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aeo-audit

Run a one-time AEO audit of six AI answer engines, scored per surface. "check our AI search visibility"

Published by indranilbanerjee ↗indranilbanerjee/digital-marketing-pro

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SKILL.md

name: aeo-auditdescription: "Run a one-time AEO audit of six AI answer engines, scored per surface. \"check our AI search visibility\""argument-hint: "[brand-name or URL]"

/digital-marketing-pro:aeo-audit

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

Metrics: mention rate, citation rate, share of model, share of AI voice, sentiment and the AI Visibility Score are defined, with formulas, in skills/context-engine/ai-visibility-metrics.md. Report every score with its formula and its sample size (n).

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 tab; see the AI Mode note in skills/aeo-geo/SKILL.md), 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 separate conversational tab (AI Overviews remain the default) backed by Gemini 3.5 Flash with deeper reasoning, follow-ups, and a different citation pattern. The two surfaces often select different sources for the same query; no figure is published, so measure the overlap in your own probe set. Audit both.

Copilot citation gaps from Bing (13 Oct 2026): Bing Webmaster Tools → AI Performance → Topics Portfolio (beta) lists, per topic, approximate citations across all sites, how many sites are cited, and Top Opportunity Queries: grounding queries where other sites are cited and yours is not. It is the first first-party citation-gap list; use its opportunity queries to seed this audit's probe set, and label its figures approximate (Bing does). It shows no clicks, and the tab appears only when Bing has enough data for the site. Source: Bing Webmaster blog.

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, last updated 2026-07-10): 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.

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

How you'll know it worked

Every recommendation gets a metric it should move, the direction it should move in, and a date to check it. Add this table to the report, one row per recommendation:

RecommendationMetricExpected directionCheck by
Add a dated statistic with its source to the pricing pageCitation rate on ChatGPT for 10 pricing promptsUp2027-02-01

Then record the rows so the continuous-improvement loop checks them when they fall due:

python "${CLAUDE_PLUGIN_ROOT}/scripts/recommendation-tracker.py" --brand {slug} --action add --source aeo-audit --data '[{"recommendation": "...", "metric": "...", "direction": "up", "check_by": "YYYY-MM-DD"}]'

A recommendation without a metric, a direction (up or down) and a future check-by date is refused (exit 2): if you can't say how you'd know it worked, rewrite it until you can.

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 — sequenced
PLAN.md                     single-page deliverable

Reconcile 03-platform-scorecard.md against /digital-marketing-pro:gsc-ai-performance actuals — probe results show what AI could surface; GSC shows what it actually surfaced.

Quality scorecard

GateWhat it checks
query_set_size≥ 10 queries probed (below this, results are anecdotal)
platform_coverage≥ 4 of the 6 supported platforms probed (ChatGPT, Perplexity, AI Mode, AI Overviews, Gemini, Copilot)
competitor_coverage≥ 2 competitors probed alongside the brand on same query set
citation_accuracy_doneEvery "brand appears" result has been fact-checked (no silent ship of "AI said X — sounds right")

status: ready requires all four gates pass.

AI-visibility scoring standard (canonical — reused across the plugin)

This skill defines the plugin's single AI-visibility scoring standard. Every AI-visibility surface reuses it — do not invent a parallel model.

  • Canonical surfaces (6): Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, Microsoft Copilot. This exact set is the PLATFORMS constant in scripts/geo-tracker.py — reference that constant, don't re-list a different set.
  • Canonical rubric: the per-platform 1-10 visibility score plus the four gates above. Score each platform separately; never average across platforms (a brand can be 9/10 on Perplexity and 2/10 on ChatGPT — the average misleads).
  • Recurring mode: /digital-marketing-pro:geo-monitor applies this same rubric on a schedule (weekly / monthly) and tracks it over time. The 0-100 GEO health score + A-F letter grade that geo-tracker.py emits is the trend view of the same underlying data — a longitudinal roll-up, not a second scoring model.
  • Consumers: geo-monitor (recurring), share-of-voice (its AI dimension), rank-monitor (AI Overview citation presence in --features mode). All reconcile synthetic probe scores against GSC actuals via /digital-marketing-pro:gsc-ai-performance.

Chain handoffs

  • Upstream: /digital-marketing-pro:aeo-geo for the strategy framing this audit measures against
  • Downstream:
    • /digital-marketing-pro:gsc-ai-performance — reconcile synthetic probe results against GSC actuals
    • /digital-marketing-pro:keyword-cluster — 06-content-gaps.md becomes seed input for clustering
    • /digital-marketing-pro:entity-audit — drives 05-source-authority.md corrections in Knowledge Graph
    • /digital-marketing-pro:seo-drift — next quarter, compare two AEO snapshots

Tips & caveats

  • AI Mode and AI Overviews frequently disagree on the same queries (no figure is published, so measure the overlap in your own probe set) — always probe both separately, never roll them into "Google AI".
  • Don't probe more than 25 queries per session. Beyond that, model rate limits + token cost dominate. Pick the 10-25 highest-value queries.
  • Citation accuracy is the audit's most-skipped step. AI engines confidently hallucinate brand facts; if you don't fact-check, you're certifying wrong info. Always check at least the top-cited fact per platform.
  • Synthetic probes overstate presence. Real users phrase queries differently than the test set. The cross-reference with the GSC AI Performance Report (3 Jun 2026, UK first) is what tells you actual impressions.
  • Score the probe results, don't average platforms. A brand can score 9/10 on Perplexity (cites everyone) and 2/10 on ChatGPT (selective citing) — the average misleads. Report per-platform scores side by side.

Agents Used

  • seo-specialist — AI search analysis, entity optimization, structured data, citation strategy