Map Pack Query Qualification

Local keyword research usually imports volume from a tool and assumes local intent follows. Google decides per query whether to show a pack, and that decision varies by device, by distance from the searcher, and over time. A typical list contains a large share of queries that return no pack anywhere, and for those a profile cannot appear no matter how well it is built.

The skill file

---
name: map-pack-query-qualification
description: Use when a local keyword list is being built or costed and nobody has checked which of those queries return a map pack at all rather than ten ordinary results.
---

# Map Pack Query Qualification

Local keyword research usually imports volume from a tool and assumes local intent follows. Google decides per query whether to show a pack, and that decision varies by device, by distance from the searcher, and over time. A typical list contains a large share of queries that return no pack anywhere, and for those a profile cannot appear no matter how well it is built.

## What you need first

- The candidate query list in both forms: the service term alone and the service term with a place modifier
- A way to search from coordinates inside the target area, either a geo-located device or a tool that accepts a location parameter
- The device split for the category, since packs appear more readily on mobile
- The page or profile currently targeting each query

## Method

1. Run every candidate from a coordinate inside the target area rather than from your own office, because location changes the result type and not merely the ranking.
2. Record the result type per query: local pack present, local finder only, organic results alone, or a page dominated by directories and marketplaces. This classification is the deliverable and volume is secondary to it.
3. Repeat the same queries on mobile at the same coordinates. Packs appear on mobile for terms that show none on desktop, and that split decides whether profile work repays at all.
4. Split the pack queries into implicit local, where the service term alone triggers a pack from the searcher location, and explicit local, where a town is typed. Implicit queries reward proximity and profile strength; explicit ones can be won by a page.
5. Where directories or marketplaces hold most of the page, decide deliberately whether to compete or to get listed inside them. Outranking an aggregator on its own territory is usually a worse use of budget than appearing within it.
6. Assign each surviving query to exactly one asset, a profile or a service page or a location page, so three assets are not funded for the same term.
7. Re-run the classification quarterly, since Google adds and removes packs for entire query classes and a list built once decays without anyone noticing.

## What this produces

A classified query list showing result type, the desktop and mobile difference, implicit or explicit intent, and the single asset assigned to each query.

## Where this goes wrong

- Taking local volume from a keyword tool without checking the result type, which funds profile work for queries no profile can appear in
- Checking results from the agency office, where the location signal produces a pack composition no customer ever sees
- Treating near me as a keyword to target, when it is an intent modifier that Google resolves to the plain service query
- Classifying once and reusing the list for years, by which point some categories have lost the pack entirely

---

From the QuQi skill library - https://www.quqi.io/skills/map-pack-query-qualification

What you need first

• The candidate query list in both forms: the service term alone and the service term with a place modifier
• A way to search from coordinates inside the target area, either a geo-located device or a tool that accepts a location parameter
• The device split for the category, since packs appear more readily on mobile
• The page or profile currently targeting each query

Method

1. 01 Run every candidate from a coordinate inside the target area rather than from your own office, because location changes the result type and not merely the ranking.
2. 02 Record the result type per query: local pack present, local finder only, organic results alone, or a page dominated by directories and marketplaces. This classification is the deliverable and volume is secondary to it.
3. 03 Repeat the same queries on mobile at the same coordinates. Packs appear on mobile for terms that show none on desktop, and that split decides whether profile work repays at all.
4. 04 Split the pack queries into implicit local, where the service term alone triggers a pack from the searcher location, and explicit local, where a town is typed. Implicit queries reward proximity and profile strength; explicit ones can be won by a page.
5. 05 Where directories or marketplaces hold most of the page, decide deliberately whether to compete or to get listed inside them. Outranking an aggregator on its own territory is usually a worse use of budget than appearing within it.
6. 06 Assign each surviving query to exactly one asset, a profile or a service page or a location page, so three assets are not funded for the same term.
7. 07 Re-run the classification quarterly, since Google adds and removes packs for entire query classes and a list built once decays without anyone noticing.

What this produces

A classified query list showing result type, the desktop and mobile difference, implicit or explicit intent, and the single asset assigned to each query.

Where this goes wrong

• Taking local volume from a keyword tool without checking the result type, which funds profile work for queries no profile can appear in
• Checking results from the agency office, where the location signal produces a pack composition no customer ever sees
• Treating near me as a keyword to target, when it is an intent modifier that Google resolves to the plain service query
• Classifying once and reusing the list for years, by which point some categories have lost the pack entirely

Use this skill in your own AI

The download is a plain markdown file with the name and trigger in its frontmatter. Where an assistant supports skills it can load itself, that frontmatter is what it reads to decide this one applies.

Questions about this skill

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