I built an open-source workflow kit that turns AI agents into structured data analysis partners

Eddie — Sun, 15 Feb 2026 03:48:17 +0000

🔗 GitHub repo: https://github.com/with-geun/alive-analysis

Over the past year, I’ve been using AI coding agents (Claude Code, Cursor, etc.) heavily for data analysis work.

They’re incredibly helpful — but I kept running into the same problem.

Every analysis was a throwaway conversation.

No structure.

No tracking.

No way to revisit why I reached a conclusion.

A month later, I’d remember what we decided, but not how we got there.

So I built alive-analysis — an open-source workflow kit that adds structure, versioning, and quality checks to AI-assisted analysis.

The problem

When you ask an AI to “analyze this data,” you usually get:

a one-shot answer
reasoning that’s hard to trace later
no shared artifact for your team

In practice, analysis becomes:

inconsistent
hard to review
impossible to learn from over time

I wanted something closer to how real analysis work actually happens — iterative, documented, and revisitable.

The idea: treat analysis like a repeatable workflow

alive-analysis structures every analysis using a simple loop:

ASK → LOOK → INVESTIGATE → VOICE → EVOLVE

ASK

Define the real question, scope, and success criteria.

LOOK

Check the data first — quality, segmentation, outliers.

INVESTIGATE

Form hypotheses, test them, and eliminate possibilities.

VOICE

Document conclusions with confidence levels and audience context.

EVOLVE

Capture follow-ups and track impact over time.

Instead of generating answers immediately,

the AI guides you through these stages by asking questions.

That small change alone dramatically improved the rigor of my analyses.

What it actually does

alive-analysis is not a BI tool or dashboard replacement.

You still use:

SQL
notebooks
dashboards
your existing data stack

It simply adds a workflow and documentation layer on top.

Key features

Structured analysis stages with checklists
Versioned markdown files (Git-friendly)
Quick mode (single file) and Full mode (multi-stage)
A/B experiment workflows
Metric monitoring with alert logic
Search across past analyses
Impact tracking (recommendation → outcome)

Why I built it

After using it for a while, I noticed a few unexpected benefits:

I can reopen an analysis months later and understand the reasoning instantly
Checklists catch things I used to skip (confounders, counter-metrics)
PMs and engineers started running their own quick analyses
Decisions feel more defensible because assumptions are explicit

It basically turned AI from an “answer generator” into a thinking partner.

How it works in practice

Typical workflow:

Initialize in your repo
Start a new analysis
Move through the ALIVE stages
Archive when complete
Search or review later

Everything lives as markdown in your project, so it becomes a long-term knowledge base instead of lost chat history.

Who this is for

Data analysts who want more rigor
Engineers and PMs doing lightweight analysis
Teams using AI agents for decision support
Anyone who wants a traceable reasoning process

What I’m looking for feedback on

I’d love to hear from people doing real analysis work:

Does this workflow match how you actually think?
What steps feel missing or unnecessary?
Would you use something like this in a team setting?

Brutally honest feedback is very welcome 🙏

Project

👉 GitHub: https://github.com/with-geun/alive-analysis

Quick start, examples, and templates are all available in the repo.

If you’ve been using AI for analysis, I’d especially love to know:

👉 What’s the biggest friction you still feel in your workflow?

DEV Community: Eddie