

python campaign-tracker.py --brand {slug} --action list-campaigns

before planning new work

If no brand exists, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
Check brand guidelines — If
```
~/.claude-marketing/brands/{slug}/guidelines/_manifest.json
```
exists, load and enforce:
```
restrictions.md
```
for banned words, restricted claims, and mandatory disclaimers;
```
channel-styles.md
```
for channel-specific tone overrides (may differ from base voice);
```
messaging.md
```
for approved key messages, taglines, and positioning language;
```
voice-and-tone.md
```
for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

在从此模块生成任何营销输出之前：

检查会话上下文——会话开始时已输出活跃品牌摘要。使用其中显示的品牌名称、行业、语气设置、渠道、目标、合规要求和竞争对手信息。

如需完整资料，请阅读：

~/.claude-marketing/brands/{slug}/profile.json

应用品牌语气——正式程度、活力、幽默感、权威性必须影响所有内容的语调和用词选择
检查合规性——使用
```
skills/context-engine/compliance-rules.md
```
自动应用品牌目标市场和行业的规则
参考行业基准——查阅
```
skills/context-engine/industry-profiles.md
```
获取品牌所在行业的相关信息
使用平台规范——参考
```
skills/context-engine/platform-specs.md
```
了解字符限制和格式要求

检查活动历史——在规划新工作前运行

python campaign-tracker.py --brand {slug} --action list-campaigns

若无品牌资料，请告知："未找到品牌资料。使用/digital-marketing-pro:brand-setup创建一个，或我可以按照通用最佳实践进行操作。"
检查品牌指南——若
```
~/.claude-marketing/brands/{slug}/guidelines/_manifest.json
```
存在，请加载并执行：
```
restrictions.md
```
中的禁用词汇、受限声明和强制性免责条款；
```
channel-styles.md
```
中的渠道特定语气覆盖规则（可能与基础语气不同）；
```
messaging.md
```
中的已批准关键信息、标语和定位语言；
```
voice-and-tone.md
```
中超出4项数值评分的详细语气规则。若为特定渠道生成内容，渠道风格规则优先于基础语气设置。

请勿向用户询问其品牌资料中已有的信息。

Required Context

必要上下文

Before executing AEO/GEO work, gather:

Brand Identity: Official brand name, key products/services, unique value propositions, and brand positioning
Current AI Footprint: Ask the user if they have tested how AI platforms currently describe their brand (or offer to audit)
Target Queries: The questions and topics the brand wants to be cited for in AI-generated answers
Existing Content Assets: Website URL, blog, knowledge base, Wikipedia presence, schema markup status
Competitive Landscape: Key competitors who may already have strong AI visibility
Industry Vertical: Needed to assess YMYL (Your Money Your Life) sensitivity and trust signal requirements

If the user cannot provide all context, proceed with what is available and flag gaps as recommendations.

Minimum viable context: Brand name and website URL. Everything else can be inferred or discovered during the audit process.

在执行AEO/GEO工作之前，需收集：

品牌标识：官方品牌名称、核心产品/服务、独特价值主张和品牌定位
当前AI足迹：询问用户是否已测试过AI平台当前对其品牌的描述（或主动提出审核）
目标查询：品牌希望在AI生成答案中被引用的问题和主题
现有内容资产：网站URL、博客、知识库、维基百科存在情况、Schema标记状态
竞争格局：可能已具备较强AI可见性的主要竞争对手
行业垂直领域：用于评估YMYL（你的金钱与生活）敏感性和信任信号要求

