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/competitive-landscape/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/competitive-landscape)<a href="https://agentmods.dev/skills/every-app/open-seo/competitive-landscape"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/competitive-landscape.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 | $0.00022 | $0.01154 |
| Opus 5 | $0.00011 | $0.00577 |
| Sonnet 5 | $0.00004 | $0.00231 |
| Haiku 4.5 | $0.00002 | $0.00115 |
Grade A, and why
competitive-landscape 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Competitive Landscape
Goal
Answer: "Who is winning this SEO market, what content is working for them, and where are the openings?"
Use this when the user wants a market-level view across several competitors. For a deep dive on one domain, use competitor-analysis.
Required inputs
projectId- Topic, seed keywords, market/category, or user's domain
- Optional known competitors
- Optional 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 market read in it — the saved competitors are the starting roster, and the business and positioning decide who counts as a competitor. - This skill needs competitors. If none are saved, run a minimal inline setup: ask the user who they compete with, or infer a shortlist from
find_serp_competitorsand the site and confirm it, write it back withupdate_project_context(addCompetitors), then continue the landscape work. 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— every confirmed competitor viaaddCompetitorswith a short note on why they matter, plusremoveCompetitorsfor entries you added that turned out irrelevant (leave rows the user added alone) — and append a research log entry:{ appendResearchLog: { summary: "Competitive landscape: <market/query set>. Verdict: <conclusion>" } }.
OpenSEO MCP tools
research_keywords: discover representative market queries.get_keyword_metrics: validate known query sets with volume, difficulty, intent, and trends.get_serp_results: identify recurring ranking domains across target queries.find_serp_competitors: compare domains competing across supplied keywords; use this before manual SERP counting when a keyword set is available.get_domain_overview: size organic footprint for candidate leaders.get_search_console_performance: when the user's own domain is in the comparison and Search Console is connected, anchor their position with first-party clicks/impressions/CTR rather than third-party estimates.get_ranked_keywords: find exact ranking keywords, URLs, ranks, intents, and SERP result types for leaders.get_backlinks_overview: compare backlink/referring-domain strength where relevant.search_local_businesses,get_local_serp_results, andget_google_business_questions: use for local SEO markets where proximity, Maps rankings, business categories, reviews, or Google Q&A affect who is winning.
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 · 90 lines · 22 tokens per session scan A 51dd7405ba64
competitive-landscape is a skill published in the GitHub repository every-app/open-seo (17,561 stars, last pushed 4d ago), licensed MIT. It adds 22 tokens to every session and 1,154 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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