glm
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill glm --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# GLM Model Doc ## 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: - preprocessed task BOLD image - events TSV/CSV with onset, duration, trial type - repetition time (`TR`) - optional confounds TSV - optional mask image These should be prepared before model fitting. If not ready, delegate t
- Overview
- Quick Start
- 1) Prepare task-fMRI inputs
- 2) Typical first-level GLM flow
- 3) Second-level GLM (group-level inference)
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use GLM Instead of Deep Learning
- Limitations and Notes
- Reference
delegated through claw-shell after preprocessing is confirmed python skills/nilearn-tool/scripts/task_glm_reference.py \ delegated through claw-shell after subject-level contrasts are prepared python skills/nilearn-tool/scripts/second_level_glm_reference.py \
What does the glm skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill glm --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.