Borrowing it
Nothing to install: this file belongs to prashishh/seo-geo-report-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/prashishh/seo-geo-report-engine/main/.agents/skills/local-seo/SKILL.mdgit clone --depth 1 https://github.com/prashishh/seo-geo-report-engineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/prashishh/seo-geo-report-engine/local-seo)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/local-seo"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/local-seo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/local-seo"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/local-seo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01568 |
| Opus 5 | $0.00046 | $0.00784 |
| Sonnet 5 | $0.00019 | $0.00314 |
| Haiku 4.5 | $0.00009 | $0.00157 |
Grade A, and why
local-seo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
local-seo
Wins the local pack and local organic for a business's target cities. Covers Google Business
Profile, citation/NAP health, local landing pages, reviews, and pack tracking. Uses Ahrefs MCP for
the rank/keyword side (see knowledge/ahrefs-mcp-map.md); call doc before first use.
Methodology (PERCEIVE → ANALYZE → VALIDATE → ACT)
PERCEIVE — establish the footprint. Resolve project (./bin/mkt config show --project <client>); read client.yml for the canonical NAP (name/address/phone), service areas, and
target cities. Classify the business: brick-and-mortar (physical address), service-area
business / SAB (no public address), or hybrid — this changes GBP setup and page strategy.
Pull tracked locations with management-locations; pull local rankings with rank-tracker-overview
and rank-tracker-serp-overview filtered to each city/location, and competitor positions via
rank-tracker-competitors-overview. Capture the live pack with serp-overview (location-set) +
WebSearch/WebFetch for GBP fields that aren't in Ahrefs.
ANALYZE — five levers.
- GBP optimization — correct primary category (the single biggest local lever; a wrong one suppresses the pack), secondary categories, complete services/products, hours, photos, posts, and verification. Geo-coordinates accurate to 5+ decimals.
- NAP / citation consistency — name/address/phone identical across the site, GBP, and top directories (Apple Business Connect, Bing Places, BBB, industry/local citations). Inconsistency = the most common pack-ranking drag. List every discrepancy found.
- Local landing pages — one credible page per city/service the business genuinely serves (not doorway/spam pages); city in title/H1/URL, embedded map, local proof. Dedicated service pages are a top local organic factor.
- Reviews strategy — rating health (target 4.5+), velocity (steady fresh reviews — long gaps correlate with pack decline), a review-generation ask in the customer flow, and owner responses to all reviews.
- Local pack tracking — track the money terms per city in
rank-tracker-overview; watch competitor movement withrank-tracker-competitors-pages/-stats.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 94 lines · 93 tokens per session scan A bdf72cf397f2
local-seo is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 1,568 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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