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 neuroimaging-decodinggit 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/neuroimaging-decoding)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/neuroimaging-decoding"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/neuroimaging-decoding/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/neuroimaging-decoding"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/neuroimaging-decoding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00099 | $0.00927 |
| Opus 5 | $0.00049 | $0.00464 |
| Sonnet 5 | $0.00020 | $0.00185 |
| Haiku 4.5 | $0.00010 | $0.00093 |
Grade A, and why
neuroimaging-decoding 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neuroimaging Decoding Workflow
Overview
neuroimaging-decoding coordinates three complementary analyses:
| Mode | Input | Scientific output |
|---|---|---|
mvpa |
ROI/parcel feature CSV | cross-validated prediction |
roi-glm |
ROI feature CSV + design CSV | ROI-wise effect and FDR table |
searchlight |
aligned NIfTI images + mask | voxel-wise decoding map |
Use nilearn-tool for full first-level and second-level task-fMRI GLM design.
This skill handles the downstream ROI or SearchLight analysis.
Installation
pip install numpy pandas scipy scikit-learn statsmodels nilearn nibabel
Workflows
1. ROI MVPA
python skills/neuroimaging-decoding/scripts/train_reference.py \
--mode mvpa \
--features roi_features.csv \
--target diagnosis \
--subject-col subject_id \
--task classification \
--model svm \
--folds 5 \
--output-dir run_models_output/mvpa
The tabular estimator choices are inherited from statistical-ml. Scaling and
feature selection must remain inside cross-validation.
2. ROI-wise GLM
roi_features.csv contains subject ID plus ROI columns. design.csv contains
the same subject ID plus intercept/covariate/contrast columns.
python skills/neuroimaging-decoding/scripts/train_reference.py \
--mode roi-glm \
--features roi_features.csv \
--design design.csv \
--subject-col subject_id \
--contrast-index 1 \
--output-dir run_models_output/roi_glm
The output includes effect, standard error, P value, and FDR-corrected Q value for every ROI.
3. Voxel-wise SearchLight
Create images.txt with one aligned NIfTI path per line. The row order must
match the labels CSV.
python skills/neuroimaging-decoding/scripts/train_reference.py \
--mode searchlight \
--images-list images.txt \
--features labels.csv \
--target condition \
--mask group_mask.nii.gz \
--folds 5 \
--output-dir run_models_output/searchlight
All images and the mask must share the same space, affine, and voxel grid.
What ships with it
1 file 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.
- 9d ago First seen · 142 lines · 99 tokens per session scan A c23ccc5353fe
neuroimaging-decoding is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 6d ago), licensed MIT. It adds 99 tokens to every session and 927 once invoked, about $0.0005 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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