Analysis updated 2026-05-18
Set up a metric registry so an AI agent always uses one authoritative definition for each number
Add an independent read-only validator agent to check an AI analyst's SQL and reports
Build a self-growing rule library that captures each corrected mistake for future use
| fafa-ai-data-lab/ai-data-analyst-agent | aa2448208027-code/localaihotswap | aaronparton2-sketch/swell-event | |
|---|---|---|---|
| Stars | 39 | 39 | 39 |
| Language | — | Python | — |
| Setup difficulty | moderate | moderate | — |
| Complexity | 2/5 | 3/5 | — |
| Audience | data | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Templates are placeholders, you must fill in your own tables, metric definitions, and rules before it is useful.
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.
A methodology and template kit that turns an AI coding agent into a trustworthy data analyst using metric registries and independent validation.
Free to use, modify, and sell, with no warranty from the author (MIT license).
Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.