Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawnpx agentmods add skills/cuhk-aim-group/neuroclaw/dipy-toolWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/dipy-tool)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/dipy-tool"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/dipy-tool/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/dipy-tool"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/dipy-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.02413 |
| Opus 5 | $0.00043 | $0.01207 |
| Sonnet 5 | $0.00017 | $0.00483 |
| Haiku 4.5 | $0.00009 | $0.00241 |
Grade A, and why
dipy-tool scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_otsubrain maskingscripts/dti_metrics_reference.py-> tensor fitting and FA/MD/AD/RD exportscripts/roi_stats_reference.py-> atlas-based summary statistics on tensor metrics
Quick Reference (Core Tasks)
| Task | What it does | Output |
|---|---|---|
| Load DWI + gradients | Validates shapes, loads NIfTI+bvals+bvecs | in-memory arrays |
| Brain mask | Auto mask (median_otsu) or use external | brain_mask.nii.gz |
| DTI fit | TensorModel fit on selected volumes | tensor fit object |
| Export tensor metrics | Compute & save FA/MD/AD/RD | FA.nii.gz, MD.nii.gz, AD.nii.gz, RD.nii.gz |
| ROI stats | Per-label summary (mean/median/std/p05/p95) | roi_stats_FA.csv, etc. |
Curated Reference Scripts
These scripts are aligned with NeuroClaw's DWI handling pattern and with the modality / dependency expectations documented in rs-fMRI-Pipeline-Tutorial/:
- the tutorial explicitly includes DTI/DWI as a supported modality
- the tutorial installs
dipyas a core dependency - the tutorial's multimodal structure motivates deterministic outputs and atlas-based summaries
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 239 lines · 86 tokens per session scan A 5f31f6e2f44f
dipy-tool is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 4d ago), licensed MIT. It adds 86 tokens to every session and 2,413 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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