Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CUHK-AIM-Group/NeuroClaw --skill nii2dcmgit clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawWrote 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/nii2dcm)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/nii2dcm"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/nii2dcm/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/nii2dcm"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/nii2dcm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
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 →
- high YARA Match · line 39 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium MCP Rug Pull · line 62 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 64 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00230 | $0.01583 |
| Opus 5 | $0.00115 | $0.00792 |
| Sonnet 5 | $0.00046 | $0.00317 |
| Haiku 4.5 | $0.00023 | $0.00158 |
Grade A, and why
nii2dcm 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NIfTI to DICOM conversion
Overview
A NIfTI file (.nii/.nii.gz) is a compact format widely used in neuroimaging research, typically stripped of patient metadata.
DICOM is the clinical standard for medical images, including rich metadata and interoperability with PACS/hospital systems.
This skill wraps nii2dcm (v0.1.6, May 2025) to convert NIfTI volumes into single-frame DICOM series (multi-slice 2D), primarily for MRI-derived data.
It supports modality-specific metadata (MR, SVR) and optional metadata transfer from a reference DICOM file.
Research use only — not certified for clinical diagnosis, treatment, or patient care.
Quick Reference
| Task | Approach / Command Flag |
|---|---|
| Basic conversion (generic) | nii2dcm input.nii.gz output_dir/ |
| MRI multi-slice series | --dicom-type MR or -d MR |
| SVR (3D swept volume recon) | --dicom-type SVR or -d SVR |
| Copy patient/study metadata | --ref-dicom ref.dcm or -r ref.dcm |
| Custom series description | Add via wrapper or post-process |
| Verify output | Open in Horos, 3D Slicer, ITK-Snap |
Installation
Via pip (recommended for NeuroClaw)
pip install nii2dcm>=0.1.6
# or latest
pip install git+https://github.com/tomaroberts/nii2dcm.git
From source (for customization / debugging)
git clone https://github.com/tomaroberts/nii2dcm.git
cd nii2dcm
python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install .
nii2dcm -h # Verify
Core dependencies (automatically installed):
- highdicom >= 0.9.0
- SimpleITK >= 2.2.0
- pydicom
- numpy
Docker alternative (if preferred in containerized env):
docker pull ghcr.io/tomaroberts/nii2dcm:latest
docker tag ghcr.io/tomaroberts/nii2dcm:latest nii2dcm
docker run nii2dcm -v # check version
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.
- 9d ago First seen · 143 lines · 230 tokens per session scan A 2d06e9d6e188
nii2dcm is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 6d ago), licensed MIT. It adds 230 tokens to every session and 1,583 once invoked, about $0.0011 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 2026-09-03.
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paper-compile
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