OpenSEO is an open-source SEO platform for keyword research, rank tracking, competitor analysis, backlink analysis, site audits, and AI visibility work. It connects SEO data to AI agents through an MCP server and reusable agent skills, while allowing users to supply their own DataForSEO API key and self-host the tool. Catalogue add-ons guide agents through OpenSEO's SEO workflows.
Borrowing it
Nothing to install: this file belongs to every-app/open-seo. 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/every-app/open-seo/main/.agents/skills/local-seo/SKILL.mdgit clone --depth 1 https://github.com/every-app/open-seoWrote 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/every-app/open-seo/local-seo)<a href="https://agentmods.dev/skills/every-app/open-seo/local-seo"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/local-seo.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00024 | $0.01514 |
| Opus 5 | $0.00012 | $0.00757 |
| Sonnet 5 | $0.00005 | $0.00303 |
| Haiku 4.5 | $0.00002 | $0.00151 |
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 8d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Local SEO
Goal
Work out why a business does or does not show up in Google Maps and the local pack near its customers, and what to fix first.
Use this when rankings depend on a physical location or service area. For national organic work, use competitor-analysis or keyword-research.
Required inputs
projectId- The business: name, or a
cid/placeId(most reliable) - Its coordinate (latitude/longitude) — derive it from a
search_local_businesses/get_local_serp_resultsrow; only ask the user when derivation is ambiguous - One to three keywords customers actually search (e.g. "emergency plumber", not the brand name)
Project context
The project-context tools are free and shared with the app and other agents.
- Call
get_project_contextfirst and ground the work in it — what the business does and where it operates decides which keywords and radius matter. - This skill needs
business_overview. If it is empty, run a minimal inline setup: infer what the business does and its location from the site and confirm it with the user in one question, write it back withupdate_project_context, then continue. Never front-load the full interview; suggestseo-project-setupat the end for the rest. - Before spending credits, check the research log. If the same research ran within the last 30 days, reuse that result and say so instead of re-buying it.
- On finish, write back what is durable with
update_project_context— local competitors that have a website viaaddCompetitors(competitor rows are keyed by domain, so skip listings without one), a correctedbusiness_overview— and append a research log entry:{ appendResearchLog: { summary: "Local SEO: <business> near <area>. Verdict: <conclusion>" } }.
OpenSEO MCP tools
search_local_businesses: nearby listings, filterable byminRating,minReviews, andisClaimed— useisClaimed: falseto find unclaimed listings when prospecting. One call with the brand name asqueryand a wide radius returns category, rating, review count, claimed status, coordinates, andcidfor every location of a chain — usually enough that per-locationget_business_profilecalls are unnecessary.get_local_serp_results: the Maps/Local Finder result set near a coordinate. The rows carrycidandplace_id— collect them once and reuse them everywhere below.get_business_profile: the full profile for one business (hours, rating breakdown) when thesearch_local_businessesrow isn't enough.get_business_reviews: reviews with ratings, text, and whether the owner replied. Queued: aprocessingresponse returns ataskId— call again with it after 30-60 seconds, at no extra cost.get_local_rank_grid: rank at every point of a grid around a coordinate, with each point's result count and #1 business. 3x3 is nine searches; only go to 5x5 when the service area is genuinely wide.get_google_business_questions: Q&A on the profile (acceptscid/placeId).get_business_updates: posts published on the profile, with dates.list_business_categories: valid category slugs forsearch_local_businesses.
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.
- 8d ago First seen · 82 lines · 24 tokens per session scan A f66da59563a8
local-seo is a skill published in the GitHub repository every-app/open-seo (17,762 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 1,514 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
setup-svelte-vitals
Set up svelte-vitals in a SvelteKit project: inspect what the project already uses, derive a svelte-vitals.config from its markuplint / eslint-plugin-check-file config and its actual directory conventions, measure each candidate rule before adopting it, and hand the remaining targets to svelte-vitals install. Use when…
wordstat-collector
A tool that collects search statistics from Yandex Wordstat, the Yandex service for researching what people search for. It returns search phrases and their frequencies for Russian-language market research.
improve-svelte
Survey a whole SvelteKit codebase as a senior Svelte/SvelteKit engineer, using svelte-vitals' scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when…
seo-pipeline-llm
4-stage SEO content pipeline: keyword research → competitor scraping → SEO analysis → content creation. Outputs local files, no CMS integration.
seo-pipeline-bash
One-shot SEO content pipeline: scrape keywords from SERP and produce article markdown files via Browser Use SDK or DuckDuckGo fallback.
dataseo-seo-research
Use DataSEO MCP for SEO research workflows including backlink analysis, keyword research, traffic checks, domain comparison, backlink opportunity discovery, AI search query planning, and SEO content briefs. Use this whenever the user asks to investigate a domain, compare competitors, plan SEO content, find backlink…