若用户无法提供所有上下文，可基于现有信息继续操作，并将缺失部分标记为建议。

最低可行上下文：品牌名称和网站URL。其他所有信息均可在审核过程中推断或发现。

Capabilities

功能

AI Visibility Audit: Systematic testing of how a brand appears across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Copilot for target queries
Citation Optimization: Restructuring content to maximize the probability of being cited as a source in AI-generated responses
Entity Consistency Audit: Cross-referencing brand information across Google Knowledge Graph, Wikidata, Wikipedia, Crunchbase, LinkedIn, and industry databases to identify inconsistencies
LLM Content Strategy: Creating content specifically designed to be ingested and accurately represented by language models
AI Answer Monitoring Framework: Setting up systematic tracking of AI mentions and citations over time
Structured Data for AI Citation: Implementing Organization, Product, FAQ, HowTo, and other schema types that improve AI comprehension
Knowledge Graph Optimization: Improving entity representation in structured knowledge bases
Topical Authority Mapping: Identifying content gaps that prevent a brand from being recognized as an authority by AI models
AI-First Content Formatting: Restructuring existing content with clear definitions, factual statements, and citation-worthy snippets
Competitive AI Visibility Benchmarking: Comparing brand AI presence against competitors across platforms

AI可见性审核：针对目标查询，系统测试品牌在ChatGPT、Perplexity、Google AI概览、Gemini和Copilot中的呈现情况
引用优化：重构内容以最大化被AI生成响应引用为来源的概率
实体一致性审核：跨Google知识图谱、Wikidata、维基百科、Crunchbase、LinkedIn和行业数据库交叉引用品牌信息，识别不一致之处
LLM内容策略：创建专门设计用于被语言模型摄取和准确呈现的内容
AI答案监控框架：建立系统的AI提及和引用跟踪机制
AI引用结构化数据：实施组织、产品、FAQ、HowTo等Schema类型，提升AI理解能力
知识图谱优化：提升结构化知识库中的实体呈现效果
主题权威映射：识别阻碍品牌被AI模型认可为权威的内容缺口
AI优先内容格式化：通过清晰的定义、事实陈述和值得引用的片段重构现有内容
竞争AI可见性基准测试：跨平台比较品牌与竞争对手的AI存在情况

Process

流程

Primary Workflow: AI Visibility Audit & Optimization

Discovery & Baseline
- Collect brand details, target queries (10-25 queries), and competitor list
- Document current schema markup, Knowledge Graph presence, and Wikipedia/Wikidata status
- Identify the business model to determine which AI platforms matter most
- Catalog existing authoritative content assets (whitepapers, research, data, expert bios)
- Assess YMYL classification — brands in health, finance, or legal face higher authority thresholds
AI Platform Testing
- For each target query, document how the brand appears (or fails to appear) on:
  - Google AI Mode (default conversational surface, Gemini 3.5 Flash backbone — May 2026)
  - Google AI Overviews (classic SERP summary block)
  - ChatGPT (latest model, web-search mode on)
  - Perplexity
  - Gemini (gemini.google.com)
  - Microsoft Copilot
- Score each result: Cited (direct mention with link), Referenced (mentioned without link), Absent, Misrepresented
- Capture exact AI-generated text for each query as a baseline
Entity Consistency Check
- Audit brand name, founding date, leadership, product descriptions, and key claims across all knowledge sources
- Flag inconsistencies between sources (e.g., different founding years on Crunchbase vs. Wikipedia)
- Prioritize fixes by source authority weight
Gap Analysis & Strategy
- Identify patterns: Which query types yield citations? Which don't?
- Map content gaps: What authoritative content is missing that AI models need?
- Assess structured data gaps: What schema markup is missing or incorrect?
- Benchmark against competitors who ARE getting cited
Optimization Execution Plan
- Prioritized list of content to create or restructure
- Schema markup implementation plan
- Knowledge Graph correction/enhancement steps
- Entity consistency fix checklist
- Content formatting guidelines for AI-first optimization
Monitoring & Iteration
- Define monitoring cadence (weekly for priority queries, monthly for full audit)
- Set up tracking framework to detect citation changes
- Establish KPIs: citation rate, accuracy score, query coverage percentage
- Track competitor citation changes as part of ongoing monitoring
- Re-test after major content updates or schema implementations to measure impact
- Log all AI platform model updates that may affect visibility (new model releases, retrieval changes)

