geo-audit

geo-audit is a skill for Claude Code from multiplex-ai/muggle-ai-teams. It costs 60 tokens per session (2,896 once invoked), scanned A, a copy of geo-audit, MIT.

A full website review for GEO, or Generative Engine Optimization: improving how AI systems discover, understand, cite, and recommend a site. It combines checks of technical access, content, AI citability, platforms, and structured data into an overall score and action plan.

In plain words
What is it for?
Use it to audit a website's homepage, content, crawler access, structured data, and presence across AI platforms, then produce prioritised improvements.
Why use it?
It brings several separate checks into one review, so problems affecting AI visibility can be found and prioritised together. GEO differs from traditional SEO because it focuses on AI-generated answers as well as search rankings.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Part of the muggle-ai-teams plugin — 166 skills, 55 commands, 30 agents, 4 hooks shipped together

Good fit Use it to audit a website's homepage, content, crawler access, structured data, and presence across AI platforms, then produce prioritised improvements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/multiplex-ai/muggle-ai-teams/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 multiplex-ai/muggle-ai-teams --skill geo-audit
Clone the repo
git clone --depth 1 https://github.com/multiplex-ai/muggle-ai-teams

Made for: Claude Code.

Or install muggle-ai-teams, the plugin that ships this one along with the rest of its 166 skills, 55 commands, 30 agents, 4 hooks.

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/multiplex-ai/muggle-ai-teams/geo-audit/github.svg)](https://agentmods.dev/skills/multiplex-ai/muggle-ai-teams/geo-audit)
Your own site
<a href="https://agentmods.dev/skills/multiplex-ai/muggle-ai-teams/geo-audit"><img src="https://agentmods.dev/badge/skills/multiplex-ai/muggle-ai-teams/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/multiplex-ai/muggle-ai-teams/geo-audit"><img src="https://agentmods.dev/badge/skills/multiplex-ai/muggle-ai-teams/geo-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,896 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 88% copy Near-identical to another mod 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 $0.00060 $0.02896
Opus 5.5 $0.00024 $0.01158
Sonnet 5.5 $0.00012 $0.00579
Haiku 4.5 $0.00006 $0.00290

Measured 5d ago against content hash 5456d3d76a7f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 5d 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.

Origin

This is a copy

88% identical to geo-audit — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/geo-audit/SKILL.md · 338 lines

How it starts

The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Audit Orchestration Skill

Purpose

This skill performs a comprehensive Generative Engine Optimization (GEO) audit of any website. GEO is the practice of optimizing web content so that AI systems (ChatGPT, Claude, Perplexity, Gemini, etc.) can discover, understand, cite, and recommend it. This audit measures how well a site performs across all GEO dimensions and produces an actionable improvement plan.

Key Insight

Traditional SEO optimizes for search engine rankings. GEO optimizes for AI citation and recommendation. Sites that score high on GEO metrics see 30-115% more visibility in AI-generated responses (Georgia Tech / Princeton / IIT Delhi 2024 study). The two disciplines overlap but have distinct requirements.


Audit Workflow

Phase 1: Discovery and Reconnaissance

Step 1: Fetch Homepage and Detect Business Type

  1. Use WebFetch to retrieve the homepage at the provided URL.

  2. Extract the following signals:

    • Page title, meta description, H1 heading
    • Navigation menu items (reveals site structure)
    • Footer content (reveals business info, location, legal pages)
    • Schema.org markup on homepage (Organization, LocalBusiness, etc.)
    • Pricing page link (SaaS indicator)
    • Product listing patterns (E-commerce indicator)
    • Blog/resource section (Publisher indicator)
    • Service pages (Agency indicator)
    • Address/phone/Google Maps embed (Local business indicator)
  3. Classify the business type using these patterns:

Business Type Detection Signals
SaaS Pricing page, "Sign up" / "Free trial" CTAs, app.domain.com subdomain, feature comparison tables, integration pages
Local Business Physical address on homepage, Google Maps embed, "Near me" content, LocalBusiness schema, service area pages
E-commerce Product listings, shopping cart, product schema, category pages, price displays, "Add to cart" buttons
Publisher Blog-heavy navigation, article schema, author pages, date-based archives, RSS feeds, high content volume
Agency/Services Case studies, portfolio, "Our Work" section, team page, client logos, service descriptions
Hybrid Combination of above signals -- classify by dominant pattern

Read the full file on GitHub · 338 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. 5d ago First seen · 338 lines · 60 tokens per session scan A 5456d3d76a7f

Subscribe to this mod's changes

geo-audit is a skill published in the GitHub repository multiplex-ai/muggle-ai-teams (2 stars, last pushed 2d ago), licensed MIT. It adds 60 tokens to every session and 2,896 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to geo-audit, differing in 22 lines, and is treated as a copy.

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