dwi-skill skillB
dwi-skill is agent-read markdown (skill) from cuhk-aim-group/neurodiscovery: Use this skill whenever the user wants to preprocess diffusion MRI / DWI data, compute diffusion metrics (FA/MD/AD/RD, etc.), extract ROI-wise diffusion features, or run tractography/connectome-related workflows. Triggers include: 'DWI', 'DTI', 'diffusion MRI', 'FA', 'MD', 'AD', 'RD', 'eddy', 'topup', 'QSIPrep', 'tractography', 'connectome', 'TBSS', 'white matter microstructure'. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# DWI Skill (Modality Layer) ## Overview `dwi-skill` is the NeuroClaw **modality-layer** interface skill responsible for diffusion MRI (DWI/DTI) preprocessing and feature extraction. It strictly follows NeuroClaw hierarchical design principles: - This skill defines **WHAT needs to be done** and **which tool skill to delegate to**. - It contains **no implementation code** and **no concrete shell commands**. - All concrete execution is delegated to tool skills and routed through `claw-shell`.- Reference implementations (MATLAB, Python) are provided for user understanding but should be wrapped via tool skills in production workflows. **Core workflow (never bypassed):** 1. Identify input type (DICOM / NIfTI / BIDS), single-shell vs multi-shell, reverse phase-encoded b0/fieldmaps availability. 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 to the relevant tool skill via `claw-shell`. 5. Save outputs into `dwi_output/`. …
Read the whole file at its exact version.
How to install
mdr add cuhk-aim-group/neurodiscovery/dwi-skill@git:20260506.86420c0mdr add cuhk-aim-group/neurodiscovery/dwi-skill@sha256:13ccfa76ed2db0fdPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_3jedpy3a4gdh2zsz)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260506.86420c0 latest | 2026-05-06 | 86420c0 | 22,740 B | B | view · diff |
| git:20260505.e9ddd22 | 2026-05-05 | e9ddd22 | 22,748 B | B | view · diff |
| git:20260430.becfb09 | 2026-04-30 | becfb09 | 22,587 B | B | view · diff |
| git:20260407.c40d70f | 2026-04-07 | c40d70f | 15,799 B | B | view |
Audit of the latest version
- fail: No curl or wget piped into a shell (matched: curl -Ls https://fsl.fmrib.ox.ac.uk/fsldownloads/fslconda/releases/getfsl.sh | sh)
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (22740 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
cuhk-aim-group/neurodiscovery · 91 stars · license MIT · pushed 2026-09-22 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_3jedpy3a4gdh2zsz GET https://markdownregistry.com/api/v1/resolve?ref=cuhk-aim-group/neurodiscovery/dwi-skill GET https://markdownregistry.com/api/v1/blob/13ccfa76ed2db0fd521ce76578fdccc0d09c2021aac292addca153f2f852607e
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