{"id":"aeo-geo","name":"aeo-geo","summary":"Answer Engine / Generative Engine Optimizationの戦略モジュール — AIの可視性監査、引用のためのコンテンツ再構築、Knowledge Graph、Wikidata、Wikipedia、Crunchbase、LinkedInでのエンティティ整合性チェックを実行し、JSON…","body":"# AEO/GEO Intelligence\n\n## When to Use This Skill\n\nActivate this module when the user's request involves any of the following:\n\n- **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)\n- **Answer Engine Optimization (AEO)**: Optimizing content so it gets selected as a source for AI-generated answers\n- **Generative Engine Optimization (GEO)**: Structuring content and entities so generative AI platforms accurately represent a brand\n- **Citation Tracking**: Monitoring which sources AI models cite when answering queries related to a brand or industry\n- **Entity Consistency**: Ensuring brand information is uniform across all knowledge sources that AI models train on or retrieve from\n- **Knowledge Graph Optimization**: Improving how a brand is represented in Google Knowledge Graph, Wikidata, and other structured knowledge bases\n- **Structured Data for AI**: Implementing schema markup and structured data specifically to improve AI comprehension and citation likelihood\n\n**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\"\n\n**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.\n\n**Additional I/O 2026 announcements that change AEO scope** ([source: blog.google/products-and-platforms/products/search/search-io-2026](https://blog.google/products-and-platforms/products/search/search-io-2026/)):\n\n- **AI Overview → AI Mode follow-up flow** is live worldwide (desktop + mobile) — users can ask a follow-up directly from an AI Overview and flow into a conversational AI Mode session. AEO implication: the *first* impression in an AI Overview is now also a gateway to multi-turn citation. Optimize for being the foundational citation, not just the brief snippet.\n- **Personal Intelligence in AI Mode** is expanding to ~200 countries and 98 languages, no subscription required, with Gmail / Photos / Calendar connections. AEO implication: AI answers are increasingly personalized — generic brand-search results will be reweighted against the user's own context. Brand schema completeness and entity consistency (NAP, services, hours) matter even more.\n- **AI Information Agents** (user-created, monitoring blogs/news/social 24/7) launch for AI Pro & Ultra subscribers in summer 2026. AEO implication: brands that publish structured, dated updates on owned channels will be more legible to user-configured agents than those relying on third-party PR pickup.\n\n**Official Google guidance on AI search optimization** (updated 15 May 2026 — [Google AI Optimization Guide](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)):\n\n- **No `llms.txt` file is needed.** Google's official position: \"You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search.\" Do not waste time generating `llms.txt` for Google AI Features. (Other AI search engines may or may not consume it; current Anthropic / OpenAI / Perplexity public positions are also that they do not require it. Document any client pressure to ship `llms.txt` as a low-priority deliverable with no measurable upside.)\n- **No special AI-specific schema is needed.** \"Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add.\" Schema continues to matter for classic SEO and rich results.\n- **Eligibility is standard Search.** \"To be eligible to be shown in generative AI features on Google Search, a page must be indexed and eligible to be shown in Google Search with a snippet, fulfilling the Search technical requirements.\"\n\n**Opt-out and AI training controls** ([Google AI Features doc](https://developers.google.com/search/docs/appearance/ai-features)):\n\n- For AI Overviews and AI Mode (inside Google Search): use existing snippet directives — `nosnippet`, `data-nosnippet`, `max-snippet`, `noindex`. Robots.txt for Googlebot is the canonical control. **There is no AI-specific robots/meta directive.**\n- For Google's *other* AI systems (Gemini app training, Vertex AI grounding outside Search): use the **Google-Extended** user agent in robots.txt. This is a distinct control from Googlebot.\n- **NEW (3 June 2026):** Search Console now ships an **opt-out toggle** at the property level — flip it to exclude the site from grounding AI Overviews / AI Mode responses without editing robots.txt. See `/digital-marketing-pro:gsc-ai-performance` for the decision framework on when to use it.