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statistical-ml SKILL.md skillA

statistical-ml from cuhk-aim-group/neurodiscovery is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add cuhk-aim-group/neurodiscovery/statistical-ml@git:20260825.cbd02ff

Pins git:20260825.cbd02ff in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: cuhk-aim-group/neurodiscovery
Path: skills/statistical-ml/SKILL.md
Versions recorded: 1
Latest version: git:20260825.cbd02ff, committed Aug 25, 2026
Size: 4,643 bytes
Repository stars: 93
License: MIT

Use this skill whenever the user needs classical statistical inference or tabular machine learning for neuroimaging-derived features: OLS/GLM, Cohen's d, logistic regression, Ridge, Elastic Net, SVM/SVR, XGBoost, mixed-effects models, site-aware cross-validation, or imaging-by-treatment interactions. Triggers include 'statistical model', 'GLM', 'OLS', 'effect size', 'Cohen d', 'logistic regression', 'Ridge', 'Elastic Net', 'SVM', 'SVR', 'XGBoost', 'mixed effects', 'site split', 'tabular…

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Statistical ML Skill

## Overview

`statistical-ml` is the shared NeuroClaw implementation for classical prediction
and inference on subject-level tabular features. It keeps imputation, scaling,
feature selection, and model fitting inside each training fold.

**Supported estimators**

| Task | Models |
|---|---|
| Classification | `logistic`, `ridge`, `elastic_net`, `svm`, `xgboost` |
| Regression | `ols`, `ridge`, `elastic_net`, `svr`, `xgboost` |
| Inference | Cohen's d, robust formula OLS/GLM, linear mixed-effects models |
| Longitudinal treatment analysis | dose/time/treatment mixed-effects formulas |

Use this skill after imaging data have been converted into one row per subject
or observation. Use `temporal-models` for sequence tensors and
`cnn3d` for full 3D volumes.

---

## Installation

Core dependencies are installed with NeuroClaw:

```bash
pip install numpy pandas scipy scikit-learn statsmodels joblib
```

XGBoost is optional:

```bash
pip install xgboost
```

Verify imports:

```bash
python -c "import sklearn, statsmodels; print('Statistical ML OK')"
```

---

## Workflows

### 1. Prepare a tabular CSV
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add cuhk-aim-group/neurodiscovery/statistical-ml@sha256:5389ed66a1548997

The line at the top of this page pins the label git:20260825.cbd02ff, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_7bjgxd3wycz5d73m.svg)](https://markdownregistry.com/a/art_7bjgxd3wycz5d73m)

3 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Aug 25, 2026.

version	committed	commit	size	audit
git:20260825.cbd02ff latest	2026-08-25	cbd02ff	4,643 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (4643 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

cuhk-aim-group/neurodiscovery · 93 stars · license MIT · pushed 2026-10-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_7bjgxd3wycz5d73m
GET https://markdownregistry.com/api/v1/resolve?ref=cuhk-aim-group/neurodiscovery/statistical-ml
GET https://markdownregistry.com/api/v1/blob/5389ed66a15489979323fa6221699500630000990c61869a9c7022400a214523

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.