glm skillA
glm is agent-read markdown (skill) from cuhk-aim-group/neurodiscovery: Use this model doc whenever the user wants to run a classical General Linear Model (GLM) for task-evoked fMRI activation analysis. This is a non-deep-learning model route focused on design matrices, first-level/second-level statistics, and statistical maps..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# GLM Model Doc Task-fMRI first/second-level GLM remains implemented through `nilearn-tool`. For tabular formula OLS, robust covariance, Cohen's d, mixed-effects, and prediction baselines, route to the `statistical-ml` skill. ## Overview GLM refers to the classical General Linear Model used for task-based fMRI activation analysis. - Model family: non-deep-learning statistical model - Typical objectives: - first-level GLM for subject/session-level task activation analysis - second-level GLM for group-level inference across subjects - Primary input: preprocessed task fMRI, events, TR, optional confounds, optional brain mask - Primary output: first-level contrast maps, second-level z maps, thresholded activation maps, region-level summaries In NeuroClaw, this document is model-level guidance for statistical activation workflows rather than phenotype prediction. Upstream preparation should usually be delegated to: - `fmri-skill` for task-fMRI preprocessing and confounds preparation - `nilearn-tool` for concrete GLM fitting, design matrix construction, and statistical map generation **Research use only.** --- ## Quick Start ### 1) Prepare task-fMRI inputs Expected inputs: …
Read the whole file at its exact version.
How to install
mdr add cuhk-aim-group/neurodiscovery/glm@git:20260825.cbd02ffmdr add cuhk-aim-group/neurodiscovery/glm@sha256:cfa251a02f0303d9Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_tmsv3bi76hynn6lh)
1 badge views in 30 days
Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260825.cbd02ff latest | 2026-08-25 | cbd02ff | 5,620 B | A | view · diff |
| git:20260505.e9ddd22 | 2026-05-05 | e9ddd22 | 5,408 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (5620 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
cuhk-aim-group/neurodiscovery · 91 stars · license MIT · pushed 2026-09-22 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_tmsv3bi76hynn6lh GET https://markdownregistry.com/api/v1/resolve?ref=cuhk-aim-group/neurodiscovery/glm GET https://markdownregistry.com/api/v1/blob/cfa251a02f0303d9d0760467afdab71841bf51c91591e6030f3346a0d6bb1856
Your agent 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.