Databox is an agentic analytics platform that brings performance data from across your business together with consistent metrics and business context. Ask its AI Analyst questions about your business, use Routines to surface important changes automatically, and turn insights into reports that explain what happened and why it matters. Databox does more of the recurring reporting and analysis, so you can understand performance faster and take better-informed action.
This is the 11th launch from Databox. View more
Databox CLI
Launched this week
Create data sources, load data, build metrics, and automate workflows directly from your terminal. Use 90+ commands yourself, or let AI coding agents like Claude Code, Cursor, and Codex do the work with 10 ready-to-use agent skills.
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Launch Team / Built With
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Promoted
HubSpot
Agency Analytics
Looker Studio
Claude by Anthropic
Hi Product Hunt! 👋
I’m Tadej from Databox. Today we’re launching Databox CLI, bringing Databox directly into your terminal.
For most people, Databox lives in a browser tab. You log in, check dashboards, and manage your analytics in the app. Meanwhile, teams increasingly use scripts, data pipelines, CI jobs, and AI coding agents to move data between systems, configure tools, and automate repetitive tasks.
Until now, bringing Databox into those workflows meant working directly with our API, writing and maintaining custom code, handling authentication, retries, and errors, and checking that everything worked as expected.
This is exactly the kind of friction we want to remove as part of our broader vision to open up the Databox platform, making it easier for people and AI agents to work with Databox through the tools they already use.
That's why we built Databox CLI. It brings the capabilities of our public API directly into your terminal, so you can build dashboards and metrics, manage your data, and automate repetitive tasks in Databox using simple commands.
Here’s what you can do with it:
Create datasets with defined schemas: set column types and primary keys, and validate records before they’re ingested.
Bring in data from anywhere: Load JSON data from files, scripts, or directly from pipelines. For example: curl -s https://api.internal/export | databox dataset ingest DATASET_ID
Verify every ingestion: Confirm that your data arrived successfully and see exactly what went wrong if it didn't.
Build metrics: Create metrics on top of your datasets and trace where the data comes from.
Manage your Databox account: Work with users, permissions, databoards, and connections.
That's 91 commands across 14 areas, with a 1:1 match to the Databox public API.
And you can let AI coding agents do the work for you, too.
Databox CLI comes with 10 ready-to-use agent skills for Claude Code, Cursor, Codex, and other compatible coding agents. You can ask your agent to set up a dataset, load data, verify the results, and build metrics, and it can execute those steps for you.
We've also built in safeguards for reliable automation. Commands support structured JSON output with --json, clear exit codes, and errors that explain what went wrong. Idempotency keys make retries safe, and destructive actions require an explicit --force flag.
Want to try it?
Getting started takes three commands:
npm install -g databox-cli
databox auth login
databox account list
To add the agent skills: npx skills add databox/databox-cli --skill '*'
Databox CLI is available on all Databox plans and is open source under the MIT license.: github.com/databox/databox-cli, available as a node npm package https://www.npmjs.com/package/databox-cli.
I’ll be here in the comments all day. I'd love to hear what you'd build or automate with Databox CLI. Share your use case, and I'll help you figure out the commands to make it happen. 🙏
Been looking forward to this one team - awesome work, especially for going full agentic.
My first use case with this (as someone in sales) is to use my agent to automate my outreach cadence to people from product signals I have in the database - work that is now being done manually (with great results), but can free up so much time for my AEs to work on the meetings they have.
@zan_perkovic Thanks Žan! That’s a great first use case. Product signals are exactly the kind of data an agent should act on, not just report on. If the signals live in a Databox dataset, your agent can pull the rows with dataset data and build the outreach list from there. Would love to hear how it goes once it’s running.
@zigapotocnik will update you for sure!
Good to see a proper CLI with the agent skills already in the box. I've ended up giving my agents command line tools against the API for most things, so this is pretty much how I'd want to hook an agent up to analytics. Did the 91 commands come straight from the API spec?
@simon_moxon Thanks Simon! Yes, every command maps 1:1 to an endpoint in the Databox public API (V2), so there’s no extra logic between your agent and the data. The 10 agent skills are split by area (datasets, metrics, data sources, users, etc.), so an agent only loads the part it needs. Agents also get --json, clear exit codes, and --idempotency-key for safe retries on writes. Curious what you’ve been using for your other API tools - anything you’d want us to add?
I'm a big fan of CLIs and use them a lot. They're really not as scary as they might sound :) And now Databox (finally) has one too!
What I also like is that anyone can create their own API key and use the CLI, regardless of their Databox plan or user role. Permissions still follow your existing role, so there's nothing extra to manage.
This makes life a lot easier, especially when you're working with scripts, automations, and agents.
@mateja_verlic_bruncic Thanks Mateja! Agree, CLIs are a lot friendlier than people expect, especially once an agent is doing the typing. And yes, anyone can create their own API key now. Since the key acts as you, it follows your existing role and access. No separate permission setup, which keeps scripts and agents simple and safe.
Hey Product Hunt 👋
AI agents are great at writing code. Things get interesting when you give them the tools to do the work.
Databox CLI is that toolset for analytics. Tell your agent “set up our orders data, load last quarter, and build the revenue metrics.” It does the work, checks it, and reports back. You don’t write any custom API integration.
What’s inside:
91 commands, a 1:1 mirror of our public API
10 ready-to-use skills for Claude Code, Cursor, Codex and other coding agents
Schema validation on every record and explicit confirmation for destructive commands, because you’re trusting an agent with real data
This is the third step in opening Databox up. First came the public API, then MCP, and now the CLI.
My question for you: if your agent could run your analytics, what’s the first thing you’d hand over?
Boy do I like not leaving my terminal these days. MCPs are cool, but I like spamming my terminal commands as in the old days more ;)
@aljaazm Ha, same here Aljaz. Both have their place - MCP when you want to chat with your data inside Claude or ChatGPT, the CLI when you want to script it, pipe it, or let an agent run it in the terminal.
When you envision the ultimate user for this product. What does he/he use it for, and how does he/she use it? What problem do they have, and how does this solve it and what does the outcome look like? Trying to understand it a bit more. Thanks
@alchemist_studio Great question, André. The ideal user is someone technical enough to work in a terminal or with an AI coding agent like Claude Code - a developer, a data or ops person, or a technical marketer at a SaaS company or agency.
The problem: a lot of important data doesn’t come from a ready-made integration. Product data from your own database, CSV exports, internal tools. Getting it into Databox used to mean manual uploads or writing custom code against the API. Agencies have a second problem: setting up the same data sources, datasets and metrics for every new client by hand.
How they use it: install the CLI, add the skills, and tell the agent what you want in plain words. “Create a dataset for our monthly revenue export and load this file.” The agent picks the commands, sets up the dataset, loads the data and checks it worked.
The outcome: data that was stuck in files or scripts shows up in Databox on its own, next to everything else, with metrics and dashboards on top. Repeat work turns into one command or one prompt, and the team spends its time on the numbers instead of moving them around.