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 brain-age-modelinggit 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/brain-age-modeling)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/brain-age-modeling"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/brain-age-modeling/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/brain-age-modeling"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/brain-age-modeling.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 | $0.00088 | $0.00906 |
| Opus 5 | $0.00044 | $0.00453 |
| Sonnet 5 | $0.00018 | $0.00181 |
| Haiku 4.5 | $0.00009 | $0.00091 |
Grade A, and why
brain-age-modeling 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain-Age Modeling Workflow
Overview
brain-age-modeling is a leakage-safe task workflow over NeuroClaw regression
estimators. It trains predicted-age models, fits age-bias correction on each
training fold, and exports held-out raw age, corrected age, and Brain-PAD.
Brain-PAD = bias-corrected predicted age - chronological age
Positive Brain-PAD indicates an older-appearing brain relative to chronological age under the fitted model; it is not by itself a diagnosis or causal effect.
Installation
pip install numpy pandas scipy scikit-learn joblib
Optional feature generators such as FreeSurfer, NeuroSTORM, or a 3D CNN are handled by their own skills before this tabular brain-age workflow.
Workflows
1. Prepare brain features
subject_id,site,age,cortical_thickness,hippocampal_volume,fc_001
sub-001,A,64,2.51,3810,0.12
sub-002,B,59,2.63,4022,0.08
Use a healthy training reference when the scientific interpretation requires deviation from normative aging. Do not include downstream disease outcomes as predictors.
2. Ridge brain-age model
python skills/brain-age-modeling/scripts/train_reference.py \
--features brain_features.csv \
--age-col age \
--subject-col subject_id \
--group-col site \
--model ridge \
--folds 5 \
--seed 123 \
--output-dir run_models_output/brain_age
3. Alternative regressors
The workflow reuses regression estimators from statistical-ml, including
ols, ridge, elastic_net, svr, and optional xgboost. Keep site,
family, or cohort groups intact where appropriate.
4. Downstream analysis
After held-out Brain-PAD has been generated, analyze group differences or clinical associations with explicit age, sex, site, intracranial-volume, and other prespecified covariates. Use only held-out Brain-PAD values.
Input / Output Summary
| Item | Format |
|---|---|
| Input | CSV with subject, chronological age, and numeric brain features |
| Optional grouping | site/cohort/family column |
| Predictions | predictions.csv |
| Prediction columns | raw age, corrected age, Brain-PAD |
| Fold membership | fold_assignments.csv |
| Metrics | raw and bias-corrected metrics in metrics.json |
| Checkpoint | predictor and corrector per fold in checkpoint.joblib |
| Provenance | config.json, run_manifest.json |
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 · 133 lines · 88 tokens per session scan A dce61d7dd227
brain-age-modeling is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 4d ago), licensed MIT. It adds 88 tokens to every session and 906 once invoked, about $0.0004 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.
Other skills, from other repositories
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
arboreto
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…
deepspot-m
Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
pick-a-pii-model
Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.