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The Agentic Discovery Playbook
GEO Playbook for Optimizing Products for AI Agents
Discover how AI agents find, evaluate, and pick products. A free, ungated playbook built on original experiments and live data. Proven on coding agents (Claude Code, Codex, OpenCode) choosing APIs, MCP servers, and libraries. 11 evidence-backed plays, original research, honest verdicts.
Hey PH 👋 Raymond from Synscribe.
Six months ago we started asking a simple question: when an AI coding agent "decides" to use Stripe or Resend or Postgres, what actually made that choice? We expected to find SEO-with-extra-steps. We found a different machine entirely.
Some of what we measured, building this:
> A two-year-old auth library is the #2 most-fetched docs source among coding agents, ahead of React. Defaults are NOT locked into training data.
> In our pilot experiments, a one-paragraph AGENTS.md file flipped an agent's product choice 100% of the time, and the agent invented reasons to praise the product it was told to use.
> Bun's growth hack is a filename: `bun init` writes `use-bun-instead-of-node...` rules into every project it scaffolds.
> We instrumented agents doing real research: they opened ~6% of search results and killed ~48% of vendor claims they fact-checked.
> ~99% of MCP distribution bypasses the directories everyone tells you to list in.
So we wrote the playbook we wished existed: 11 plays across the agent's whole journey, 10 company teardowns (Stripe, Bun, Tailwind, Better Auth…), a 30-min self-audit, and a mythbusting ledger where we publish "unproven" out loud when our data can't settle it.
Everything is free, ungated, with methodology and limitations published. Our experiments are pilot-scale and labeled that way on every page.
The meta part: the guide practices what it preaches. It ships its own llms.txt, serves every page as raw markdown, and stamps every claim with a verification date. We ran the playbook on the playbook.
The data came from an agent-observability instrument we built (think: Search Console for the part of the funnel where agents decide).
I'll be here all day. Bring skepticism.
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About The Agentic Discovery Playbook on Product Hunt
“GEO Playbook for Optimizing Products for AI Agents”
The Agentic Discovery Playbook was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #110 on the daily leaderboard. Discover how AI agents find, evaluate, and pick products. A free, ungated playbook built on original experiments and live data. Proven on coding agents (Claude Code, Codex, OpenCode) choosing APIs, MCP servers, and libraries. 11 evidence-backed plays, original research, honest verdicts.
The Agentic Discovery Playbook was featured in Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers) and Tech (627.5k followers) on Product Hunt. Together, these topics include over 345.9k products, making this a competitive space to launch in.
Who hunted The Agentic Discovery Playbook?
The Agentic Discovery Playbook was hunted by Raymond Yeh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Raymond Yeh