geo-audit

geo-audit is a skill for Claude Code from nocodework/growth-os. It costs 121 tokens per session (1,295 once invoked), scanned A, original, MIT.

A measurement process for checking how often a brand appears in answers from AI assistants such as ChatGPT, Gemini, Claude, and Perplexity. It runs 15 natural-language questions and records visibility, position, tone, recommendations, competing mentions, and cited sources.

In plain words
What is it for?
Use it to measure a brand's presence in AI-generated answers over time. It helps track mentions, ranking within answers, recommendation frequency, share of voice, sentiment, and the websites assistants cite.
Why use it?
Traditional web search shows links, while AI assistants may answer directly without sending a visitor to a website. This audit shows whether a brand is being mentioned and recommended when its potential customers ask relevant questions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the growth-os plugin — 8 skills shipped together

Good fit Use it to measure a brand's presence in AI-generated answers over time. It helps track mentions, ranking within answers, recommendation frequency, share of voice, sentiment, and the websites assistants cite.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nocodework/growth-os/geo-audit
Install

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.

Any agent
npx skills add nocodework/growth-os --skill geo-audit
Clone the repo
git clone --depth 1 https://github.com/nocodework/growth-os

Made for: Claude Code.

Or install growth-os, the plugin that ships this one along with the rest of its 8 skills.

Wrote 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.

agentmods badge for geo-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/nocodework/growth-os/geo-audit/github.svg)](https://agentmods.dev/skills/nocodework/growth-os/geo-audit)
Your own site
<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.

agentmods 80×15 button for geo-audit

Your own site · 80×15
<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>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,295 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 3eeb8697bb7d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

core/skills/geo-audit/SKILL.md · 80 lines

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 to context.
  • 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.

  1. 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.

Read the full file on GitHub · 80 lines

Changes

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.

  1. 11d ago First seen · 80 lines · 121 tokens per session scan A 3eeb8697bb7d

Subscribe to this mod's changes

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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