Find the AI Prompts Your Brand Should Rank For

Where this fits

Imagine a startup GTM lead opens ChatGPT, asks "best expense management tool for small teams," and their product isn't mentioned. There is no ad auction to fix this. Before you can influence AI answers, you need to know which prompts matter. geo-content-research makes an agent do that research and produce prompts.csv, a prioritized query list your team can plug into briefs and tracking.

Why agents benefit

• The agent behaves as a GEO strategist instead of a generic scraper, returning exact query phrasings users type into AI engines.
• Output is a strictly-schema'd CSV, so downstream workflows can ingest it without parsing prose.
• Covers ChatGPT, Gemini, Perplexity, and Claude, recognizing each engine's answer behavior.
• Prioritization means agents don't output a flat dump; they rank where citation is most needed.
• Validation-friendly contract: the harness captures raw CSV, validates it, and fails fast if malformed.

Practical scenarios

Content gap analysis for a new category

Ask the agent to research "AI note-taking apps." It returns queries like "best AI note app for meetings," sorted by citation opportunity. Use the CSV as a content brief.

Monitoring brand visibility in AI answers

Feed prompts.csv to the monitor workflow. The agent periodically asks the same prompts, records whether the brand is cited, and alerts when share of voice drops.

Competitor displacement research

Run the skill for your category and for a competitor. Compare the CSVs: queries where the competitor is cited but your brand is absent become high-priority defense content.

Add it to your agent workflow

Install the skill:

npx skills add onvoyage-ai/gtm-engineer-skills --skill geo-content-research

Then in an agent task: "Run geo-content-research for project management software and write prompts.csv to artifacts." The agent will research and emit the CSV. The output contract is strict: no commentary around it, just the schema-compliant artifact. You can track related GTM skills and community signals on the OpenAgentSkill hub: https://www.openagentskill.com/skills/onvoyage-ai-geo-content-research.

Compare before adopting

Evaluate it against your current research stack. Do you already get AI-engine queries from keyword tools? Those tools model search, not conversation. Check quality signals: Are prompts phrased as people actually ask, not keyword variants? Is the priority score explainable? Can you rerun monthly to keep freshness? Compare with alternatives like generic SEO research skills: they won't produce the exact schema or mention the monitor workflow. Also assess whether your downstream monitor can accept prompts.csv today; if it needs conversion, factor that in.

Why it is worth tracking

The repo has 1,296 stars and a clear maintainer, onvoyage-ai, with a focused schema and integration path. This is early but concrete: AI answers are becoming a measurable channel, and a skill that standardizes query research is worth evaluating as part of your GEO stack. Watch for updates to the schema, engine coverage, and community usage before fully committing.