Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OneWave-AI/claude-skills --skill local-seo-optimizergit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote 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/onewave-ai/claude-skills/local-seo-optimizer)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/local-seo-optimizer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/local-seo-optimizer/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/onewave-ai/claude-skills/local-seo-optimizer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/local-seo-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00765 |
| Opus 5 | $0.00028 | $0.00382 |
| Sonnet 5 | $0.00011 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
local-seo-optimizer 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 9d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local SEO Optimizer
For a local business, the Google Business Profile is the real homepage and the local pack (map results) is the real search ranking. This skill audits the three local ranking pillars -- relevance, distance, prominence -- and produces the fix list. Complements seo-optimizer (content/on-page SEO); this is the maps-and-profile layer.
Workflow
- Baseline. Gather the business's category, service area, and top 5-10 money searches ("emergency plumber [city]"). Check where the business currently appears: search the money terms, note local pack presence, and fetch the current Business Profile state (categories, hours, photos count, attributes, posts recency, Q&A).
- Profile audit. Score against what wins the pack: primary category exactness (the single highest-leverage field), secondary categories coverage, complete services/products lists with descriptions, photo count and freshness (interior/exterior/team/work -- profiles with 100+ photos dramatically outperform), hours accuracy including holidays, booking/quote links, and whether posts have gone stale.
- Review engine. Review count, average, velocity vs. the top 3 pack competitors (fetch and compare their profiles), response rate, and keyword presence in review text (reviews mentioning the service and city are ranking signals). Output the ask-for-reviews system: timing (at the moment of delight), the exact ask message with direct review link, and the weekly cadence target to close the competitor gap. Pair with
review-response-writerfor the response side. - NAP sweep. Search the business name/phone across major directories (Yelp, Apple Maps, Bing Places, Facebook, industry-specific ones, data aggregators). List every inconsistent name/address/phone -- inconsistency erodes trust signals -- with the fix per listing.
- Website local layer. Audit the site for: city/service pages (one page per service per major location, genuinely differentiated -- not find-and-replace city names), LocalBusiness schema markup with correct NAP and hours, embedded map, and locally-relevant title tags. Output the page-by-page fix list, with drafts for the highest-value gaps.
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.
- 9d ago First seen · 35 lines · 57 tokens per session scan A 463a6d7f246a
local-seo-optimizer is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 765 once invoked, about $0.0003 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 2026-09-03.
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