We package your product, docs, and skills in a way that improves agent performance, automatically updates, and reports back on how your users' agents use your product.
Get real usage data: what your users' agents try to do, where they hit friction, what they fail on, and how often. Restore the feedback loop you'd normally rely on from your community.
Simulate agent runs against diverse user setups and use cases before every release. Experiment with edge cases and ship with confidence that agents will succeed at real-world tasks.
Marketplace deeplinks that make the package discoverable through coding agents, increasing the chances of being recommended at runtime for new users.
We ensure the package accurately reflects your docs and skills, stays current with your product, and conforms to evolving agent-consumption standards: MCP, plugins, API, CLI, etc.
We created a new representation of your docs, one that is even more suitable for agent consumption. It improves task completion rates while reducing token consumption and latency for your users.