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 cnn3dgit 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/cnn3d)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/cnn3d"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/cnn3d/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/cnn3d"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/cnn3d.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 | $0.00092 | $0.00795 |
| Opus 5 | $0.00046 | $0.00398 |
| Sonnet 5 | $0.00018 | $0.00159 |
| Haiku 4.5 | $0.00009 | $0.00080 |
Grade A, and why
cnn3d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CNN3D Skill
Overview
cnn3d is NeuroClaw's canonical compact residual 3D CNN. It owns one model,
one NPZ input contract, and one checkpoint format. NeuroSTORM remains a
separate model skill with its own external repository and runtime.
| Model | Input | Tasks |
|---|---|---|
VoxelCNN3D |
whole-volume tensor | classification, regression |
Installation
pip install numpy torch scikit-learn pandas
Verify:
python -c "from models.cnn3d import VoxelCNN3D; print('CNN3D OK')"
Workflows
1. Prepare a volume NPZ
X: float array [subjects, channels, depth, height, width]
y: array [subjects]
subject_id: string array [subjects] (optional)
All subjects must use the same orientation, voxel size, grid, crop, and intensity-normalization protocol.
2. Classification
python skills/cnn3d/scripts/train_reference.py \
--input volumes.npz \
--task classification \
--base-channels 16 \
--dropout 0.1 \
--epochs 50 \
--batch-size 4 \
--folds 5 \
--device cuda \
--output-dir run_models_output/cnn3d
3. Regression
python skills/cnn3d/scripts/train_reference.py \
--input volumes.npz \
--task regression \
--base-channels 32 \
--epochs 100 \
--lr 0.0003 \
--weight-decay 0.0001 \
--output-dir run_models_output/cnn3d_regression
Preprocessing must be frozen before cross-validation. Site harmonization, augmentation, and intensity transforms must not use held-out subjects.
Input / Output Summary
| Item | Format |
|---|---|
| Input | .npz with X, y, optional subject_id |
| Predictions | predictions.csv |
| Fold membership | fold_assignments.csv |
| Metrics | metrics.json |
| Fold checkpoints | checkpoint.pt |
| Provenance | config.json, run_manifest.json |
Testing
pytest models/tests/test_extended_models.py -q
python skills/cnn3d/scripts/train_reference.py --help
Directory Reference
models/cnn3d/
├── net.py residual 3D CNN
└── train.py cross-validated trainer
skills/cnn3d/
├── SKILL.md
└── scripts/train_reference.py
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.
- 11d ago First seen · 132 lines · 92 tokens per session scan A f56f84503097
cnn3d is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 4d ago), licensed MIT. It adds 92 tokens to every session and 795 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 2026-08-30.
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