Hypothesis-Driven Data Investigation

My BI GPT project let me ask questions in plain English and get back perfect SQL queries, but I was still stuck on the thinking problem. I'd look at our dashboards, notice something interesting, then lose momentum switching between tools to investigate. I needed a way to go from "that's weird" to "here's what the data actually shows" without breaking my train of thought.

Building on that foundation, I created a hypothesis-driven workflow where each idea starts as a conversation with Claude, then gets marked as supported, contradicted, or inconclusive after investigation.

Next.jsTypeScriptPrismaSQLiteClaude Code

>Load up the hypothesis

⏵⏵ ready(click Execute to begin)

I run the app locally and open Claude Code in my project folder. When I load a CSV and type a hypothesis like "template users churn faster," Claude has access to the app's codebase.

Every pattern gets verified through actual SQL queries. Claude writes them, SQLite runs them, real numbers come back. When we test if template users churn faster, we're not discussing it theoretically. We run the actual queries, get actual counts and calculate percentages.

Claude isn't generating code in a vacuum. It's working with my local database, using Prisma to write queries, accessing the libraries I have installed. The conversation guides the investigation while the database does the math.

There's almost no UI since Claude Code can see and run everything in the project folder. Loading data is just typing "load analytics.csv" and joining tables is just asking for it.