\n\n**EU AI Act Article 50 (applicable 2 August 2026)** — for AI-generated marketing content surfaced in EU markets, see `skills/context-engine/eu-code-of-practice.md` for the voluntary Code of Practice (WG1 providers / WG2 deployers) and the C2PA `c2pa.ai-disclosure` assertion path. Compliance is plugin-level and applies to `c2pa-metadata` outputs.\n\n## Brand Context (Auto-Applied)\n\nBefore producing any marketing output from this module:\n\n1. **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.\n2. **If you need the full profile**, read: `~/.claude-marketing/brands/{slug}/profile.json`\n3. **Apply brand voice** — Formality, energy, humor, authority levels must shape all content tone and word choices\n4. **Check compliance** — Auto-apply rules for brand's target_markets and industry using `skills/context-engine/compliance-rules.md`\n5. **Reference industry benchmarks** — Consult `skills/context-engine/industry-profiles.md` for the brand's industry\n6. **Use platform specs** — Reference `skills/context-engine/platform-specs.md` for character limits and format requirements\n7. **Check campaign history** — Run `python \"${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py\" --brand {slug} --action list-campaigns` before planning new work\n8. **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.\"\n9. **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.\n\nDo not ask the user for information that already exists in their brand profile.\n\n## Required Context\n\nBefore executing AEO/GEO work, gather:\n\n1. **Brand Identity**: Official brand name, key products/services, unique value propositions, and brand positioning\n2. **Current AI Footprint**: Ask the user if they have tested how AI platforms currently describe their brand (or offer to audit)\n3. **Target Queries**: The questions and topics the brand wants to be cited for in AI-generated answers\n4. **Existing Content Assets**: Website URL, blog, knowledge base, Wikipedia presence, schema markup status\n5. **Competitive Landscape**: Key competitors who may already have strong AI visibility\n6. **Industry Vertical**: Needed to assess YMYL (Your Money Your Life) sensitivity and trust signal requirements\n\nIf the user cannot provide all context, proceed with what is available and flag gaps as recommendations.\n\n**Minimum viable context**: Brand name and website URL. Everything else can be inferred or discovered during the audit process.\n\n## Capabilities\n\n- **AI Visibility Audit**: Systematic testing of how a brand appears across the 6 canonical surfaces — Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot — for target queries (scored with the standard defined in `/digital-marketing-pro:aeo-audit`)\n- **Citation Optimization**: Restructuring content to maximize the probability of being cited as a source in AI-generated responses\n- **Entity Consistency Audit**: Cross-referencing brand information across Google Knowledge Graph, Wikidata, Wikipedia, Crunchbase, LinkedIn, and industry databases to identify inconsistencies\n- **LLM Content Strategy**: Creating content specifically designed to be ingested and accurately represented by language models\n- **AI Answer Monitoring Framework**: Setting up systematic tracking of AI mentions and citations over time\n- **Structured Data for AI Citation**: Implementing Organization, Product, FAQ, HowTo, and other schema types that improve AI comprehension\n- **Knowledge Graph Optimization**: Improving entity representation in structured knowledge bases\n- **Topical Authority Mapping**: Identifying content gaps that prevent a brand from being recognized as an authority by AI models\n- **AI-First Content Formatting**: Restructuring existing content with clear definitions, factual statements, and citation-worthy snippets\n- **Competitive AI Visibility Benchmarking**: Comparing brand AI presence against competitors across platforms\n\n## Process\n\n**Primary Workflow: AI Visibility Audit & Optimization**\n\n1. **Discovery & Baseline**\n   - Collect brand details, target queries (10-25 queries), and competitor list\n   - Document current schema markup, Knowledge Graph presence, and Wikipedia/Wikidata status\n   - Identify the business model to determine which AI platforms matter