adni-skill
Use this skill whenever the user wants an end-to-end workflow for ADNI data (fMRI + T1), including BIDS preparation, fMRIPrep preprocessing, and DK68 ROI pipeline. This is the NeuroClaw dataset-orchestration layer for ADNI.
npx skills add BioTender-max/awesome-bio-agent-skills --skill adni-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.
# ADNI Skill (Dataset-Orchestration Layer) ## Overview `adni-skill` is the NeuroClaw orchestration skill for ADNI subject-level fMRI + T1 workflows. It supports two distinct usage modes: 1. A narrow ADNI raw NIfTI -> BIDS staging path. 2. A full downstream ADNI workflow path (BIDS + fMRIPrep + DK68 ROI extraction). It coordinates a fixed two-stage pipeline: 1. Prepare ADNI data into BIDS and run fMRIPrep. 2. Run DK68 ROI extraction with QC. It also provides an **optional VQA generation path** for VLM use cases: - Reorganize ADNI data and convert DICOM to NIfTI. - Generate task labels (task1-task5). - Generate VQA pairs from task outputs. This skill follows NeuroClaw hierarchy: - Defines **WHAT to do**, not low-level implementation details. - Does **not** execute direct shell commands itself. - Delegates all execution via `claw-shell` to tool skills. **Research use only.** --- ## Narrow Path: ADNI Raw NIfTI -> BIDS Staging Use this path when the task only asks to reorganize raw ADNI NIfTI files into a BIDS-style dataset and does not require preprocessing, ROI extraction, VQA generation, EEG handling, or DICOM conversion. ### When this narrow path should dominate - The task objective
- Overview
- Narrow Path: ADNI Raw NIfTI -> BIDS Staging
- When this narrow path should dominate
- Narrow-path contract
- Expected narrow-path behavior
- Important restriction for narrow staging tasks
- Core Workflow (Never Bypassed)
- Input Layout (Example)
- BIDS Preparation (Stage A-C)
- Stage A: Prepare BIDS root metadata
- Stage B: Create BIDS directories
- Stage C: Copy and rename NIfTI + JSON
- fMRIPrep Stage (Stage D)
- Typical Docker run
mkdir -p bids/sub-130S0969/ses-M00/anat mkdir -p bids/sub-130S0969/ses-M00/func cp "nifti/130_S_0969/T1/"*.nii.gz \ cp "nifti/130_S_0969/T1/"*.json \ cp "nifti/130_S_0969/fMRI/"*.nii.gz \ cp "nifti/130_S_0969/fMRI/"*.json \ docker run --rm -it \ python run_dk68_pipeline_qc.py \ python reorganize_adni.py --cmd dcm2niix python reorganize_adni.py --cmd dcm2niix --cleanup
What does the adni-skill skill do?
Use this skill whenever the user wants an end-to-end workflow for ADNI data (fMRI + T1), including BIDS preparation, fMRIPrep preprocessing, and DK68 ROI pipeline. This is the NeuroClaw dataset-orchestration layer for ADNI.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill adni-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.