Secondary Workflow: Citation-Optimized Content Creation

Identify a target query cluster where the brand should be cited but currently is not
Analyze what sources ARE being cited for those queries — study their content structure, authority signals, and formatting
Create or restructure content that surpasses cited sources in:
- Factual accuracy and specificity (include precise data, dates, numbers)
- Clear definitional statements (AI models favor content with unambiguous definitions)
- Structured formatting (clear headings, bullet points, tables that AI can parse)
- Source credibility signals (author credentials, citations to primary research, organizational authority)
Implement supporting schema markup (FAQ, HowTo, Article, Organization as appropriate)
Build inbound authority signals (internal links from high-authority pages, external citations)
Re-test AI platform responses 2-4 weeks after publication to measure citation pickup

主要工作流：AI可见性审核与优化

发现与基准建立
- 收集品牌详情、目标查询（10-25个）和竞争对手列表
- 记录当前Schema标记、知识图谱存在情况以及维基百科/Wikidata状态
- 确定商业模式，以判断哪些AI平台最为重要
- 分类现有权威内容资产（白皮书、研究报告、数据、专家简历）
- 评估YMYL分类——健康、金融或法律领域的品牌面临更高的权威门槛
AI平台测试
- 针对每个目标查询，记录品牌在以下平台的呈现情况（或未呈现情况）：
  - Google AI Mode（默认对话场景，基于Gemini 3.5 Flash——2026年5月）
  - Google AI概览（经典SERP摘要模块）
  - ChatGPT（最新模型，开启网页搜索模式）
  - Perplexity
  - Gemini（gemini.google.com）
  - Microsoft Copilot
- 为每个结果评分：已引用（直接提及并附带链接）、已参考（提及但无链接）、未出现、呈现错误
- 捕获每个查询的AI生成文本作为基准
实体一致性检查
- 审核所有知识源中的品牌名称、成立日期、领导层、产品描述和关键声明
- 标记源之间的不一致之处（例如，Crunchbase与维基百科上的成立年份不同）
- 根据源的权威权重优先处理修复工作
缺口分析与策略制定
- 识别模式：哪些类型的查询能获得引用？哪些不能？
- 映射内容缺口：AI模型需要但缺失的权威内容是什么？
- 评估结构化数据缺口：哪些Schema标记缺失或不正确？
- 与已获得引用的竞争对手进行基准对比
优化执行计划
- 待创建或重构内容的优先级列表
- Schema标记实施计划
- 知识图谱修正/增强步骤
- 实体一致性修复清单
- AI优先优化的内容格式指南
监控与迭代
- 定义监控节奏（优先级查询每周一次，全面审核每月一次）
- 建立跟踪框架以检测引用变化
- 设定KPI：引用率、准确性得分、查询覆盖百分比
- 将竞争对手的引用变化纳入持续监控范围
- 在重大内容更新或Schema实施后重新测试，衡量影响
- 记录所有可能影响可见性的AI平台模型更新（新模型发布、检索方式变化）

次要工作流：引用优化内容创建

确定品牌应被引用但目前未被引用的目标查询集群
分析这些查询中当前被引用的来源——研究其内容结构、权威信号和格式
创建或重构内容，在以下方面超越被引用的来源：
- 事实准确性和特异性（包含精确数据、日期、数字）
- 清晰的定义性陈述（AI模型偏好明确无歧义的内容）
- 结构化格式（AI可解析的清晰标题、项目符号、表格）
- 来源可信度信号（作者资质、对原始研究的引用、组织权威性）
实施配套的Schema标记（根据情况使用FAQ、HowTo、Article、Organization等类型）
构建入站权威信号（来自高权威页面的内部链接、外部引用）
发布后2-4周重新测试AI平台响应，衡量引用获取情况

