open-seo-mcp-skills
Free SEO MCP server + open-source SEO and GEO skills for Claude: keyword research, rank tracking, audits, backlinks, AI visibility on your real GSC/GA4/ads data. claude mcp add ryze --transport http https://connector.get-ryze.ai/mcp
Could not verifysource: GitHubhomepageShellMITcommit 81ac50e95131
Shell, MIT licensed. The project labels itself: ai seo, ai visibility, backlinks, claude, claude code, claude skills, dataforseo and generative engine optimization.
Argusic could not get open-seo-mcp-skills running. An Argusic agent installed it in 0.2 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
All 8 SKILL.md files installed to ~/.claude/skills/ with correct content. The repository is a collection of Markdown skill definitions for Claude Code that rely on the external Ryze MCP connector, no local application to launch or tests to execute.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 50 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- under a minute
- Cold machine to finish
- 6 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- bash install.sh exited 0; all 8 skills copied to ~/.claude/skills/ with byte-exact content match verified (diff between source and destination for all 8 files); all 8 SKILL.md files have valid YAML frontmatter (name + description fields present); plugin.json and marketplace.json are valid JSON; ghs-toplight.png verified intact via `ffmpeg -v error -i FILE -f null -`
- Model tokens used
- 37,649
- Exact commit tested
- 81ac50e95131
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
- Whether it works at all
- this run verified nothing, so this page makes no claim either way
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
skills/content-brief/SKILL.md | 32 +++++++++++++ skills/keyword-research/SKILL.md | 29 +++++++++++ skills/rank-tracking/SKILL.md | 33 +++++++++++++ skills/seo-audit/SKILL.md | 34 +++++++++++++ skills/seo-vs-ads/SKILL.md | 31 ++++++++++++ 14 files changed, 412 insertions(+) exec /bin/sh -lc "cat << 'EOF' EOF' in /work/repo succeeded in 0ms: tokens used 37,649
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Installed in 0.2 minutes.
- Nothing broke on the way: zero errors between clone and running.
- Licensed MIT, as reported by its host.
- Measured 2 times, so the result is not a one-off.
What did not, or is not known
- An agent with no time limit did not get it running.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Did not run | 50.00 | 0.03 | |
| Argusic Runner | 1/3 | Did not run | 50.00 | 0.02 |
Topics (from GitHub)
ai-seoai-visibilitybacklinksclaudeclaude-codeclaude-skillsdataforseogenerative-engine-optimizationgeogeo-mcpkeyword-researchllm-seomcpmcp-serveropen-source-seorank-trackingseoseo-auditseo-mcpseo-mcp-server
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/open-seo-mcp-skills)Questions
- Does open-seo-mcp-skills run?
- Argusic could not verify that open-seo-mcp-skills runs. Argusic installed and launched it on a clean machine in 0 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test open-seo-mcp-skills?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 81ac50e95131. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test open-seo-mcp-skills?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does open-seo-mcp-skills take to install?
- 0.2 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does open-seo-mcp-skills need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing open-seo-mcp-skills?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for open-seo-mcp-skills?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.