qsiprep-tool
Use this skill whenever the user wants to run QSIPrep (BIDS App) for diffusion MRI (DWI) preprocessing with best-practice workflows (topup/eddy, denoising/unringing options, susceptibility/motion correction, coregistration/normalization, QC reports) on BIDS datasets. This skill is the NeuroClaw interface-layer wrapper for QSIPrep: it checks installation (Docker/Singularity/conda), generates an execution plan with exact commands and resource estimates, waits for explicit confirmation, then routes all execution through claw-shell.
npx skills add BioTender-max/awesome-bio-agent-skills --skill qsiprep-tool --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.
# QSIPrep Tool (Interface Layer) ## Overview QSIPrep is a BIDS-App pipeline for **diffusion MRI (DWI) preprocessing** that emphasizes: - Robust distortion/motion/eddy-current correction - Interoperable derivatives for downstream modeling (DTI/DKI/CSD, tractography, connectome, etc.) - Strong QC reporting (HTML) This skill is the **NeuroClaw interface-layer wrapper** for QSIPrep and strictly follows the NeuroClaw safety pattern: 1. Check whether QSIPrep is available (preferred: Docker/Singularity image; alternative: conda). 2. If missing → invoke `dependency-planner` to produce an installation plan. 3. Verify inputs (must be BIDS-compliant; detect DWI + fieldmaps/reverse-PE b0 if present). 4. Generate a clear numbered plan with **exact commands**, runtime/resource estimates, and risks. 5. Wait for explicit user confirmation (“YES” / “execute” / “proceed”). 6. On confirmation → delegate all commands to `claw-shell`. 7. Summarize outputs (derivatives paths + QC report location) and suggest next steps. **Research use only.** --- ## What QSIPrep Typically Does (High-Level) - Validates BIDS layout (or skips if requested) - Creates brain mask(s) - Denoising (optional), Gibbs unringing (op
- Overview
- What QSIPrep Typically Does (High-Level)
- Quick Reference
- Installation (Handled by dependency-planner)
- Common Command Templates (Executed via claw-shell)
- A) Docker (Recommended on workstations)
- B) Singularity / Apptainer (Recommended on HPC)
- NeuroClaw recommended wrapper script (Reference): qsiprepwrapper.py
- Important Notes & Limitations
- When to Call This Skill
- Post-Execution Verification (Harness Integration)
- Complementary / Related Skills
- Reference
docker --version docker image ls | grep -i qsiprep or apptainer --version apptainer exec qsiprep.sif qsiprep --version mkdir -p "$OUT_DIR" "$WORK_DIR" docker run --rm -t \ apptainer run --cleanenv \
What does the qsiprep-tool skill do?
Use this skill whenever the user wants to run QSIPrep (BIDS App) for diffusion MRI (DWI) preprocessing with best-practice workflows (topup/eddy, denoising/unringing options, susceptibility/motion correction, coregistration/normalization, QC reports) on BIDS datasets. This skill is the NeuroClaw interface-layer wrapper for QSIPrep: it checks installation (Docker/Singularity/conda), generates an execution plan with exact commands and resource estimates, waits for explicit confirmation, then routes all execution through claw-shell.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill qsiprep-tool --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.