Most data work isn t analysis. It s setup and upkeep.
Creating a data source for a new client. Pushing rows from an internal tool. Building the same custom metric for the fifth account in a row. Checking why last night s ingestion failed. Updating permissions when someone leaves the team.
All of it lives behind clicks. So it doesn t run in CI, it doesn t run on a schedule, and your AI agent can t touch it.
That s why we built Databox CLI - launching on Product Hunt on November 6. It puts the Databox API in your terminal: data sources, datasets, data ingestion, custom metrics, databoards, users and client accounts. One install, JSON or CSV output, clear exit codes for scripts. It also ships with agent skills, so Claude Code (or any agent that runs shell commands) can manage your Databox setup for you.
Thanks so much for the detailed review, Harini! Glad the Routines feature stood out to you - that's exactly the idea, get the analysis to run on its own and land where your team already works, instead of everyone having to log into another dashboard.
Really good callout on guided examples. We know the range of things you can build with Routines can feel wide open at first, and we want to make it easier to see what to build, not just how. We're working on more use-case-based examples and templates to help people get started faster. Appreciate you flagging it.
Thanks again for taking the time to write this up, and for the Power BI comparison too. Let us know if you ever want to dig deeper into how to set up automated analysis for your specific use case, happy to help.