Reference Files

参考文件

```
ai-visibility-audit.md
```
— Step-by-step audit methodology, scoring rubric, and platform-specific testing protocols
```
citation-optimization.md
```
— Content restructuring techniques, citation-worthy formatting patterns, and source authority building
```
entity-consistency.md
```
— Cross-platform entity audit checklist, Knowledge Graph optimization, Wikidata editing guidelines
```
llm-content-strategy.md
```
— AI-first content creation framework, topical authority mapping, and structured data implementation guide

```
ai-visibility-audit.md
```
—— 分步审核方法、评分规则和平台特定测试协议
```
citation-optimization.md
```
—— 内容重构技巧、值得引用的格式模式和来源权威构建方法
```
entity-consistency.md
```
—— 跨平台实体审核清单、知识图谱优化、Wikidata编辑指南
```
llm-content-strategy.md
```
—— AI优先内容创建框架、主题权威映射和结构化数据实施指南

Output Formats

输出格式

Deliverable	Format	Description
AI Visibility Scorecard	Table/Spreadsheet	Query-by-query visibility scores across all AI platforms
Entity Consistency Report	Document	All inconsistencies found with correction instructions
AEO Content Brief	Document	Content creation/restructuring briefs optimized for AI citation
Schema Markup Spec	Code snippets (JSON-LD)	Ready-to-implement structured data markup
Monitoring Dashboard Spec	Document	KPIs, tracking methodology, and reporting cadence
Competitive AI Visibility Matrix	Table	Side-by-side comparison of brand vs. competitor AI visibility
LLM Content Strategy	Document	90-day content plan focused on building AI authority

交付物	格式	描述
AI可见性评分卡	表格/电子表格	所有AI平台上按查询划分的可见性得分
实体一致性报告	文档	发现的所有不一致之处及修正说明
AEO内容简报	文档	针对AI引用优化的内容创建/重构简报
Schema标记规范	代码片段（JSON-LD）	可直接实施的结构化数据标记
监控仪表板规范	文档	KPI、跟踪方法和报告节奏
竞争AI可见性矩阵	表格	品牌与竞争对手AI可见性的并排对比
LLM内容策略	文档	专注于构建AI权威性的90天内容计划

Edge Cases

边缘案例

Brand with Negative AI Perception

品牌存在负面AI认知

Situation: AI platforms are generating inaccurate or negative information about the brand
Approach: Prioritize entity consistency fixes and authoritative source correction before any content optimization. Create factual correction content on high-authority owned properties. Do NOT attempt to manipulate AI outputs directly — focus on fixing the underlying source material. Flag potential reputation management needs to the user.

场景：AI平台生成关于品牌的不准确或负面信息
方法：在进行任何内容优化之前，优先处理实体一致性修复和权威来源更正。在高权威自有资产上创建事实更正内容。请勿尝试直接操纵AI输出——专注于修复底层源材料。向用户标记潜在的声誉管理需求。

New Brand with Zero AI Visibility

新品牌无AI可见性

Situation: Brand does not appear in any AI-generated answers
Approach: Start with foundation-building — create a Wikipedia-worthy web presence (not necessarily Wikipedia itself), establish Wikidata entry, implement comprehensive schema markup, and build topical authority content. Set realistic timelines: AI model knowledge has lag times (weeks to months depending on platform).

场景：品牌未出现在任何AI生成答案中
方法：从基础建设开始——创建具备维基百科水准的网络存在（不一定是维基百科本身），建立Wikidata条目，实施全面的Schema标记，并构建主题权威内容。设定现实的时间表：AI模型的知识存在滞后性（根据平台不同，可能需要数周至数月）。

Common-Word Brand Names

通用词品牌名称

Situation: Brand name is a common word (e.g., "Apple," "Slack," "Monday")
Approach: Entity disambiguation is critical. Emphasize co-occurring terms, use full official names in structured data, ensure Knowledge Graph correctly disambiguates, and optimize content with entity-clarifying context. Always include industry/product qualifiers in target queries.

