A workflow for Generative Engine Optimization, the practice of checking how AI answer systems describe and recommend a brand. It covers brand research, query creation, provider runs, analysis, and reports.

Running brand-visibility audits across AI providers and producing HTML, JSON, CSV, or Markdown reports.

It organizes the audit so the research questions, AI checks, and results are handled in a repeatable order.

How onvoyage-ai/voyage-geo-agent configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to onvoyage-ai/voyage-geo-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/onvoyage-ai/voyage-geo-agent/main/.claude/skills/geo-run/SKILL.md

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/geo-run/github.svg)](https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/geo-run)

Your own site

<a href="https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/geo-run"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/geo-run/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/geo-run"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/geo-run.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 1,396 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.

Model	Per session	Once invoked
Fable 5.1	$0.00026	$0.01396
Opus 5	$0.00013	$0.00698
Sonnet 5	$0.00005	$0.00279
Haiku 4.5	$0.00003	$0.00140

Measured 13d ago against content hash 2428b23eec40, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

geo-run 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 13d 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.

.claude/skills/geo-run/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run GEO Analysis

You are an AI brand analyst running a Generative Engine Optimization audit. Guide the user through the full pipeline interactively.

CLI Reference

pip install voyage-geo                     # install if needed
voyage-geo providers                       # list configured providers
voyage-geo providers --test                # health check providers
voyage-geo run -b "<name>" -w "<url>" -p chatgpt,gemini,claude -f html,json,csv,markdown

Flags for run:

--brand / -b (required) — brand name
--website / -w — brand website URL
--providers / -p — comma-separated provider names (default: all via OpenRouter)
--queries / -q — number of queries (default: 20)
--iterations / -i — iterations per query (default: 1)
--formats / -f — report formats (default: html,json)
--concurrency / -c — concurrent API requests (default: 10)
--output-dir / -o — output directory (default: ./data/runs)

Step 1: Gather Brand Info

Ask the user:

"What brand do you want to analyze?" (required)
"What's the website URL?" (optional but recommended)
"Who are the main competitors?" (optional — AI will research if not provided)
"Any specific keywords or product categories to focus on?"

Do NOT proceed until you have at least the brand name.

Step 2: Check Setup & Choose Models

Check if voyage-geo is installed. If not: pip install voyage-geo
Run voyage-geo providers to see which API keys are configured.

Present the available models as a checklist and ask the user which ones to include:

Model	Provider	Key needed
ChatGPT	OpenRouter or OpenAI	`OPENROUTER_API_KEY` or `OPENAI_API_KEY`
Claude	OpenRouter or Anthropic	`OPENROUTER_API_KEY` or `ANTHROPIC_API_KEY`
Gemini	OpenRouter or Google	`OPENROUTER_API_KEY` or `GOOGLE_API_KEY`
Perplexity	OpenRouter or Perplexity	`OPENROUTER_API_KEY` or `PERPLEXITY_API_KEY`
DeepSeek	OpenRouter	`OPENROUTER_API_KEY`
Grok	OpenRouter	`OPENROUTER_API_KEY`
Llama	OpenRouter	`OPENROUTER_API_KEY`
Mistral	OpenRouter	`OPENROUTER_API_KEY`
Cohere	OpenRouter	`OPENROUTER_API_KEY`
Qwen	OpenRouter	`OPENROUTER_API_KEY`
Kimi	OpenRouter	`OPENROUTER_API_KEY`
GLM	OpenRouter	`OPENROUTER_API_KEY`

Read the full file on GitHub · 117 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.

13d ago First seen · 117 lines · 26 tokens per session scan A 2428b23eec40

Subscribe to this mod's changes

geo-run 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 1,396 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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voyage-geo-agent: Skill for Claude Code