Live product: agent pick-rate scanner
Agent Choice Lab
The SEO-style audit for AI coding agents. Paste your docs, README, or launch copy and see whether Codex, Claude Code, Cursor, Copilot, and workflow agents can tell when to choose your devtool.
Live scanner
No login
Client-side only
Sample audit included
Product Hunt signal
May development launches include agent VMs, Cursor, Amp, and MCP-native tools.
Agent-engineer signal
Recent PH AI-engineer products cluster around coding agents, QA, repos, and workflows.
Social signal
Reddit founders are asking whether agents are becoming a new SaaS distribution channel.
Working product
Paste your docs. Get the agent pick-rate report.
This client-side scanner looks for the signals agentic devtools can actually use: crisp job framing, install commands, integration metadata, machine-readable docs, safety boundaries, and proof.
AGENTS.md snippet
Seven-day $100 sprint
Paid audit preview
What the `$49` fix pass actually returns.
The paid audit is not another score screenshot. It gives the founder a before/after patch for agent-readable positioning, install routing, machine docs, and misuse boundaries.
Agents could install it, but could not decide when to choose it.
Score: 58 / 100 Missing: - "use when / avoid when" - competitor contrast - secret handling - machine-readable docs - fresh changelog signal
Agents get a routing rule, safe-use boundary, and copy-paste setup.
Score: 86 / 100 Patched: - AGENTS.md snippet - llms-full.txt outline - quickstart rewrite - "choose us over X when..." - security boundary block
Why this wedge
AI search is crowded. Agent selection is still early.
Agents do not browse like humans.
They parse docs, package metadata, tool manifests, examples, and instructions. A product can rank in SEO and still be hard for agents to choose.
Developer tools have an immediate buyer.
Founders already spend on docs, SEO, launch copy, and onboarding. A narrow agent-readiness audit can sell before a full SaaS exists.
The output is launch-native.
Every scan creates a shareable score, a concrete fix list, and copy that fits Product Hunt, Reddit, GitHub, and founder channels.
Launch kit
Product Hunt angle: the SEO audit for AI coding agents.
The fastest revenue path is concierge: sell three $49 audits in founder communities, then turn repeated fixes into the recurring monitor.
Choose Agent Choice Lab over generic AI visibility dashboards when the buyer needs docs that tell coding agents exactly when to pick, install, and safely use a developer tool.
Free scanner
$49 manual audit
$19 weekly monitor
Launch offer
$49 agent pick-rate audit
Three manual slots this week. You get a score, top missing signals, an AGENTS.md or llms-full.txt snippet, and a before/after docs patch.
- For SDKs, APIs, CLIs, MCP servers, and agent workflow tools.
- Results delivered as a concise Markdown report.
- Weekly drift monitor offered only after the first fix pass.
Start with a public GitHub request using public URLs only. Never paste secrets, private docs, customer data, or payment details there; payment and delivery move to your public contact path after review.