most\n   - Catalog existing authoritative content assets (whitepapers, research, data, expert bios)\n   - Assess YMYL classification — brands in health, finance, or legal face higher authority thresholds\n\n2. **AI Platform Testing**\n   - For each target query, document how the brand appears (or fails to appear) on:\n     - **Google AI Mode** (default conversational surface, Gemini 3.5 Flash backbone — May 2026)\n     - Google AI Overviews (classic SERP summary block)\n     - ChatGPT (latest model, web-search mode on)\n     - Perplexity\n     - Gemini (gemini.google.com)\n     - Microsoft Copilot\n   - Score each result: Cited (direct mention with link), Referenced (mentioned without link), Absent, Misrepresented\n   - Capture exact AI-generated text for each query as a baseline\n\n3. **Entity Consistency Check**\n   - Audit brand name, founding date, leadership, product descriptions, and key claims across all knowledge sources\n   - Flag inconsistencies between sources (e.g., different founding years on Crunchbase vs. Wikipedia)\n   - Prioritize fixes by source authority weight\n\n4. **Gap Analysis & Strategy**\n   - Identify patterns: Which query types yield citations? Which don't?\n   - Map content gaps: What authoritative content is missing that AI models need?\n   - Assess structured data gaps: What schema markup is missing or incorrect?\n   - Benchmark against competitors who ARE getting cited\n\n5. **Optimization Execution Plan**\n   - Prioritized list of content to create or restructure\n   - Schema markup implementation plan\n   - Knowledge Graph correction/enhancement steps\n   - Entity consistency fix checklist\n   - Content formatting guidelines for AI-first optimization\n\n6. **Monitoring & Iteration**\n   - Define monitoring cadence (weekly for priority queries, monthly for full audit)\n   - Set up tracking framework to detect citation changes\n   - Establish KPIs: citation rate, accuracy score, query coverage percentage\n   - Track competitor citation changes as part of ongoing monitoring\n   - Re-test after major content updates or schema implementations to measure impact\n   - Log all AI platform model updates that may affect visibility (new model releases, retrieval changes)\n\n**Secondary Workflow: Citation-Optimized Content Creation**\n\n1. Identify a target query cluster where the brand should be cited but currently is not\n2. Analyze what sources ARE being cited for those queries — study their content structure, authority signals, and formatting\n3. Create or restructure content that surpasses cited sources in:\n   - Factual accuracy and specificity (include precise data, dates, numbers)\n   - Clear definitional statements (AI models favor content with unambiguous definitions)\n   - Structured formatting (clear headings, bullet points, tables that AI can parse)\n   - Source credibility signals (author credentials, citations to primary research, organizational authority)\n4. Implement supporting schema markup (FAQ, HowTo, Article, Organization as appropriate)\n5. Build inbound authority signals (internal links from high-authority pages, external citations)\n6. Re-test AI platform responses 2-4 weeks after publication to measure citation pickup\n\n## Reference Files\n\n- `ai-visibility-audit.md` — Step-by-step audit methodology, scoring rubric, and platform-specific testing protocols\n- `citation-optimization.md` — Content restructuring techniques, citation-worthy formatting patterns, and source authority building\n- `entity-consistency.md` — Cross-platform entity audit checklist, Knowledge Graph optimization, Wikidata editing guidelines\n- `llm-content-strategy.md` — AI-first content creation framework, topical authority mapping, and structured data implementation guide\n\n## Output Formats\n\n| Deliverable | Format | Description |\n|---|---|---|\n| AI Visibility Scorecard | Table/Spreadsheet | Query-by-query visibility scores across all AI platforms |\n| Entity Consistency Report | Document | All inconsistencies found with correction instructions |\n| AEO Content Brief | Document | Content creation/restructuring briefs optimized for AI citation |\n| Schema Markup Spec | Code snippets (JSON-LD) | Ready-to-implement structured data markup |\n| Monitoring Dashboard Spec | Document | KPIs, tracking methodology, and reporting cadence |\n| Competitive AI Visibility Matrix | Table | Side-by-side comparison of brand vs. competitor AI visibility |\n| LLM Content Strategy | Document | 90-day content plan focused on building AI authority |\n\n## Edge Cases\n\n### Brand with Negative AI Perception\n- **Situation**: AI platforms are generating inaccurate or negative information about the brand\n- **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.