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 nocodework/growth-os --skill geo-auditgit clone --depth 1 https://github.com/nocodework/growth-osWrote 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/nocodework/growth-os/geo-audit)<a href="https://agentmods.dev/skills/nocodework/growth-os/geo-audit"><img src="https://agentmods.dev/badge/skills/nocodework/growth-os/geo-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/nocodework/growth-os/geo-audit"><img src="https://agentmods.dev/badge/skills/nocodework/growth-os/geo-audit.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00121 | $0.01295 |
| Opus 5 | $0.00060 | $0.00647 |
| Sonnet 5 | $0.00024 | $0.00259 |
| Haiku 4.5 | $0.00012 | $0.00129 |
Grade A, and why
geo-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 11d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
geo-audit
Search is splitting. A growing share of buying research now happens inside an AI assistant that answers directly and never sends a click. geo-audit measures whether your brand exists in that layer: when someone asks an assistant the questions your ICP actually asks, do you get named, recommended, and cited — or does a competitor?
What it does
Runs a repeatable measurement protocol and produces a scorecard you can re-run over time to see the trend:
- Visibility — in what share of the query set does the brand appear at all.
- Rank — when it appears, where in the answer (first named, mid-list, footnote).
- Sentiment — how it's framed (recommended, neutral mention, caveated, negative).
- Recommendation rate — how often the assistant actively suggests it, versus merely listing it.
- Share of Voice — brand mentions as a fraction of all brand mentions across the set (you vs the field).
- Citation domains — which sources the assistants pull from when they answer these questions (your owned domains, review sites, competitors, publications). This is the roadmap for
geo-content.
When to use
- As the GEO section of
/growth-os:audit. - Standalone, when someone specifically wants to know their standing in AI answers.
- On a repeating baseline (monthly) to measure whether GEO work is moving the needle.
Inputs
- A written hub (
.agents/product-marketing.md) — the ICP and category drive the query set. Without it, ask for the ICP or route tocontext. - The brand name and its main alternatives (from the hub).
The protocol
The whole point is repeatability. Same queries, same method, same read — so the numbers compare across runs. Design the run once, then keep it fixed.
- Derive ~15 queries from the ICP. Turn the ICP's real jobs-to-be-done into the natural language a buyer would actually type — not brand-name lookups. Mix:
- Category discovery: "best tools for ," "how do teams handle ."
- Comparison: " alternatives," " vs others."
- Recommendation: "recommend a for , and cite your sources." Explicitly ask for sources where the assistant supports it — you want the citation domains.
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.
- 11d ago First seen · 80 lines · 121 tokens per session scan A 3eeb8697bb7d
geo-audit is a skill published in the GitHub repository nocodework/growth-os (6 stars, last pushed 2mo ago), licensed MIT. It adds 121 tokens to every session and 1,295 once invoked, about $0.0006 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 2026-08-31.
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