voyage-geo-agent: Instructions file for Codex

AGENTS.md

voyage-geo-agent AGENTS.md is an instructions file for Codex, OpenCode from onvoyage-ai/voyage-geo-agent. It costs 1,271 tokens per session, scanned A, original, MIT.

Installation instructions for Voyage GEO, a Python tool that analyzes how brands appear in answers from generative AI systems. GEO, or generative engine optimization, means improving a brand’s visibility in those answers.

In plain words
What is it for?
Use them to set up Voyage GEO, test provider connections, run brand audits or leaderboards, and export reports in formats such as HTML, JSON, CSV, or Markdown.
Why use it?
They describe how to install the tool, create its agent skill, check configured providers, and run brand or category analyses.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: reads .claude/ paths; mentions Claude Code; built for openclaw.

This is onvoyage-ai/voyage-geo-agent's own configuration. It tells Codex and OpenCode how to work on voyage-geo-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything voyage-geo-agent configures →

Reuse

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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.

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Clone the repo
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voyage-geo-agent AGENTS.md 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.

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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.

AGENTS.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Voyage GEO — Install Instructions for AI Agents

You are an AI agent. Follow these steps exactly to install Voyage GEO for your user.

1. Install the Python package

pip install voyage-geo

2. Create the skill file

Create this one all-in-one skill file. Detect your platform:

  • OpenClaw: write to ~/.openclaw/skills/
  • Claude Code: write to .claude/skills/ in the current project

File: voyage-geo-aeo-analysis/SKILL.md

---
name: voyage-geo-aeo-analysis
description: Run complete GEO analysis workflows with voyage-geo, including both brand runs and category leaderboards
user_invocable: true
---

# 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.

## 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
voyage-geo leaderboard "<category>" -p chatgpt,gemini,claude -q 20 --stop-after query-generation
voyage-geo leaderboard "<category>" --resume <run-id> -p chatgpt,gemini,claude -f html,json,csv,markdown

## Step 1: Validate Setup & Providers

1. Check if voyage-geo is installed. If not: `pip install voyage-geo`
2. Run voyage-geo providers to see which API keys are configured.
3. Present available models and ask the user which ones to include:
   - ChatGPT (OPENROUTER_API_KEY or OPENAI_API_KEY)
   - Claude (OPENROUTER_API_KEY or ANTHROPIC_API_KEY)
   - Gemini (OPENROUTER_API_KEY or GOOGLE_API_KEY)
   - Perplexity (OPENROUTER_API_KEY or PERPLEXITY_API_KEY)
   - DeepSeek (OPENROUTER_API_KEY)
   - Grok (OPENROUTER_API_KEY)
   - Llama (OPENROUTER_API_KEY)
   - Mistral (OPENROUTER_API_KEY)
   - Cohere (OPENROUTER_API_KEY)
   - Qwen (OPENROUTER_API_KEY)
   - Kimi (OPENROUTER_API_KEY)
   - GLM (OPENROUTER_API_KEY)
   Tip: OpenRouter (https://openrouter.ai/keys) gives access to all models with one key.
4. After the user picks models, check which API keys are missing.
   - If keys are missing, ask the user to provide them.
   - Write keys to .env file. NEVER echo keys back to the user.
5. Check the Processing provider line in the voyage-geo providers output.
   - 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.
6. Verify with voyage-geo providers --test
7. Confirm the final model list with the user before proceeding.

## Step 2: Choose Workflow

Ask which workflow they want:
- `brand-run` (single brand GEO analysis)
- `leaderboard` (category-wide GEO ranking)

## Step 3A: Run `brand-run`

Ask:
1. "What brand do you want to analyze?" (required)
2. "What's the website URL?" (optional but recommended)
3. "Who are the main competitors?" (optional)
4. "Any specific keywords or product categories to focus on?" (optional)

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

Summarize the analysis plan, then run:
voyage-geo run -b "<name>" -w "<url>" -p <list> -q <n> -f html,json,csv,markdown

After the run completes:
1. Read data/runs/<run-id>/analysis/summary.json
2. Read data/runs/<run-id>/analysis/analysis.json
3. Present key findings conversationally: mention rate, sentiment, mindshare rank, provider comparison
4. Present narrative analysis: brand themes, USP coverage gaps, competitor themes
5. Highlight recommendations
6. Tell them where the HTML report is

## Step 3B: Run `leaderboard`

Ask:
1. "What category do you want to rank?" (required)
2. "Any specific provider set, query count, or max brands?" (optional)

Do NOT proceed without a category.

Run query generation first:
voyage-geo leaderboard "<category>" -p <list> -q <n> --stop-after query-generation

Then:
1. Read data/runs/<run-id>/queries.json
2. Present queries in a table for user review
3. Resume full execution:
voyage-geo leaderboard "<category>" --resume <run-id> -p <list> -f html,json,csv,markdown

After completion:
1. Read data/runs/<run-id>/analysis/leaderboard.json
2. Present rankings table
3. Highlight #1, biggest gaps, provider preferences, surprises
4. Tell them where the HTML report is

Ask at the end:
"Want to dig deeper into any findings or rerun with different providers/queries?"

Read the full file on GitHub · 128 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. 2mo ago First seen · 128 lines · 1,271 tokens per session scan A 3efb14671693

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voyage-geo-agent AGENTS.md is an instructions file published in the GitHub repository onvoyage-ai/voyage-geo-agent (385 stars, last pushed 8mo ago), licensed MIT. It adds 1,271 tokens to every session, about $0.0051 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.

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