What is geo-leaderboard?

A report that compares brands in a category by checking how often AI services recommend or mention them. It discovers the brands from the AI responses instead of using a preset list.

Use it to study categories such as CRM software or venture capital firms and export the results as HTML, JSON, CSV, or Markdown.

It shows which brands have visibility in AI recommendations, including their share of mentions, general opinion, and relative ranking.

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-leaderboard/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-leaderboard/github.svg)](https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/geo-leaderboard)

Your own site

<a href="https://agentmods.dev/skills/onvoyage-ai/voyage-geo-agent/geo-leaderboard"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/geo-leaderboard/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-leaderboard"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/voyage-geo-agent/geo-leaderboard.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 1,028 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	$0.01028
Opus 5.5	$0.00010	$0.00411
Sonnet 5.5	$0.00005	$0.00206
Haiku 4.5	$0.00003	$0.00103

Measured 2mo ago against content hash acfa68e9e92f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

geo-leaderboard 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 2mo 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-leaderboard/SKILL.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GEO Leaderboard

You are an AI brand analyst running a category-wide leaderboard. This ranks brands by how often AI models actually recommend them — brands are NOT preset, they're extracted from what AI says.

How It Works

Generate recommendation-seeking queries for the category
Execute queries against AI providers
Extract every brand name that AI actually mentioned in its responses
Analyze each brand's mention rate, mindshare, sentiment
Rank by score

No brands are predetermined. The leaderboard measures what AI models actually say.

CLI Reference

python3 -m voyage_geo leaderboard "<category>" -p <providers> -q <n> --stop-after query-generation
python3 -m voyage_geo leaderboard "<category>" --resume <run-id> -p <providers> -f html,json,csv,markdown
python3 -m voyage_geo providers

Flags for leaderboard:

category (positional, required) — e.g. "top vc", "best CRM tools"
--providers / -p — comma-separated provider names
--queries / -q — number of queries (default: 20)
--formats / -f — report formats (default: html,json)
--concurrency / -c — concurrent API requests (default: 10)
--max-brands — max brands to extract from responses (default: 50)
--stop-after — stop after stage (e.g. query-generation) for review
--resume / -r — resume from existing run ID
--output-dir / -o — output directory (default: ./data/runs)

Step 1: Get the Category

Ask: "What category do you want to rank?" Examples: "top vc firms", "best CRM tools", "cloud providers".

Step 2: Check Providers

Run python3 -m voyage_geo providers silently.

Execution providers: If at least one has an API key, proceed.
Processing provider: Check the "Processing provider" line at the bottom.
- If it says "configured" — good, proceed.
- If it says "NOT CONFIGURED" — the user needs at least one of: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, or OPENROUTER_API_KEY. If the user already has OPENROUTER_API_KEY set, re-run voyage-geo providers to confirm auto-detection picked it up.

Read the full file on GitHub · 114 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.

2mo ago First seen · 114 lines · 26 tokens per session scan A acfa68e9e92f

Subscribe to this mod's changes

geo-leaderboard is a skill published in the GitHub repository onvoyage-ai/voyage-geo-agent (385 stars, last pushed 8mo ago), licensed MIT. It adds 26 tokens to every session and 1,028 once invoked, about $0.0001 per session on Opus 5.5. 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.

Analytics and metrics Marketing and SEO

geo-leaderboard next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
geo-leaderboardonvoyage-ai/voyage-geo-agent	Skill	385	8mo ago	MIT	A	26	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	today	MIT	A	103	Claude Code

voyage-geo-agent: Skill for Claude Code