smri-skill
Use this skill whenever the user wants to process structural MRI (sMRI) such as T1w/T2w/FLAIR for brain extraction, bias correction, tissue segmentation (GM/WM/CSF), registration to MNI, cortical/subcortical parcellation, cortical thickness/volumetry (FreeSurfer), HCP-style structural preprocessing, WMH lesion segmentation (FLAIR+T1), ROI-wise feature extraction, or converting results back to DICOM. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills.
npx skills add BioTender-max/awesome-bio-agent-skills --skill smri-skill --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.
# sMRI Skill (Modality Layer) ## Overview `smri-skill` is the NeuroClaw **modality-layer** interface skill responsible for **structural MRI** processing (T1w/T2w/FLAIR) and feature extraction. It strictly follows NeuroClaw hierarchical design principles: - This skill describes **WHAT needs to be done** and **which tool skill to delegate to**. - It contains **no implementation code** and **no direct shell commands**. - All concrete execution is delegated to tool skills and routed through `claw-shell`. **Core workflow (never bypassed):** 1. Identify input type (DICOM / NIfTI / BIDS), modalities available (T1w only vs T1w+T2w vs T1w+FLAIR). 2. Generate a **numbered execution plan** (steps, tools, outputs, runtime, risks). 3. Present the plan and wait for explicit user confirmation (“YES” / “execute” / “proceed”). 4. On confirmation, delegate each step via `claw-shell`. 5. Save outputs into a clean folder structure (`smri_output/`). ## Benchmark-Facing Default Mainline For benchmark-style structural MRI tasks, start from the narrowest valid anatomical mainline and only add optional branches when the prompt or inputs explicitly require them. - If the task is full structural MRI processi
- Overview
- Benchmark-Facing Default Mainline
- Quick Reference (Common sMRI Tasks → Delegation Map)
- Recommended Strategy (Decision Logic)
- FreeSurfer Setup & Prerequisites (Ubuntu)
- System Dependencies & Installation
- When to Use Each Stage
- Standard Output Layout (Recommended)
- Safety / Execution Rules (NeuroClaw)
- Important Notes & Limitations
- When to Call This Skill
- Post-Execution Verification (Harness Integration)
- Complementary / Related Skills
- Reference
sudo apt-get update sudo apt-get install -y \ tcsh bc perl tar libgomp1 build-essential \ wget vim-common libxmu-dev libxi-dev libxt-dev \ libx11-dev libglu1-mesa-dev libjpeg62-dev export FREESURFER_HOME=/usr/local/freesurfer export SUBJECTS_DIR=/path/to/your/freesurfer/subjects source $FREESURFER_HOME/SetUpFreeSurfer.sh
What does the smri-skill skill do?
Use this skill whenever the user wants to process structural MRI (sMRI) such as T1w/T2w/FLAIR for brain extraction, bias correction, tissue segmentation (GM/WM/CSF), registration to MNI, cortical/subcortical parcellation, cortical thickness/volumetry (FreeSurfer), HCP-style structural preprocessing, WMH lesion segmentation (FLAIR+T1), ROI-wise feature extraction, or converting results back to DICOM. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill smri-skill --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.