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 statistical-mlgit 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/statistical-ml)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/statistical-ml"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/statistical-ml/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/statistical-ml"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/statistical-ml.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 | $0.00142 | $0.01150 |
| Opus 5 | $0.00071 | $0.00575 |
| Sonnet 5 | $0.00028 | $0.00230 |
| Haiku 4.5 | $0.00014 | $0.00115 |
Grade A, and why
statistical-ml 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical ML Skill
Overview
statistical-ml is the shared NeuroClaw implementation for classical prediction
and inference on subject-level tabular features. It keeps imputation, scaling,
feature selection, and model fitting inside each training fold.
Supported estimators
| Task | Models |
|---|---|
| Classification | logistic, ridge, elastic_net, svm, xgboost |
| Regression | ols, ridge, elastic_net, svr, xgboost |
| Inference | Cohen's d, robust formula OLS/GLM, linear mixed-effects models |
| Longitudinal treatment analysis | dose/time/treatment mixed-effects formulas |
Use this skill after imaging data have been converted into one row per subject
or observation. Use temporal-models for sequence tensors and
cnn3d for full 3D volumes.
Installation
Core dependencies are installed with NeuroClaw:
pip install numpy pandas scipy scikit-learn statsmodels joblib
XGBoost is optional:
pip install xgboost
Verify imports:
python -c "import sklearn, statsmodels; print('Statistical ML OK')"
Workflows
1. Prepare a tabular CSV
The CSV must contain a subject identifier, target, and numeric features:
subject_id,site,diagnosis,age,roi_001,roi_002,network_fc
sub-001,A,0,24,0.12,-0.04,0.31
sub-002,B,1,31,0.08,-0.09,0.27
Identifier, target, and optional group columns are excluded from predictors.
2. Classification
python skills/statistical-ml/scripts/train_reference.py \
--features features.csv \
--target diagnosis \
--subject-col subject_id \
--group-col site \
--model logistic \
--task classification \
--folds 5 \
--output-dir run_models_output/logistic
Use --group-col site or another cohort column when sites must not be split
between training and test folds.
3. Regression
python skills/statistical-ml/scripts/train_reference.py \
--features features.csv \
--target cognitive_score \
--model ridge \
--task regression \
--folds 5 \
--output-dir run_models_output/ridge
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 · 172 lines · 142 tokens per session scan A 5389ed66a154
statistical-ml is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 6d ago), licensed MIT. It adds 142 tokens to every session and 1,150 once invoked, about $0.0007 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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