dipy-tool
Use this skill whenever any NeuroClaw diffusion MRI / DWI modality skill needs to execute concrete DIPY operations: load DWI (NIfTI+bvals+bvecs), optional masking, DTI fitting, compute FA/MD/AD/RD, and extract ROI statistics. This is the dedicated base/tool skill that contains all specific DIPY code and usage patterns. Never called directly by the user.
npx skills add BioTender-max/awesome-bio-agent-skills --skill dipy-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.
# DIPY Tool (Base/Tool Layer) ## Overview `dipy-tool` is the **NeuroClaw base/tool skill** that provides the concrete **DIPY** implementation for diffusion MRI (DWI/DTI) processing and feature extraction. It is **never called directly by the user**. It is delegated to by a diffusion modality-layer skill (e.g., future `dwi-skill` / `dmri-skill`) and executed via `claw-shell` for safety, logging, and long-running stability. This skill provides: - Robust loading of **DWI NIfTI + bvals + bvecs** with sanity checks. - Brain mask generation (`median_otsu`) or use of a provided mask. - **DTI fitting** (optionally selecting a b-value range) and metric export: - **FA / MD / AD / RD** as NIfTI maps - **ROI / atlas statistics** extraction (CSV summaries). **Research use only** — not for clinical diagnosis. ## Agent Reference Rule When the agent needs DIPY-based implementation code, it should first consult the curated snippets in `skills/dipy-tool/scripts/` instead of copying the large embedded wrapper or unrelated tutorial files with hard-coded paths. Reference snippets available: - `scripts/load_and_mask_reference.py` -> DWI + gradients loading, b0 discovery, `median_otsu` brain masking - `s
- Overview
- Agent Reference Rule
- Quick Reference (Core Tasks)
- Curated Reference Scripts
- scripts/loadandmaskreference.py
- scripts/dtimetricsreference.py
- scripts/roistatsreference.py
- Installation (Handled by dependency-planner)
- NeuroClaw recommended wrapper
- Example execution (must be routed via claw-shell)
- Important Notes & Limitations
- Benchmark Adapter Guidance
- Complementary / Related Skills
- Reference
python skills/dipy-tool/scripts/load_and_mask_reference.py \ python skills/dipy-tool/scripts/dti_metrics_reference.py \ python skills/dipy-tool/scripts/roi_stats_reference.py \ conda create -n neuroclaw-dipy python=3.11 -y conda activate neuroclaw-dipy conda install -c conda-forge dipy nibabel numpy scipy scikit-image pandas -y conda install -c conda-forge matplotlib -y conda run -n neuroclaw-dipy python skills/dipy-tool/dipy_pipeline.py \
What does the dipy-tool skill do?
Use this skill whenever any NeuroClaw diffusion MRI / DWI modality skill needs to execute concrete DIPY operations: load DWI (NIfTI+bvals+bvecs), optional masking, DTI fitting, compute FA/MD/AD/RD, and extract ROI statistics. This is the dedicated base/tool skill that contains all specific DIPY code and usage patterns. Never called directly by the user.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill dipy-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.