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/competitor-analysisnpx skills add every-app/open-seo --skill competitor-analysisgit 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/competitor-analysis)<a href="https://agentmods.dev/skills/every-app/open-seo/competitor-analysis"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/competitor-analysis.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 | $0.00022 | $0.01152 |
| Opus 5 | $0.00011 | $0.00576 |
| Sonnet 5 | $0.00004 | $0.00230 |
| Haiku 4.5 | $0.00002 | $0.00115 |
Grade A, and why
competitor-analysis 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 5d 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:
- competitor-analysis — 91% identical, 15 lines differ
- conqueror-competitor-analysis — 91% identical, 23 lines differ
- competitor-analysis — 89% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Competitor Analysis
Goal
Analyze one competitor deeply enough to decide what to learn from, avoid, counter-position against, or outrank.
Use this for a named competitor. For identifying the market leaders first, use competitive-landscape.
Required inputs
projectId- Competitor domain
- User's domain when comparison is requested
- Optional topic/category/location/language
Project context
The project-context tools are free and shared with the app and other agents.
- Call
get_project_contextfirst and ground the analysis in it — the saved competitors say whether this domain is already known and what was concluded about it before. - This skill needs competitors. If none are saved, run a minimal inline setup: save the competitor being analyzed, and ask the user (or infer from
find_serp_competitorsand confirm) whether there are others, write them back withupdate_project_context(addCompetitors), then continue the analysis. 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— anaddCompetitorsupsert for this domain with a short note on its strengths and where it is vulnerable — and append a research log entry:{ appendResearchLog: { summary: "Competitor analysis: <domain>. Verdict: <conclusion>" } }.
OpenSEO MCP tools
get_domain_overview: baseline organic traffic and keyword count.get_search_console_performance: when comparing to the user's own domain and Search Console is connected, use it as the first-party baseline (real clicks/impressions/CTR/position) instead of estimating the user's own performance from third-party data.get_ranked_keywords: exact keyword, URL, rank, intent, traffic, CPC, and SERP-type rows for the competitor domain or page.get_backlinks_overview: backlink/referring-domain profile.find_serp_competitors: validate whether the named competitor is a real search competitor across the target keyword set.search_local_businesses,get_local_serp_results, andget_google_business_questions: use for local SEO competitors when Maps/local-pack visibility, nearby businesses, categories, or Google Q&A matter.get_serp_results: validate direct head-to-head SERPs for important keywords.research_keywords: expand gaps or category terms when needed.
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.
- 5d ago First seen · 93 lines · 22 tokens per session scan A c27520abd2cb
competitor-analysis is a skill published in the GitHub repository every-app/open-seo (17,031 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,152 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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