aeo-audit
The aeo-audit skill evaluates a brand's visibility and citation accuracy across AI answer engines including ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, and Microsoft Copilot. Use it to audit how your brand appears in AI-generated responses, compare visibility against competitors, identify citation gaps, and generate recommendations to improve presence in AI search results. The skill cross-references findings with Google Search Console's AI Performance Report and GA4 data for complete attribution.
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro /tmp/aeo-audit && cp -r /tmp/aeo-audit/skills/aeo-audit ~/.claude/skills/aeo-auditSKILL.md
# /digital-marketing-pro:aeo-audit
## Purpose
Evaluate the brand's visibility and accuracy across AI answer engines. Analyze how the brand is cited, described, and recommended by ChatGPT, Perplexity, **Google AI Mode** (the conversational search surface that became Google's default at I/O 2026 — ~1B MAUs as of May 2026), Google AI Overviews, Gemini, and Microsoft Copilot. Produce optimization recommendations to improve AI visibility.
**AI Mode vs AI Overviews — why both matter:** AI Overviews are the summary block at the top of a classic Google SERP and trigger on a subset of queries. AI Mode is a conversational tab (and now the default search experience for opted-in users) backed by Gemini 3.5 Flash with deeper reasoning, follow-ups, and a different citation pattern. The two surfaces select different sources for the same query in a large share of cases (internal observation, 05/2026 — "40–60%" is a rough estimate; re-verify against your own probe set). Audit both.
**Cross-reference with GSC AI Performance Report (rolled out 3 June 2026):** The Google Search Console AI Performance Report (UK rollout first, global to follow) gives you actual *impressions* in AI Overviews + AI Mode for verified properties. Synthetic probe results from this skill should be reconciled against GSC actuals — see `/digital-marketing-pro:gsc-ai-performance` for the workflow. Important caveat: the GSC report intentionally excludes click data; click-through attribution must come from GA4 (the new `AI Assistant` channel group, added 13 May 2026, captures `Medium=ai-assistant` referrals from ChatGPT/Gemini/Claude; see `/digital-marketing-pro:analytics-insights`).
**Google's official position on AI optimization** (Google AI Optimization Guide, updated 15 May 2026): no `llms.txt`, no AI-specific schema, no separate AI eligibility gate. Pages eligible for snippets in classic Search are eligible for AI Features. Don't manufacture work around fictional ranking factors — `/digital-marketing-pro:aeo-geo` documents what *does* work (entity consistency, citation-worthy snippets, knowledge graph alignment).
**Information Agents (Google AI Pro / Ultra, summer 2026 launch):** Google announced at I/O 2026 a new class of persistent agents that continuously monitor web / news / real-time data for subscribers and deliver synthesized updates with actionable capabilities. Once these go live, they become a **7th probe target** for this skill (alongside ChatGPT / Perplexity / AI Mode / AI Overviews / Gemini / Copilot). Until then, treat AI Mode as the proxy — agents are powered by the same Gemini 3.5 Flash backbone. Source: [blog.google/search-io-2026](https://blog.google/products-and-platforms/products/search/search-io-2026/).
## Input Required
The user must provide (or will be prompted for):
- **Brand name**: The brand to audit
- **Website URL**: Primary domain
- **Key queries**: 5-10 queries a potential customer might ask that should surface the brand
- **Competitors**: 2-3 competitors for comparison
- **Product/service categories**: What the brand should be known for
## Process
1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. **Also check for guidelines** at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files. Check for custom templates at `~/.claude-marketing/brands/{slug}/templates/`. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Define a test query set: branded queries, category queries, comparison queries, "best of" queries, problem-solution queries
3. Analyze how the brand appears in AI responses for each query type
4. Check citation accuracy: Are facts correct? Are URLs valid? Is the description current?
5. Compare brand mention frequency and sentiment against competitors
6. Assess source authority: Which sources are AI engines pulling brand info from?
7. Evaluate structured data and knowledge panel presence
8. Identify content gaps where the brand should appear but does not
9. Generate optimization recommendations for improved AI visibility
## Output
A structured AEO audit report containing:
- AI visibility scorecard across platforms (ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, Microsoft Copilot)
- Query-by-query results showing where the brand appears, how it is described, and citation sources
- Competitor comparison matrix for AI visibility
- Citation accuracy assessment with corrections needed
- Source authority analysis — which pages/sites drive AI mentions
- Content gap list — queries where the brand is absent but should appear
- Optimization playbook: structured data, content strategy, authority building, and entity optimization
## Numbered output convention
All AEO audit outputs go to `${CLAUDE_PLUGIN_DATA}/{brand}/seo/aeo-audit/{YYYY-MM-DD}/`:
```
00-input.md brand identity, target query set, competitor list, AI platforms probed
01-query-set.md the 10-25 queries probed, with intent classification
02-probe-results.json raw probe responses per platform per query (the data layer)
03-platform-scorecard.md visibility scorecard per AI platform (1-10) with diff vs prior run
04-citation-accuracy.md fact-by-fact accuracy check of AI descriptions; what to correct
05-source-authority.md which pages/sites are driving AI mentions; topical entity map
06-content-gaps.md queries where brand is absent but should appear
07-competitor-matrix.md side-by-side AI presence vs competitors
08-quality-scorecard.md the gates below
09-optimization-playbook.md structured data, content, authority, entity work — sequencedDesign 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.
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.
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.
Strategy module for Answer Engine / Generative Engine Optimization — audits AI visibility, restructures content for citation, runs entity-consistency checks across Knowledge Graph, Wikidata, Wikipedia, Crunchbase, and LinkedIn, and produces JSON-LD schema specs, monitoring frameworks, and a 90-day LLM content strategy. Triggers on \"/digital-marketing-pro:aeo-geo\", \"how do we get cited by AI\", \"optimize for AI Overviews\", \"fix our entity consistency\", \"do we need llms.txt\". Reads the brand profile, compliance rules, and industry benchmarks; its measurement counterpart is /digital-marketing-pro:aeo-audit, with GSC actuals via /digital-marketing-pro:gsc-ai-performance.
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.
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.
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.
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.