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Skill906 repo starsupdated 17d ago

aeo-geo

The aeo-geo skill optimizes how brands and entities appear across AI-powered search and answer engines including ChatGPT, Perplexity, Google AI Mode, and Gemini. Use this skill when addressing answer engine optimization, generative engine optimization, citation tracking, entity consistency across knowledge sources, Knowledge Graph representation, or structured data implementation for AI comprehension and visibility.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro /tmp/aeo-geo && cp -r /tmp/aeo-geo/skills/aeo-geo ~/.claude/skills/aeo-geo
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# AEO/GEO Intelligence

## When to Use This Skill

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.

**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/)):

- **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.
- **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.
- **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.

**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)):

- **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.)
- **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.
- **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."

**Opt-out and AI training controls** ([Google AI Features doc](https://developers.google.com/search/docs/appearance/ai-features)):

- 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.**
- 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.
- **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.

**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.

## Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. **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.
2. **If you need the full profile**, read: `~/.claude-marketing/brands/{slug}/profile.json`
3.
ab-test-planSkill

Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs relative MDE via sample-size-calculator.py), test duration, guardrail metrics, stopping rules, and go/no-go decision criteria. Triggers on \"/digital-marketing-pro:ab-test-plan\", \"set up an A/B test\", \"how long should my test run\", \"calculate sample size for an experiment\", \"is this test result significant\". Reads the brand profile and past campaign-tracker results to avoid re-testing validated hypotheses; finished tests are evaluated with significance-tester.py by the cro-specialist agent.

ad-creativeSkill

Generate 3-5 ad copy variations per platform — headlines, descriptions, and CTAs formatted to Google, Meta, LinkedIn, TikTok, X, and Pinterest specs — each scored 1-10 with policy-compliance flags, A/B testing groupings, and a message-match check against the landing page. Triggers on \"/digital-marketing-pro:ad-creative\", \"write ad copy for Meta\", \"give me RSA headline variations\", \"we need LinkedIn ad copy\", \"draft TikTok ad creative\". Reads the brand profile, guidelines, and compliance rules; routes video ad scripts to /digital-marketing-pro:video-script and gates AI-generated visuals for EU campaigns through /digital-marketing-pro:c2pa-metadata and /digital-marketing-pro:check.

add-integrationSkill

Walk through adding a custom MCP server integration to the plugin — searches npm for an existing MCP package (or scaffolds a custom server from the plugin's guide), generates the exact .mcp.json entry, sets up environment-variable credentials, tests connectivity, and documents the tools the new server exposes. Triggers on \"/digital-marketing-pro:add-integration\", \"connect Ahrefs to the plugin\", \"add a new MCP server\", \"integrate our internal API\", \"hook up Stripe data\". Reads the brand profile and agency credential profiles at ~/.claude-marketing/credentials/ to map client-specific keys; custom builds follow skills/context-engine/custom-mcp-guide.md.

aeo-auditSkill

Audit how a brand appears across the 6 canonical AI answer surfaces — ChatGPT, Perplexity, Google AI Mode, AI Overviews, Gemini, Copilot — probing 10-25 queries into a numbered output bundle with per-platform visibility scorecards, citation-accuracy checks, a competitor matrix, content gaps, and an optimization playbook behind a four-gate quality scorecard. Triggers on \"/digital-marketing-pro:aeo-audit\", \"does ChatGPT know about our brand\", \"check our AI search visibility\", \"how does Perplexity describe us\", \"are we showing up in AI Overviews\". Reads the brand profile; reconciles probes against GSC actuals via /digital-marketing-pro:gsc-ai-performance and defines the AI-visibility scoring standard reused by geo-monitor and share-of-voice.

agency-dashboardSkill

Generate a portfolio-level dashboard across ALL client brands — per-client RAG health scores, campaign activity, budget pacing, aggregate KPIs, team utilization, pending approvals, upcoming deadlines, and an alerts panel — built for agency standups and weekly reviews. Triggers on \"/digital-marketing-pro:agency-dashboard\", \"how are all our clients doing\", \"portfolio health check\", \"budget pacing across accounts\", \"which accounts are at risk\". Enumerates every brand under ~/.claude-marketing/brands/ and pulls data via campaign-tracker.py, execution-tracker.py, and team-manager.py; drill into a single client with /digital-marketing-pro:performance-report or /digital-marketing-pro:client-report.

agency-operationsSubagent

Invoke when the user needs to manage multiple client brands, view portfolio-level dashboards, generate client reports, manage SOPs, switch credential profiles, assign team tasks, configure regions, or generate executive summaries. Triggers on requests involving multi-client management, agency workflows, client onboarding, or portfolio oversight.

analytics-analystSubagent

Invoke when the user needs help with marketing measurement, KPI definition, dashboard design, attribution reporting, performance analysis, competitive benchmarking, or translating data into marketing decisions. Triggers on requests involving metrics, reporting, analytics setup, or data interpretation.

analytics-insightsSkill

Marketing measurement module — builds KPI trees per business model, reporting templates (weekly, monthly, QBR, campaign), anomaly root-cause diagnosis, MMM and incrementality guidance, dark-social tracking, and privacy-first cookieless measurement architecture, including the GA4 AI Assistant channel group for attributing AI-referred traffic. Triggers on \"/digital-marketing-pro:analytics-insights\", \"why did traffic drop\", \"define our KPIs\", \"design an executive dashboard\", \"can we do marketing mix modeling\". Reads the brand profile, industry benchmarks, and campaign history; pairs with /digital-marketing-pro:gsc-ai-performance and /digital-marketing-pro:aeo-audit to triangulate AI-surface impressions against actual traffic.