local-competitor-analysis
Skill0 starsChanged 35 days agoMITView on GitHub
Compare a local business with nearby competitors in Google Maps or the Search local pack for the same services and geography. Identify observable profile, review, content and visibility gaps without inventing ranking causes.
In this repository8 other files it gives its agents.
- citation-auditSkill
- gbp-auditSkill
- local-keyword-researchSkill
- local-seo-reportSkill
- location-page-seoSkill
- maps-rank-trackingSkill
- review-insightsSkill
- vibelocalSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Read WORKFLOW.md and ACTORS.md.
≈ “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.
Files
Local competitor comparison
Read WORKFLOW.md and ACTORS.md. Reuse the business context, primary service and area. Ask for missing branch identity or service; do not compare every business in a city.
Use supplied public listings or plan a small places query for that service/location, normally 10–20 results. A local-pack snapshot can answer who appeared in that Search pack; it is distinct from broad Maps discovery. Select 3–5 genuinely relevant candidates from observed results. Verify category/service, branch location, public listing URL and place ID. Keep ambiguous matches in a review queue, not the benchmark.
Create a source-dated comparison matrix: public name/category, relevant service evidence, website/branch page, observed rating and review count, sampled recurring review themes if approved, and measured visibility only where matching query/settings data exists. Missing data is unknown. Broad Maps discovery order is not automatically a precise grid rank.
Look for actionable differences the owner can verify: an inaccurate category, a service that is offered but not explained, clearer appointment/contact information, useful access information, authentic branch content, unresolved review themes or a measured location-specific visibility gap. Do not infer revenue, ad spend, traffic, backlink strength or Google's factor weights from public profile counts.
Deliver three evidence-backed opportunities with the source, likely user benefit, effort estimate labelled as an estimate and a verification step. Recommend one small next change. Use public business data only; no bulk contact enrichment, outreach or copying competitors' descriptions/images. Writing or sending to competitors is outside this route.
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
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.