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/keyword-clustering/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/keyword-clustering)<a href="https://agentmods.dev/skills/every-app/open-seo/keyword-clustering"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/keyword-clustering/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/every-app/open-seo/keyword-clustering"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/keyword-clustering.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00017 | $0.01033 |
| Opus 5 | $0.00009 | $0.00517 |
| Sonnet 5 | $0.00003 | $0.00207 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
keyword-clustering 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 10d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- keyword-clustering — 86% identical, 9 lines differ
- conqueror-keyword-clustering — 84% identical, 17 lines differ
- keyword-clustering — 84% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Keyword Clustering
Goal
Group keywords into page-level clusters and decide which existing or new page should target each cluster. This is a keyword mapping workflow, not just a semantic grouping exercise.
Required inputs
projectId- A keyword list, saved keyword tag, seed topic, or target domain
- Optional existing URLs/pages to map against
If keywords are not provided, use list_saved_keywords for saved sets, research_keywords for seed discovery, or get_ranked_keywords when the user starts from a target domain.
Project context
The project-context tools are free and shared with the app and other agents.
- Call
get_project_contextfirst and ground the mapping in it — the saved key pages are the existing pages clusters should map to, and the business and goal decide which clusters are worth targeting. - This skill needs key pages. If none are saved, run a minimal inline setup: ask the user for the pages that matter, or propose a shortlist from the site, an audit, or Search Console and confirm it, write it back with
update_project_context(addKeyPages), then continue the clustering. 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— new or correctedaddKeyPagesentries with the topic each page now targets — and append a research log entry:{ appendResearchLog: { summary: "Keyword clustering: <keyword set>. Verdict: <conclusion>" } }.
OpenSEO MCP tools
list_saved_keywords: fetch an existing keyword set, optionally filtered by tags.research_keywords: expand a seed when the user starts from a topic.get_ranked_keywords: gather exact ranking keywords and URLs when the user starts from a domain or page.get_search_console_performance: when Search Console is connected, pull real queries withdimensions: ["query","page"]to map terms to the pages already earning impressions and to surface cannibalization (one query splitting clicks across multiple URLs).get_serp_results: validate whether keywords belong on the same page by checking SERP overlap and intent.get_local_serp_results: use for local SEO clusters when Maps/local-pack intent should affect page mapping.save_keywords: optionally tag final clusters after user confirmation.
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.
- 10d ago First seen · 86 lines · 17 tokens per session scan A f4a53947527c
keyword-clustering is a skill published in the GitHub repository every-app/open-seo (18,020 stars, last pushed 6d ago), licensed MIT. It adds 17 tokens to every session and 1,033 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.
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