Geo Fundamentals
wasp-lang/wasp
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
Run a category-wide GEO leaderboard — compare all brands in a category to see who has the strongest AI visibility
Install Geo Leaderboard
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboard --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/geo-leaderboard .claude/skills/geo-leaderboard && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .claude/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboardType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboard --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/geo-leaderboard .agents/skills/geo-leaderboard && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .agents/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboard --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/geo-leaderboard .cursor/skills/geo-leaderboard && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .cursor/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/onvoyage-ai/voyage-geo-agent.git --path .claude/skills/geo-leaderboard--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboard --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/geo-leaderboard .gemini/skills/geo-leaderboard && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .gemini/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboardInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/geo-leaderboard .github/skills/geo-leaderboard && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .github/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install onvoyage-ai/voyage-geo-agent geo-leaderboard --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/onvoyage-ai/voyage-geo-agent.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/geo-leaderboard .opencode/skills/geo-leaderboard && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "geo-leaderboard" agent skill from https://github.com/onvoyage-ai/voyage-geo-agent/tree/main/.claude/skills/geo-leaderboard into .opencode/skills/geo-leaderboard/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "geo-leaderboard", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
geo-leaderboard- GitHub stars
- 384
- Token cost
- ~989 tokens
- SKILL.md length
- 433 words
- Files
- 1
- Skills in repo
- 3
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run a category-wide GEO leaderboard — compare all brands in a category to see who has the strongest AI visibility
- Works in 6 steps: Get the Category → Check Providers → Generate Queries (stop for review) → …
- Tasks that involve AI search optimization
- SKILL.md covers How It Works, CLI Reference, Step 1: Get the Category and Step 2: Check Providers, plus 5 more sections
- Calls python3; needs OPENROUTER_API_KEY and ANTHROPIC_API_KEY
What it does
Geo Leaderboard is an agent skill from onvoyage-ai/voyage-geo-agent. Run a category-wide GEO leaderboard — compare all brands in a category to see who has the strongest AI visibility
Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering AI search optimization. The repository describes itself as: Agentic Generative Engine Optimizaiton. The licence is MIT.
When your agent uses it
- Tasks that involve AI search optimization
Example prompts
- “/geo-leaderboard”
Requirements
- Python 3
- A credential in ANTHROPIC_API_KEY
- A credential in OPENAI_API_KEY
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 2ed1cfa. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
OPENROUTER_API_KEYANTHROPIC_API_KEYOPENAI_API_KEYGOOGLE_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Geo Leaderboard loads about 989 tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 433 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from onvoyage-ai/voyage-geo-agent at commit 2ed1cfa, republished under its MIT licence (© onvoyage-ai). 433 words, ~989 tokens.
Download SKILL.mdSave it as
.claude/skills/geo-leaderboard/SKILL.md (or your agent's skills folder).- name
- geo-leaderboard
- description
- Run a category-wide GEO leaderboard — compare all brands in a category to see who has the strongest AI visibility
- user_invocable
- true
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 providersFlags 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, orOPENROUTER_API_KEY. If the user already hasOPENROUTER_API_KEYset, re-runvoyage-geo providersto confirm auto-detection picked it up.
Step 3: Generate Queries (stop for review)
Run with --stop-after query-generation:
python3 -m voyage_geo leaderboard "<category>" -p <providers> -q <n> --stop-after query-generationNote the run ID.
Show full SKILL.md (170 more words)Show less
Step 4: Review Queries with User
Read data/runs/<run-id>/queries.json and present them in a table:
Leaderboard Queries
| # | Strategy | Category | Query |
|---|---|---|---|
| 1 | discovery | recommendation | which vcs are worth pitching to right now |
| 2 | discovery | general | who are the good investors for early stage startups |
| 3 | vertical | recommendation | who invests in climate tech startups these days |
| 4 | vertical | best-of | im in healthcare ai who should i be talking to |
Ask: "These are the queries I'll send to all AI models. Look good?"
If changes needed, edit queries.json directly.
Step 5: Run Full Execution
Once confirmed, resume:
python3 -m voyage_geo leaderboard "<category>" --resume <run-id> -p <providers> -f html,json,csv,markdownThis will:
- Execute all queries against AI providers
- Extract every brand the AI models actually recommended
- Analyze and rank each one
Step 6: Present Results
Read data/runs/<run-id>/analysis/leaderboard.json. Present rankings:
| # | Brand | Score | Mention Rate | Mindshare | Sentiment |
|---|---|---|---|---|---|
| 1 | Sequoia Capital | 72 | 85% | 28% | +0.34 |
| 2 | a16z | 58 | 60% | 18% | +0.12 |
Highlight: who's #1, biggest gaps, provider preferences, surprises.
