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 cpmgit 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/cpm)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/cpm"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/cpm/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/cpm"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/cpm.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 | $0.00060 | $0.00660 |
| Opus 5 | $0.00030 | $0.00330 |
| Sonnet 5 | $0.00012 | $0.00132 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
cpm 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 10d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CPM Skill
Overview
cpm is the canonical NeuroClaw implementation of Connectome Predictive
Modeling. Edge selection is repeated independently inside every training fold.
| Task | Input | Output |
|---|---|---|
| Classification | subject FC matrices/vectors | class and probability |
| Regression | subject FC matrices/vectors | continuous prediction |
Installation
pip install numpy pandas scipy scikit-learn joblib
Workflows
1. Prepare data
connectomes.npz:
X: [subjects, nodes, nodes] or [subjects, edges]
subject_id: [subjects]
labels.csv contains the same subject IDs and a target column.
2. Regression
python skills/cpm/scripts/train_reference.py \
--connectomes connectomes.npz \
--labels labels.csv \
--target cognitive_score \
--subject-col subject_id \
--task regression \
--p-threshold 0.01 \
--folds 5 \
--output-dir run_models_output/cpm
3. Classification
python skills/cpm/scripts/train_reference.py \
--connectomes connectomes.npz \
--labels labels.csv \
--target diagnosis \
--task classification \
--p-threshold 0.01 \
--output-dir run_models_output/cpm_classification
If p-threshold is tuned, use nested validation or training-only selection.
Input / Output Summary
| Item | Format |
|---|---|
| Connectomes | .npz with X, subject_id |
| Labels | CSV keyed by subject ID |
| Predictions | predictions.csv |
| Fold membership | fold_assignments.csv |
| Metrics | metrics.json |
| Fold models | checkpoint.joblib |
| Provenance | config.json, run_manifest.json |
Testing
pytest models/tests/test_extended_models.py -q
python skills/cpm/scripts/train_reference.py --help
Directory Reference
models/cpm/
├── cpm.py fold-local CPM estimator
└── train.py cross-validated CLI
skills/cpm/
├── SKILL.md
└── scripts/train_reference.py
Reference
- Finn et al. functional connectome fingerprinting and connectome-based prediction framework, Nature Neuroscience (2015).
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.
- 10d ago First seen · 123 lines · 60 tokens per session scan A 67395d54be17
cpm is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 660 once invoked, about $0.0003 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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