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Ai presence auditor

Skill OptimizerTeam/agent-skills/skills/ai-presence-auditor

Audit whether a local or home-service business shows up when customers ask AI assistants (ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot) to recommend a provider — and produce the specific, prioritized fixes that move the needle. Use for an AEO/GEO audit of a local business, or when someone asks "do I show up in ChatGPT," "why does AI recommend my competitor," or "how do I get found by AI."From its SKILL.md

Install
npx -y skills add OptimizerTeam/agent-skills --skill ai-presence-auditor

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SKILL.md

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AI Presence Auditor

Customers increasingly ask an AI assistant "who should I call for a plumber in Austin?" instead of scrolling Google. AI names only a handful of businesses — you're either in the answer or you're invisible. This skill audits where a local business stands across the surfaces AI actually reads, and returns a prioritized list of fixes.

What you need (inputs)

  • Business name
  • Primary service(s)
  • City / service area
  • Website URL
  • 2–3 competitors (optional but useful)

How AI actually picks local businesses (the model the fixes follow from)

There is no single "AI" — different assistants read different sources, so "get found by AI" is a multi-surface job, not one listing:

  • ChatGPT leans on the Bing index + your website + Yelp/Foursquare structured data — not primarily Google Business Profile.
  • Google AI Overviews / AI Mode / Gemini lean on your Google Business Profile + the local pack.
  • Microsoft Copilot ← the Bing ecosystem. Siri / Apple MapsApple Business Connect.

A few ground rules that shape every recommendation:

  • AI recommends a small set (~3–5) of businesses, and often not the Google #1. Being cited is not the same as being recommended.
  • Reviews act as a trust filter — roughly 4+ stars is table stakes; thin or below-average review profiles get left out. (There's no proven exact numeric cutoff — don't invent one.)
  • Multi-source consistency beats any single listing: consistent name/address/phone (NAP) across Google, Bing, Apple, Foursquare, Yelp, and directories is what lets the AI trust the entity.
  • Reality check: AI-sourced recommendations are still a small share of local demand today versus the Google local pack and the phone. Treat this as a growing edge, not the whole game — and say so.

The audit

  1. Run representative buyer prompts. Across ChatGPT + Perplexity + Google AI (and Gemini/Copilot if available), use real customer phrasing: "best emergency plumber in {city}", "who should I call for {problem} in {city}", "top-rated {service} near {neighborhood}". For each prompt record: Is the business named? Which competitors are named? What reason or source does the AI give?
  2. Diagnose the surfaces. Check presence + completeness on Google Business Profile, Bing Places, Apple Business Connect, Foursquare, and Yelp. Flag any that are missing or unclaimed.
  3. Check NAP consistency (name, address, phone) across those registries — mismatches confuse the entity and suppress recommendations.
  4. Check reviews — volume, recency, average rating, and response rate. Below-average or thin → likely filtered out regardless of everything else.
  5. Check the website. Does it state, in plain text, exactly what you do and where ("24/7 emergency plumbing in {city}")? Are there service and service-area pages, and an FAQ that matches how people ask AI? Vague "quality solutions for all your needs" copy is effectively invisible to AI.
  6. Check third-party mentions/citations — is the business named in local "best-of" round-ups, directories, and review sites the AI pulls from?

Output

  • A short scorecard per surface (present / missing / needs work).
  • "Where you're missing vs where AI actually looks."
  • A prioritized fix list ranked by impact × effort — quick wins first (e.g., claim Bing Places, fix a NAP mismatch, add a service-area page, start a review-a-week habit).
  • Which competitor the AI names instead, and the most likely reason.
  • An honest framing line: this is a growing edge — don't abandon Google/GBP or the phone.

Guardrails

  • Evidence-based only. Never fabricate rankings, review counts, citations, or percentages you did not actually verify.
  • Recommend; the owner approves and publishes. No surprise changes.
  • Keep the numbers honest — AI's share of local demand is still small; don't overstate urgency.

Built by Optimizer — the AI agent that helps local and home-service businesses get found across Google, their website, AI search, and reviews. This skill is the manual, do-it-yourself version of the audit; Optimizer automates the checks, the fixes, and the weekly loop — with the owner approving every change.

What ships with it: 1 file

3.0 KB alongside SKILL.md

Gives 0 of the 12 instructions most audit compliance skills give in ~1.0k tokens

Counted across 937 of the 1,487 authors here whose files we hold, read 2026-08-07

  • Fetch latest guidelines before each reviewin 43 of 937, across 3 files
  • Group findings by severityin 43 of 937
  • Check files against all fetched rulesin 42 of 937, across 2 files
  • Output findings in terse file:line formatin 41 of 937, across 3 files
  • Ask user which files to review if none specifiedin 41 of 937, across 3 files
  • Read specified files or prompt user for filesin 39 of 937, across 1 file
  • Generate the audit reportin 33 of 937, across 30 files
  • Assign a severity to every findingin 25 of 937
  • Run automated accessibility scansin 23 of 937, across 13 files
  • Output a markdown audit reportin 22 of 937
  • Map findings to WCAG criteriain 20 of 937, across 10 files
  • Confirm audit scopein 19 of 937, across 9 files

Said here and by no other author read

  • Run representative buyer prompts across AI assistants
  • Diagnose presence on major business registries
  • Check NAP consistency across registries
  • Check review volume, recency, rating, and response rate
  • Check website for plain text service and location details
  • Check third-party mentions and citations

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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