Tell them the report location. Ask "Want to dig deeper into any brand?"
Allowed Tools
- Bash
- Read
- Glob
- Grep
- Write
- Edit
© onvoyage-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .claude/skills/geo-leaderboard of onvoyage-ai/voyage-geo-agent.
Open the folder on GitHubat commit 2ed1cfa
Compare with similar skills
Geo Leaderboard next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Geo Leaderboard this skillonvoyage-ai/voyage-geo-agent | 384 | — | ~989 | Automated safety check: Pass | MIT | |
| Geo Fundamentalswasp-lang/wasp | 19k | 9 repos | ~861 | Automated safety check: Pass | MIT | |
| SEO GeoReScienceLab/opc-skills | 1.8k | 4 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude | 11k | — | ~2.8k | Automated safety check: Notes | MIT | |
| GEO Monthly Delta Reportzubair-trabzada/geo-seo-claude | 11k | — | ~2.4k | Automated safety check: Notes | MIT | |
| SEO DataforseoAgriciDaniel/codex-seo | 799 | 2 repos | ~4.6k | Automated safety check: Pass | MIT |
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SEO Geo
ReScienceLab/opc-skills
SEO & GEO (Generative Engine Optimization) for websites. An agent skill from ReScienceLab/opc-skills.
GEO-First SEO Audit Tool
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Audits a website for AI search visibility across ChatGPT, Claude, Perplexity and Google AI Overviews while checking traditional SEO, schema and E-E-A-T content quality.
GEO Monthly Delta Report
zubair-trabzada/geo-seo-claude
Compares a baseline and a current GEO audit for a client, calculates score changes and action item progress, and writes a monthly progress report.
SEO Dataforseo
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Live SEO data via DataForSEO MCP server. An agent skill from AgriciDaniel/codex-seo.
Fire Your SEO Agency
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SEO·AEO·GEO·LLMO·NEO(네이버) 다섯 레인을 진단하고 직접 구현하며, 인용되는 콘텐츠를 계속 생산하는 서브 블로그·콘텐츠 운영 파이프라인까지 세팅하는 스킬.
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Run complete GEO analysis workflows with voyage-geo, including both brand runs and category leaderboards
Related
Categories
Questions about Geo Leaderboard
What does Geo Leaderboard do?
Run a category-wide GEO leaderboard — compare all brands in a category to see who has the strongest AI visibility. Geo Leaderboard is an agent skill from onvoyage-ai/voyage-geo-agent.
When should I use Geo Leaderboard?
Geo Leaderboard fits situations like: tasks that involve AI search optimization.
How do I install Geo Leaderboard in Claude Code?
Run `npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a claude-code`. Or copy the skill folder (.claude/skills/geo-leaderboard in onvoyage-ai/voyage-geo-agent) into .claude/skills/geo-leaderboard in your project. Claude Code loads it when a task matches its description.
How do I install Geo Leaderboard in Codex?
Run `npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a codex`. Or copy the skill folder (.claude/skills/geo-leaderboard in onvoyage-ai/voyage-geo-agent) into .agents/skills/geo-leaderboard in your project. Codex loads it when a task matches its description.
Can I use Geo Leaderboard in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add onvoyage-ai/voyage-geo-agent --skill geo-leaderboard -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geo-leaderboard, .gemini/skills/geo-leaderboard, .github/skills/geo-leaderboard and .opencode/skills/geo-leaderboard in your project.
What does Geo Leaderboard need to run?
Going by SKILL.md and its folder, Geo Leaderboard needs the command-line tools its instructions call (python3) and credentials named OPENROUTER_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY and GOOGLE_API_KEY. Our summary lists: Python 3; A credential in ANTHROPIC_API_KEY; A credential in OPENAI_API_KEY.
Does Geo Leaderboard access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Geo Leaderboard safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Geo Leaderboard use?
Geo Leaderboard is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Geo Leaderboard use?
About 989 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Geo Leaderboard?
Skills that share tags, products or a category with Geo Leaderboard: Geo Fundamentals (wasp-lang/wasp, 19k stars), SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and GEO Monthly Delta Report (zubair-trabzada/geo-seo-claude, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Geo Leaderboard?
onvoyage-ai (a GitHub organization) maintains it in onvoyage-ai/voyage-geo-agent, which has 384 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on February 24, 2026.
Source: onvoyage-ai/voyage-geo-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.