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what is ai-data-analyst-agent fr?

fafa-ai-data-lab/ai-data-analyst-agent — explained in plain English

Analysis updated 2026-05-18

39Audience · dataComplexity · 2/5LicenseSetup · moderate

tl;dr

A methodology and template kit that turns an AI coding agent into a trustworthy data analyst using metric registries and independent validation.

vibe map

mindmap
  root((AI Data Analyst Agent))
    What it does
      Metric registry first
      Independent validation
    Tech stack
      Markdown templates
      Claude Code compatible
    Use cases
      Trustworthy AI analysis
      Self-growing rules
    Audience
      Data teams
      AI agent builders

Code map

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filefunction / class

what do people make with this?

VIBE 1

Set up a metric registry so an AI agent always uses one authoritative definition for each number

VIBE 2

Add an independent read-only validator agent to check an AI analyst's SQL and reports

VIBE 3

Build a self-growing rule library that captures each corrected mistake for future use

how it stacks up fr

fafa-ai-data-lab/ai-data-analyst-agentaa2448208027-code/localaihotswapaaronparton2-sketch/swell-event
Stars393939
LanguagePython
Setup difficultymoderatemoderate
Complexity2/53/5
Audiencedatadevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 1h+

Templates are placeholders, you must fill in your own tables, metric definitions, and rules before it is useful.

Free to use, modify, and sell, with no warranty from the author (MIT license).

in plain english

This repository is a methodology and template kit for turning an AI coding agent, such as Claude Code or Cursor, into something that behaves like a careful, trustworthy data analyst rather than a chatbot that just answers questions from memory. It does not contain any real company data, table names, or business specific metric definitions. Instead, it shares the underlying structure that makes an AI data analyst dependable, and expects each team to fill in their own data and rules using the provided templates. The approach centers on four ideas. First, metric definitions come before any calculation: the agent is expected to look up a single authoritative definition of a metric from a shared registry rather than inventing one on the fly from conversation context. Second, an agent should never be the sole judge of its own work, because self checking is unreliable, so any SQL query or report the agent produces must be reviewed by a separate validator agent that only has read access and cannot make changes itself. Third, the project keeps a rule library that grows over time: whenever a mistake is corrected, a new numbered rule gets added so the same mistake is automatically caught the next time, and the validator is expected to flag situations its current rules do not yet cover. Fourth, every number delivered to a person should carry a short note explaining where its definition came from, how fresh the underlying data is, which source table it used, and who validated it. The repository includes a template for the agent's persona and instructions, a ready to use validator skill with a set of general purpose rules covering things like sanity checks on SQL results and A/B test methodology, a template for adding your own business specific rules, and guidance for setting up a metric registry. It deliberately leaves out real table names, real metric definitions, and specific past mistakes, since those are considered private to each organization. It is released under the MIT license, so it can be used, changed, and sold freely, with no warranty from the author.

prompts (copy fr)

prompt 1
Help me adapt persona/CLAUDE.template.md into a data analyst persona for my own agent
prompt 2
Explain how the independent validator skill in this repo checks an agent's SQL output
prompt 3
Show me how to set up a metric registry for my top 10 business metrics using this template
prompt 4
Walk me through adding a new rule to the data-validator rule library after a mistake

Frequently asked questions

what is ai-data-analyst-agent fr?

A methodology and template kit that turns an AI coding agent into a trustworthy data analyst using metric registries and independent validation.

What license does ai-data-analyst-agent use?

Free to use, modify, and sell, with no warranty from the author (MIT license).

How hard is ai-data-analyst-agent to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is ai-data-analyst-agent for?

Mainly data.

peek the repo → explain another one

This repo across BitVibe Labs

double-check against the repo, no cap.