ai-analyst-lab

ai-analyst-lab / ai-analyst

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AI Product Analyst — Claude Code-powered data analysis toolkit

75
15
100% credibility
Found Feb 22, 2026 at 12 stars 6x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI assistant that analyzes business data from natural language questions to produce validated insights, charts, narratives, and branded slide decks.

How It Works

1
🔍 Discover AI Analyst

You find a helpful tool that turns everyday business questions into clear answers and presentations.

2
📦 Get the sample data

Download ready-to-use store data to practice, or connect your own files easily.

3
💬 Start chatting with your AI

Open a simple conversation where the AI knows your data and waits for your questions.

4
❓ Ask your business question

Type something like 'Why is sales dropping?' and watch it explore, analyze, and build insights.

5
Choose quick peek or full story
📊
Quick chart

Get an instant visual answer with explanations.

📈
Full analysis

Launch everything for a complete report and deck.

🎉 Enjoy your ready deck

Receive beautiful slides with charts, story, notes, and action steps — ready to share.

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Star Growth

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AI-Generated Review

What is ai-analyst?

AI Analyst is a Python toolkit that turns business questions into complete data analyses and branded slide decks using Claude AI. Drop in CSV, DuckDB, Postgres, BigQuery, or Snowflake data; ask "Why is conversion dropping?" and it frames hypotheses, runs root-cause analysis, validates findings, builds narratives, and exports PDF/HTML decks—all in minutes via slash commands like /run-pipeline or /explore. Ships with a ready-to-query e-commerce dataset for instant testing, positioning it as a practical AI data analyst agent on GitHub.

Why is it gaining traction?

It skips manual SQL/charting drudgery with 17 parallel agents handling framing, exploration, validation, storytelling, and deck-building, plus auto-checks like Simpson's paradox detection and SWD-style charts. Developers love the DAG orchestration, resume-from-fail, and natural-language fallback—no memorizing commands. As an AI analyst tool, it delivers stakeholder-ready outputs faster than BI tools or custom scripts.

Who should use this?

Product managers chasing quick insights on metrics like retention or revenue drivers. Data analysts prototyping analyses without full pipelines. PMs or analysts in small teams filling AI analyst job gaps, especially for e-commerce funnels, cohort trends, or opportunity sizing.

Verdict

Promising AI analyst GitHub project for automating analysis-to-deck workflows (11 stars, 1.0% credibility score signals early maturity), with excellent docs and demo offsetting light tests. Try if you need an AI data analyst agent now; watch for production hardening.

(198 words)

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