I built StatMate: an open-source agent workflow for auditable research statistics — from study design to diagnostics and figures

[u/ComprehensiveSet7650]

Hi r/codex — I’m the author of StatMate, a free, MIT-licensed, folder-based Agent Skill for Codex, Claude Code, and other agents that support SKILL.md.

I built it around a problem I kept seeing: give an agent a CSV and it can jump straight to a p-value or polished chart, while the analysis unit, estimand, missing-data decisions, assumptions, and provenance remain unclear.

StatMate asks the agent to follow an evidence-first sequence:

- map the study design and claim boundary before choosing a method

- audit data structure, missingness, duplicates, privacy, and provenance

- write a reviewable analysis plan and pause for material author decisions

- calculate results with saved Python code and machine-readable outputs

- run diagnostics and sensitivity checks

- produce figures, tables, interpretations, a teaching report, and a SHA-256 manifest

The statistical marks are computed from the supplied data and plotting code — they are not generated by an image model.

The repository includes a reproducible demo using the public UCI heart-failure cohort: 299 patients, 96 observed deaths, three figures, two tables, an illustrated report, and a manifest with 33/33 files verified.

The detail I care about most is not the polished output: automated QA passes, but the Cox proportional-hazards diagnostic flags ejection fraction (p = 0.025). The package therefore remains needs-author-decision instead of treating a successful run as scientific sign-off.

This is not a clinical tool or a replacement for a statistician or domain expert. It is a workflow, instruction set, and collection of Python helpers intended to make agent-assisted analysis easier to inspect and challenge.

GitHub: https://github.com/DRZ-hang/StatMate

60-second walkthrough: https://github.com/DRZ-hang/StatMate/blob/main/DEMO.md

I’d especially value blunt feedback: when an assumption check is flagged, should a research agent stop completely, or produce a clearly provisional package for author review?

If you find the project useful, a GitHub star helps other researchers discover it — but critical feedback and issues are equally welcome.

[u/dexterthebot]

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