GLM (Task-fMRI) (Claude Skill)

Run a classical General Linear Model (GLM) for task-evoked fMRI activation analysis.

Type	Claude Skill
Supplier	CUHK-AIM-Group (community OSS, MIT)
Availability	GA — part of the NeuroClaw neuroimaging skill library
Pricing	Free / OSS (MIT)
Capabilities	Read/Write — Claude runs the skill’s Python locally (Bash), not as an MCP tool

How to install

• Claude Code — clone and copy the skill into your skills directory:

  git clone https://github.com/CUHK-AIM-Group/NeuroClaw
  cp -r NeuroClaw/skills/glm ~/.claude/skills/
  

  Project-scoped alternative: copy into .claude/skills/ instead. NeuroClaw skills assume the collection’s shared helpers (claw-shell, modality tool skills) and the upstream neuroimaging stack (FreeSurfer/FSL/fMRIPrep/etc.) — install those dependencies, or run the bundled installer for the full environment:

  cd NeuroClaw && python installer/setup.py
  

  which configures the Python env, CUDA/GPU, and the neuroimaging tools.

What it does

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.

Primary use cases: Run a classical General Linear Model (GLM) for task-evoked fMRI activation analysis.

Notes

Distributed as a SKILL.md (plus code examples) in the NeuroClaw skill library — Claude executes it locally via Bash/Python rather than as an MCP server. Upstream license: MIT. The skill directory upstream is skills/glm.

Sources

• CUHK-AIM-Group/NeuroClaw
• skills/glm/SKILL.md

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