场景：品牌名称是通用词（例如："Apple"、"Slack"、"Monday"）
方法：实体消歧至关重要。强调共现术语，在结构化数据中使用完整官方名称，确保知识图谱正确消歧，并使用实体澄清上下文优化内容。在目标查询中始终包含行业/产品限定词。

Multi-Brand Companies

多品牌公司

Situation: Parent company with multiple sub-brands needing separate AI identities
Approach: Audit each brand entity separately. Ensure clear parent-child relationships in structured data. Avoid cannibalization where sub-brands compete with each other in AI answers. Create distinct topical authority for each brand.

场景：母公司拥有多个子品牌，需要独立的AI身份
方法：单独审核每个品牌实体。确保结构化数据中存在清晰的父子关系。避免子品牌在AI答案中相互竞争的自我蚕食情况。为每个品牌创建独特的主题权威。

Regional AI Engines (Baidu, Yandex)

区域AI引擎（百度、Yandex）

Situation: User needs visibility on non-Western AI platforms
Approach: Acknowledge that optimization strategies differ significantly for Baidu (China) and Yandex (Russia). These require localized content, platform-specific structured data standards, and different knowledge bases. Recommend specialized regional expertise if the request goes deep. Provide general framework but flag limitations in specific platform knowledge.

场景：用户需要在非西方AI平台上获得可见性
方法：明确针对百度（中国）和Yandex（俄罗斯）的优化策略差异显著。这些平台需要本地化内容、平台特定的结构化数据标准和不同的知识库。如果请求深入，建议寻求专业的区域专家支持。提供通用框架，但标记特定平台知识的局限性。

YMYL Brands (Health, Finance, Legal)

YMYL品牌（健康、金融、法律）

Situation: Brands in Your Money Your Life categories face elevated trust requirements from AI platforms
Approach: AI platforms apply stricter source quality thresholds for YMYL topics. Prioritize: (1) Expert authorship with verifiable credentials on all content. (2) Citations to primary research, government sources, and peer-reviewed studies. (3) Medical/legal/financial review disclosures. (4) Comprehensive E-E-A-T signals (link to Digital PR module for authority building). (5) Schema markup that explicitly declares author qualifications and organizational credentials. Test AI outputs carefully for accuracy — misrepresentation in YMYL categories carries higher reputational risk.

场景：属于"你的金钱与生活"类别的品牌面临AI平台更高的信任要求
方法：AI平台对YMYL主题应用更严格的源质量阈值。优先处理：(1) 所有内容均由具备可验证资质的专家撰写。(2) 引用原始研究、政府来源和同行评审研究。(3) 医疗/法律/金融审核披露。(4) 全面的E-E-A-T信号（链接至数字PR模块以构建权威性）。(5) 明确声明作者资质和组织认证的Schema标记。仔细测试AI输出的准确性——YMYL类别中的错误呈现会带来更高的声誉风险。

Rapidly Evolving AI Landscape

快速演变的AI格局

Situation: AI platforms frequently update their models, retrieval methods, and citation behavior
Approach: Treat all AEO/GEO strategies as living processes, not one-time optimizations. Build monitoring into every engagement. When a major platform update occurs (new model release, retrieval system change, AI Overview format change), re-run the visibility audit for priority queries. Document observed behavior changes and update the workflow accordingly. Maintain a changelog of platform updates and their observed impact on brand visibility.

场景：AI平台频繁更新其模型、检索方法和引用行为
方法：将所有AEO/GEO策略视为动态流程，而非一次性优化。将监控纳入每个项目。当发生重大平台更新（新模型发布、检索系统变更、AI概览格式变更）时，针对优先级查询重新运行可见性审核。记录观察到的行为变化并相应更新工作流。维护平台更新及其对品牌可见性影响的变更日志。