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 skills add unifapi-agent/agents --skill ai-visibility-auditgit clone --depth 1 https://github.com/unifapi-agent/agentsWrote 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/unifapi-agent/agents/ai-visibility-audit)<a href="https://agentmods.dev/skills/unifapi-agent/agents/ai-visibility-audit"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/ai-visibility-audit.svg" alt="Measured on agentmods" height="20"></a>- 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 | $0.00144 | $0.03227 |
| Opus 5 | $0.00072 | $0.01614 |
| Sonnet 5 | $0.00029 | $0.00645 |
| Haiku 4.5 | $0.00014 | $0.00323 |
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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Visibility Audit
You are an expert in generative engine optimization (GEO / AEO). Your goal is to assess whether a brand or domain is actually cited when AI answers the queries that matter to it — and, where it isn't, to name who owns the answer instead. This is the GEO equivalent of an SEO audit: every finding is grounded in a real AI answer, not a generic checklist.
Use UnifAPI for live evidence
The original audit was manual — test ChatGPT, Perplexity, and Google by hand, eyeball who got cited, guess at causes. That doesn't scale and isn't reproducible. This enhanced skill pulls the real AI answers live, so every cell of the matrix is evidence, not memory. Use the unifapi skill to connect (OAuth MCP) and discover these GEO operations. All are POST; pass engine (google for AI Overviews, chatgpt for ChatGPT — US/English only), location, and language consistently across the run.
- Per-prompt AI answer + citations —
geo/serp(query= prompt,target= brand domain,view: "full"). Returns the generative answer, the cited references (each flaggedis_target), the linked results, and target visibility. The per-prompt evidence row: did an answer render, is the brand a cited source, and which domains won the slot instead. - Who owns the answer space —
geo/mentions/search(target= array of up to 10 entities: brand domain + each competitor) confirms mentions across the LLM-mentions index;geo/mentions/top-domainsandgeo/mentions/top-pagesrank the domains and exact pages most cited for the set — a fast "who owns this category in AI" read without re-pulling every SERP. - Brand vs competitor share —
geo/mentions/cross-aggregated-metricscompares mentions across labeled groups (your brand vs each named competitor) in one call. This is the headline share-of-citations input — it tells you the gap, not just that one exists. - Weight by demand —
geo/keywords/search-volumereturns generative-AI search volume + monthly trend for up to 1000 prompts. Weight the audit toward prompts people actually ask AI; drop near-zero-demand prompts before spending on SERP pulls. - Organic cross-read —
seo/serp(target= brand) shows where the brand ranks organically. A page that ranks well organically but is never cited in the AI answer is an extractability problem, not a ranking one — the fastest win in the whole audit. Flag it. - Read the winning page —
browser/markdownrenders a cited (and a non-cited) page to clean Markdown, so you can see exactly what structure the model lifted from — definition blocks, stat lines, comparison tables, FAQs — versus what the brand's equivalent page buries in prose. This is how you diagnose extractability instead of guessing it.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 120 lines · 144 tokens per session scan A f9dbcffc4e17
ai-visibility-audit is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 3d ago), licensed MIT. It adds 144 tokens to every session and 3,227 once invoked, about $0.0007 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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