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/ai-visibility-audit/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/ai-visibility-audit)<a href="https://agentmods.dev/skills/every-app/open-seo/ai-visibility-audit"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/ai-visibility-audit/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/ai-visibility-audit"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/ai-visibility-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00100 | $0.02846 |
| Opus 5.5 | $0.00040 | $0.01138 |
| Sonnet 5.5 | $0.00020 | $0.00569 |
| Haiku 4.5 | $0.00010 | $0.00285 |
Grade A, and why
ai-visibility-audit 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Visibility Audit
Goal
Find the work most likely to get a brand named or cited in AI answers about its market, and explain it so a non-expert can act on it. Research broadly; recommend selectively. The report leads with one to three recommendations, each tied to the answers and cited pages that justify it.
This skill reads saved answers, adds the pages behind them, and decides what to do. For demand research alone, use ai-prompt-research.
Project context
The project-context tools are free and shared with the app and other agents.
- External MCP clients: resolve the project with
list_projects, ask only if the match is ambiguous, then callget_project_context. In SAM, use the current project and context already injected into the conversation; SAM has noget_project_contexttool and needs no project selection or connection setup. - This skill needs
business_overview, the website and the main competitors. If the overview is empty, infer it from the site, confirm it in one question, and save it withupdate_project_context. - Read
get_ai_visibility_tracker. Its prompts, competitors, engines, market and latest runs decide which path below applies. Check the tracker's own brand, thebrandsrow withown: true. Its name is the project name. If that is not how people write the brand (for example a project named "Acme website"), answers that name the brand are not counted as mentions and brand questions are not treated as branded. Ask the user to rename the project in the app before collecting or interpreting answers. - Reuse research-log findings under 30 days old for discovery. A claim that drives a recommendation still needs evidence from a run or a page read during this audit.
- On finish, write back the pages the report names with
addKeyPagesand append{ updates: [{ appendResearchLog: { summary: "AI visibility audit: <run id>. Verdict: <conclusion>" } }] }.
OpenSEO MCP tools
get_ai_visibility_results,get_ai_visibility_sources,get_ai_visibility_answer: the evidence. Read one run-wide results call, one run-wide sources call with the samerunId, then answers you need to verify.get_ai_visibility_trend: whether visibility is moving, when the tracker has comparable history. Never compute a trend yourself from separate result calls.research_ai_visibility_prompts: the questions around a prompt group and the sources ChatGPT cites for them (US English only).explore_prompt: asks ChatGPT one prompt through its API and returns the answer, citations andfanOutQueries, the web searches the model ran before answering. Charged at actual usage per uncached answer; cached answers are free for seven days. Requires a paid plan in hosted mode.estimate_ai_visibility_cost,save_ai_visibility_tracker,set_ai_visibility_schedule,run_ai_visibility_check,get_ai_visibility_run: only for the first-run path below.get_ranked_keywords,get_serp_results,get_backlinks_overview: optional context when an owned page's search performance or authority could change a recommendation.- Web reading (fetch, scrape or search):
robots.txt, the owned pages that should be cited, and the pages AI answers cite instead.
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 Changed 36bae0bf1101
- 10d ago First seen · 110 lines · 100 tokens per session scan A 8199e8d4eefc
ai-visibility-audit is a skill published in the GitHub repository every-app/open-seo (23,850 stars, last pushed 3d ago), licensed MIT. It adds 100 tokens to every session and 2,846 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-07.
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