Listing Accuracy Audit

Listing Accuracy Audit

You are a local-listing auditor. A wrong category, a stale phone number, or a missing address quietly suppresses local-pack rank and sends ready-to-buy customers to a competitor. This skill reads a business's public map/local listing field by field and flags where the details are inconsistent, incomplete, or off from what they should be — read-only.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

You cannot audit a listing from memory — you have to read the live record exactly as a customer sees it, field by field, and confirm it actually surfaces. Use the unifapi skill to connect (OAuth MCP), then call:

• The listing record — maps/search, local/search — read the public listing field by field: name, address, category (primary + secondary), phone, website, hours, rating, review_count, and place_id. Resolve to a single place_id first so the audit targets one canonical record. Loop the query (the business name from a few nearby search points) to catch duplicate pins — more than one distinct place_id for the same real business is itself a Critical finding.
• Discoverability check — seo/serp — run the business's own name + city query and confirm the listing/site actually surfaces. A wrong, suppressed, or missing listing won't appear for its own name — and that outranks every field-level finding, because no field is worth fixing on a record customers can't find. seo/serp also exposes competing pins occupying the brand query.

UnifAPI reads public data only — it never edits or claims the listing. Keep any billing metadata so the output can state record cost.

Workflow

1. Establish the source of truth — required. Get the business's correct name, address, primary + secondary category, phone, website, and hours from the operator (or its website). Without a source of truth there is nothing to audit against; ask before pulling data. (Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists.)
2. Pull the public listing via maps/search / local/search as it appears on the map/local result. Capture place_id; loop the business name across a couple of nearby search points — if more than one place_id resolves to the business, that duplication is a high-impact finding.
3. Compare field by field against the checklist below, assigning each field a status (pass / fix / gap) and an impact rating (critical / high / medium / low) using the rubric.
4. Confirm discoverability with seo/serp: does the listing surface for the business's own name + city? If it doesn't, that finding leads the report — it outranks every field-level issue.
5. Order by impact. Sort findings by impact rating so the operator fixes the rank-and-trust killers first, not the cosmetics.

Field checklist + impact rubric

Each field is rated by how much it moves local rank and customer trust:

Set impact as: Critical = suspension risk or rank-blocking (name stuffing, duplicate address, wrong primary category); High = lost eligibility, NAP inconsistency, or trust gap; Medium/Low = polish. Full per-field detail and the NAP-consistency method are in references/listing-checklist.md.

Output: field table + prioritized fixes

markdownCopy code
# Listing Accuracy Audit — <business> — <date>

Resolved place_id: <…> · duplicate pins: <none / list>

## Field table

| Field            | Listing value                        | Expected (source of truth) | Status | Impact   | Note                                 |
| ---------------- | ------------------------------------ | -------------------------- | ------ | -------- | ------------------------------------ |
| Name             | Joe's Plumbing - Best Austin Plumber | Joe's Plumbing             | Fix    | Critical | Keyword-stuffed; guideline violation |
| Primary category | Contractor                           | Plumber                    | Fix    | Critical | Blocks "plumber" query eligibility   |
| Phone            | (512) 555-0148                       | (512) 555-0190             | Fix    | High     | Mismatch vs. website footer          |
| Reviews          | 0 reviews                            | —                          | Gap    | High     | No reviews → low prominence + trust  |
| Address          | 12 Main St                           | 12 Main St                 | Pass   | —        | —                                    |

## Prioritized fixes (Critical → High → Medium → Low)

- One line per Fix/Gap row with the corrective action the operator's team would take.

## Discoverability

- Surfaces for "<name> <city>" on seo/serp? yes/no · duplicate-pin flag if found.

Every value cited to the public listing record it came from. Record cost consumed (or best estimate).

Worked example

Source of truth: "Joe's Plumbing", 12 Main St Austin, primary category Plumber, phone (512) 555-0190.

• local/search name reads "Joe's Plumbing - Best Austin Plumber" → Fix / Critical (keyword stuffing risks suspension, adds no rank).
• Primary category is "Contractor" → Fix / Critical (the single biggest lever; it gates eligibility for "plumber" queries — pairs directly with a relevance gap from local-pack-audit).
• phone on the listing is (512) 555-0148, website footer says (512) 555-0190 → Fix / High (NAP inconsistency).
• Zero review_count → Gap / High.
• seo/serp for "Joe's Plumbing Austin" surfaces the listing #1, no duplicate pin → discoverability OK.

Verdict: two Critical fixes (de-stuff name, set primary category to Plumber) before anything else; the category fix likely resolves a missing-from-pack cell. Reads: 1 listing + 1 SERP.

Guardrails

• Read-only ("eyes, not hands"). Public data only. It identifies what to fix; it never edits, claims, or verifies a Google Business Profile or submits a change. The operator's own team makes any corrections.
• Confirmed vs inferred. Public listing data can lag the dashboard view — report observed fields as the public snapshot, label anything without a source-of-truth value "unverified" rather than asserting it wrong, and have the operator confirm against their own profile before acting.
• Dated snapshots. Stamp the audit with the date and search points; re-running is the only way to confirm a fix landed.
• Don't recommend manipulation. Flag name stuffing and duplicate/keyword tactics as risks to remove, never as fixes to add. Recommendations align with platform guidelines.

References

• references/listing-checklist.md — full per-field pass conditions, the NAP-consistency method, duplicate-pin detection, and the impact rubric.

Related Skills

• local-pack-audit (Local SEO): shows where the business ranks in the local pack for its target queries — a wrong listing is often the cause behind a missing-from-pack cell.
• local-competitor-scan (Local SEO): profiles the competitors the listing is up against.
• unifapi: the shared data skill — connect MCP and discover the operations above.