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.
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 agentmods add skills/every-app/open-seo/seo-project-setupnpx skills add every-app/open-seo --skill seo-project-setupgit 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/seo-project-setup)<a href="https://agentmods.dev/skills/every-app/open-seo/seo-project-setup"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/seo-project-setup.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.02068 |
| Opus 5 | $0.00018 | $0.01034 |
| Sonnet 5 | $0.00007 | $0.00414 |
| Haiku 4.5 | $0.00004 | $0.00207 |
Grade A, and why
seo-project-setup 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 6d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO SEO Project Setup
Goal
Interview the user once about one website or SEO project, and store the answers in that project's shared context in OpenSEO with update_project_context. That context is read by every other skill, by SAM in the app, and by the user on the project's Context settings page — so it survives new sessions, new machines, and new agents. This is a context setup workflow, not a full audit.
Tone
Be friendly, practical, and structured. Ask questions in small batches. Explain why each item matters only when useful. Do not overwhelm a beginner with jargon.
Where the answers go
Two project-context MCP tools do all the writing. Both are free — they spend no credits.
get_project_context(projectId): everything already known about the project, plus amissingSectionslist.update_project_context(projectId, updates): a list of patch ops. The ones this skill uses:{ section: "business_overview" | "current_goal" | "positioning" | "writing_preferences", content }{ addCompetitors: [{ domain, name?, notes? }] }{ addKeyPages: [{ url, role: "hub" | "spoke" | "money" | "other", topic?, notes? }] }{ customSection: "<slug>", title?, content }for anything that does not fit a typed section{ appendResearchLog: { summary } }when this session spends credits
Write in batches as the interview progresses — do not hold every answer until the end. Sections are prose (~4,000 characters each), so a few tight paragraphs, not a transcript.
Checklist
1. Verify OpenSEO MCP and resolve the project
Writes need a projectId, so do this first:
- Use
whoamiif available. - Use
list_projectsto confirm the user can access projects. - Match the project to the website/domain they want to rank for.
- If the project list is ambiguous, ask the user which project should be used.
- If no project matches, offer to create one with
create_project. - If the MCP is unavailable, tell the user to connect OpenSEO MCP; without it, nothing can be saved.
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.
- 6d ago First seen · 196 lines · 36 tokens per session scan A 3dd3e0dc904b
seo-project-setup is a skill published in the GitHub repository every-app/open-seo (17,305 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 2,068 once invoked, about $0.0002 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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