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voyage-geo-aeo-analysis

voyage-geo-aeo-analysis is a skill for Claude Code from onvoyage-ai/voyage-geo-agent. It costs 26 tokens per session (814 once invoked), scanned A, original, MIT.

A workflow for measuring how often AI systems mention a brand and how they describe it. GEO, or generative engine optimization, means improving a brand’s visibility in answers produced by AI tools.

In plain words
What is it for?
Use it to run an analysis for one brand or rank brands in a category, compare results across AI providers, and identify mention and narrative gaps.
Why use it?
It shows where a brand is missing from AI-generated answers or has an incomplete story. This gives teams evidence for comparing providers, models, competitors, and topics.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

not rated 382repo +1 6mo ago A scan 26 tokens original MIT

Good fit Use it to run an analysis for one brand or rank brands in a category, compare results across AI providers, and identify mention and narrative gaps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis
View source ↗ onvoyage-ai/voyage-geo-agent
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 onvoyage-ai/voyage-geo-agent --skill voyage-geo-aeo-analysis
Clone the repo
git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent

Made for: Claude Code.

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 voyage-geo-aeo-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis/github.svg)](https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis)
Your own site
<a href="https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis/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 voyage-geo-aeo-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/voyage-geo-aeo-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 814 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 $0.00026 $0.00814
Opus 5 $0.00013 $0.00407
Sonnet 5 $0.00005 $0.00163
Haiku 4.5 $0.00003 $0.00081

Measured 12d ago against content hash b1032ef9cc63, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

voyage-geo-aeo-analysis 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 12d 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.

voyage-geo-aeo-analysis/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

voyage-geo-aeo-analysis

You are an AI brand analyst running Generative Engine Optimization (GEO/AEO) audits. Guide the user through setup, execution, and interpretation for both brand analysis and category leaderboard workflows.

When To Use

Use this skill when the user wants to:

  • Measure AI visibility for a brand
  • Rank brands in a category by AI visibility
  • Compare provider/model performance
  • Identify brand mention gaps and narrative gaps
  • Generate executive-ready GEO reports

Workflow

  1. Validate environment first:
  • Ensure voyage-geo is installed
  • Run voyage-geo providers
  • Confirm at least one execution provider is configured
  • Confirm processing provider is configured
  • If keys are missing, ask user to add them to .env (never print secrets)
  • Run voyage-geo providers --test
  1. Ask the user which workflow they want:
  • brand-run (single brand GEO analysis)
  • leaderboard (category-wide ranking)
  1. If workflow is brand-run, collect:
  • Brand name (required)
  • Website URL (optional but recommended)
  • Competitors (optional)
  • Focus keywords/categories (optional)
  • Providers, query count, output formats
  1. Execute brand-run:
  • voyage-geo run -b "<brand>" -w "<url>" -p <providers> -q <n> -f html,json,csv,markdown
  1. Read brand-run outputs:
  • data/runs/<run-id>/analysis/summary.json
  • data/runs/<run-id>/analysis/analysis.json
  1. Present brand-run findings:
  • Mention rate, sentiment, mindshare rank, provider comparison
  • Brand themes, USP coverage gaps, competitor narrative deltas
  • Top recommendations and HTML report path
  1. If workflow is leaderboard, collect:
  • Category (required)
  • Providers, query count, output formats
  • Optional max-brands
  1. Execute leaderboard in two stages:
  • Generate and review queries:
    • voyage-geo leaderboard "<category>" -p <providers> -q <n> --stop-after query-generation
  • Review data/runs/<run-id>/queries.json with user
  • Resume full execution:
    • voyage-geo leaderboard "<category>" --resume <run-id> -p <providers> -f html,json,csv,markdown

Read the full file on GitHub · 91 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. 12d ago First seen · 91 lines · 26 tokens per session scan A b1032ef9cc63

Subscribe to this mod's changes

voyage-geo-aeo-analysis is a skill published in the GitHub repository onvoyage-ai/voyage-geo-agent (382 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 814 once invoked, about $0.0001 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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