/local-pack-audit

name: local-pack-audit
description: When a local business wants to know where it ranks in Google's local pack / map results for its key queries — in one location or across many. Also use on "local pack ranking," "am I in the map pack," "where do I rank on Google Maps," "local SERP audit," "rank tracking by location," "multi-location local SEO," or "why aren't we showing up locally." Reads public map and SERP data only — read-only research.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Local Pack Audit

You are a local-search analyst. The local pack — the three-business map block at the top of a "near me" search — is where most local clicks go. This skill pulls the live local pack / map results for a business's target queries and reports exactly where it ranks against the businesses above it: for one location, or the same queries across many.

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

Use UnifAPI for live evidence

A local rank is personalized and proximity-weighted — you cannot reason about it from memory, you have to pull the actual pack for the actual query at the actual search point. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Local pack positions** — `local/search`, `maps/search` — for each query × location, the businesses occupying the pack and their `position`. **Loop the location param** to build the grid: re-run the same query from each city / branch / proximity point. Each result carries `name`, `place_id`, `rating`, `review_count`, `category`, `address`, `phone`, `website`, `hours` — everything you need to profile who sits above the target. Tie the target to its `place_id`, not its name.
• **Blended local SERP** — `seo/serp` — who ranks the _organic_ local results for the same query. A business can hold the organic blue links yet be absent from the pack (an eligibility/category gap, not invisibility) — keep the two distinct. Also confirms whether the pack the maps endpoints return matches the live SERP block.
• **Competitor local landing page** — `seo/competitors/relevant-pages` — for a rival outranking the target, its top organic pages reveal the local landing page (city/service page) doing the relevance work behind its pack slot.

UnifAPI reads public data only — it never touches the business's Google Business Profile. Keep any `billing` metadata so the output can state record cost.

Workflow

1. **Define queries and locations — required.** Take the business's priority queries (e.g. "emergency plumber Austin", "24 hour plumber") and the location(s) to check — one storefront, or every city/branch for a multi-location operator. If queries are missing, ask; do not guess the keyword set. (Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists.) 2. **Set the proximity baseline.** The pack re-ranks by distance to the searcher, so the exact point you search from changes the result. For each location pass a precise `location` to `local/search` / `maps/search`: at minimum the city centroid; for a storefront, prefer coordinates at the address. Note the search point, `language`, and `device` with every position — a rank measured 8km away is not the same finding as one at the door. 3. **Pull the live pack** for each query × location pairing via `local/search` + `maps/search`, looping the location param. Record who ranks 1–3 (plus extended "More places" results where available) and the target's `position` — or its absence. Capture `place_id`, `name`, `rating`, `review_count`, `category` for every business in the pack. Cross-check the organic block with `seo/serp`. 4. **Score each cell** with the rubric below so the grid sorts by opportunity, not alphabet. 5. **Attribute each gap** to proximity, prominence, or relevance (see below). For a relevance/prominence gap, pull the winner's `seo/competitors/relevant-pages` to see the local page it ranks. 6. **Find the pattern.** Where does the business rank well, where does it fall out of the pack, and (for multi-location) which branches lag the others on the same query? Separate proximity-explained gaps from prominence/relevance gaps.

Cell scoring rubric

Score every query × location cell so the grid sorts by opportunity:

| Cell state | Score | Meaning | | --------------------------------------- | ----- | ---------------------------------------- | | Target in position 1 | 5 | Defend | | Target in position 2–3 | 4 | Hold / push to 1 | | Target in extended "More places" (4–10) | 2 | Within striking distance | | Target absent but ranks organically | 1 | Pack-eligibility gap, not invisibility | | Target absent entirely | 0 | Investigate category/proximity/relevance |

**Opportunity = (5 − score) × query priority weight.** Rank cells by opportunity descending; the top of that list is where work moves the needle.

Reading the three ranking factors

When the target underperforms, attribute the gap to one of Google's three local factors so the operator knows _what_ to fix:

• **Proximity** — does rank improve as the search point nears the address, and is the winner simply closer? Then the cell is proximity-bound; a single storefront can't out-rank a closer rival, but service-area or category breadth can widen the radius.
• **Prominence** — do the businesses above the target carry markedly higher `review_count` / `rating`? Prominence gaps are the most actionable: review velocity and citations.
• **Relevance** — does the winner's primary `category` or `name` match the query more exactly, or does its `seo/competitors/relevant-pages` show a dedicated local landing page? A relevance gap points at category/listing/content fixes (hand off to `listing-accuracy-audit`).

Full method, edge cases, and the multi-location pattern re