\n\n### New Brand with Zero AI Visibility\n- **Situation**: Brand does not appear in any AI-generated answers\n- **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).\n\n### Common-Word Brand Names\n- **Situation**: Brand name is a common word (e.g., \"Apple,\" \"Slack,\" \"Monday\")\n- **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.\n\n### Multi-Brand Companies\n- **Situation**: Parent company with multiple sub-brands needing separate AI identities\n- **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.\n\n### Regional AI Engines (Baidu, Yandex)\n- **Situation**: User needs visibility on non-Western AI platforms\n- **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.\n\n### YMYL Brands (Health, Finance, Legal)\n- **Situation**: Brands in Your Money Your Life categories face elevated trust requirements from AI platforms\n- **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.\n\n### Rapidly Evolving AI Landscape\n- **Situation**: AI platforms frequently update their models, retrieval methods, and citation behavior\n- **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.\n\n## Tips & caveats\n\n- **Google's official position (15 May 2026):** no `llms.txt`, no AI-specific schema, no separate AI eligibility gate. Don't manufacture work around fictional ranking factors — schema + entity consistency + citation-worthy formatting are what works.\n- **AI Mode citation patterns frequently differ from AI Overviews** on the same query (internal observation, 05/2026 — the \"40-60%\" figure is a rough estimate, re-verify against your own probe set). Audit and optimise for both, treating them as distinct surfaces.\n- **Entity consistency across Knowledge Graph, Wikidata, Wikipedia, LinkedIn, Crunchbase is the single highest-leverage AEO investment** — more impactful than schema tweaks.\n- **AI citations are stickier than blue-link rankings** but slower to win. Expect 3-6 months of consistent work before measurable shift.\n- **Don't try to \"trick\" AI into citing you** with stuffed content or fake authority signals. AI platforms detect and demote this faster than traditional search.\n- **`Google-Extended` (robots.txt) opts out of Google's *other* AI systems** (Gemini training, Vertex grounding) — distinct from the in-Search-Console toggle for AI Overviews/AI Mode (rolled out 3 Jun 2026 via `/digital-marketing-pro:gsc-ai-performance`).\n- **EU markets** require Article 50 disclosure on AI-generated content (applicable 2 Aug 2026) — see `skills/context-engine/eu-code-of-practice.md`.\n\n## Related Skills\n\n- **Content Engine** — For creating and optimizing the actual content that drives AI citations\n- **Analytics & Insights** — For measuring AI visibility performance and tracking citation changes over time\n- **Digital PR & Authority** — For building the E-E-A-T signals and earned media that strengthen AI trust in a brand\n- **Audience Intelligence** — For understanding which queries your target audience is asking AI platforms","author":"@indranilbanerjee","ownerProfile":null,"authorContacts":null,"sourceUrl":"https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/aeo-geo","license":"MIT","category":"review","lang":"en","tokens":4164,"stars":0,"calls30d":2,"claimed":false,"visibility":"public","origin":"crawler","version":"0.1.0","createdAt":"2026-08-22","updatedAt":"2026-08-22","files":[{"path":"ai-visibility-audit.md","size":6386,"sha256":"7ea3d6d8f999c8a92d93f7427ac38a1d6e2f387237bbc34c408e3f1276c02fac"},{"path":"citation-optimization.md","size":5496,"sha256":"11027f56b01fa9a97c0674b1e6ac39dfd82aa6eb594ef60013b4d050d712c827"},{"path":"entity-consistency.md","size":5782,"sha256":"36885b2c273b569d93f1d08252113ac21817f7ccb660cb175396b2edaecc7893"},{"path":"llm-content-strategy.md","size":6173,"sha256":"c5491fcbbfa97fa26e89e3f4561b026818b3a5d23e9ac274c033f7be6917dfcc"}],"requires":{"mcp":[],"tools":[]},"safety":{"flags":[],"scannedAt":"2026-08-22","hasScripts":false,"networkEndpoints":["blog.google","brand.com","developers.google.com"]}}