agentleFS
Sign inStart free
cablatecablate/mcp-google-map/skills/google-maps-local-seo/SKILL.md

google-maps-local-seo

Skill480 starsChanged 48 days agoMITView on GitHub

Analyze Google Business Profile visibility, geographic grid rankings, local search keywords, and nearby competitors through the standalone Google Maps CLI. Use for local SEO or location-strategy decisions, not ordinary place recommendations.

  • Reads credentials

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read ../_shared/content-attribution.md before presenting reviews, photos, or Google-generated summaries.

    line 15
  2. Very specific:

    Read references/local-seo.md before running an audit.

    line 20
  3. Very specific:

    Use maps_local_rank_tracker for geographic rankings.

    line 22
  4. Distinctive:

    Use place search, comparison, and area exploration to identify competitors and evidence for gaps.

    line 23
  5. Distinctive:

    Do not recommend deceptive reviews, keyword stuffing, impersonation, or other policy-violating tactics.

    line 40

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Google Maps Local SEO

Produce a reproducible local-search visibility analysis grounded in Google Maps results. Use the standalone CLI; no MCP connection is required.

Before calling the CLI

  • On the first call in a session, or after an execution failure, read ../_shared/setup-and-diagnostics.md and run the non-billable local preflight.
  • The user must supply GOOGLE_MAPS_API_KEY. Never expose its value.
  • Read ../_shared/content-attribution.md before presenting reviews, photos, or Google-generated summaries.

Workflow

  1. Confirm the target business or place_id, service area, relevant keywords, grid size, and decision the user needs to make.
  2. Read references/local-seo.md before running an audit.
  3. Resolve the target with maps_search_places and maps_place_details; do not assume businesses with similar names are identical.
  4. Use maps_local_rank_tracker for geographic rankings. Keep the keyword, center, spacing, and grid size with every reported result.
  5. Use place search, comparison, and area exploration to identify competitors and evidence for gaps.
  6. Separate observed API results from recommendations. Explain ARP, ATRP, SoLV, unfound grid points, and sample limitations.
  7. Present prioritized actions tied to the observed evidence; do not promise ranking improvements.

CLI

npx -y @cablate/mcp-google-map exec <tool> '<json_params>'

For exact parameters, read the relevant sections of ../google-maps/references/tools-api.md.

Boundaries

  • Ordinary nearby-place discovery and place comparison belong to google-maps.
  • Consumer travel itineraries belong to google-maps-travel-planning.
  • Grid results are a sampled snapshot, not an exhaustive or stable representation of Google ranking.
  • Do not recommend deceptive reviews, keyword stuffing, impersonation, or other policy-violating tactics.

References

  • references/local-seo.md: required audit method, competitor analysis, metrics, and reporting structure.
  • ../google-maps/references/tools-api.md: selected tool parameters and response shapes.
  • ../_shared/setup-and-diagnostics.md: first-run and failure recovery.
  • ../_shared/content-attribution.md: Places presentation requirements.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free