MCP App StoreMCP App StoreThis skill provides a model-level guidance for performing resting-state network decomposition using Independent Component Analysis (ICA). It enables the extraction of intrinsic connectivity networks, spatial component maps, and subject-level time series from preprocessed resting-state fMRI data.
Activate this skill when the goal is to identify intrinsic brain networks from rs-fMRI, extract subject-specific time series for downstream clustering, or perform unsupervised decomposition where interpretability of spatial networks is prioritized over phenotype prediction.
fmri-skill and concrete execution to nilearn-tool via claw-shell.Designed for agents within the NeuroClaw ecosystem or any research agent equipped with Python execution capabilities and the Nilearn library.
This skill has not been reviewed by our automated audit pipeline yet.
IBGNN (Interpretable Brain GNN)
PyG-based interpretable graph neural network for connectome (fMRI) phenotype prediction, with MPConv message MLP and support for post-hoc edge-mask explanation.
ASL Skill (Arterial Spin Labeling)
Modality-layer skill for Arterial Spin Labeling (ASL) perfusion MRI processing: preprocessing, M0 normalization, CBF quantification, PVC, and QC.
IBGNN (Interpretable Brain GNN)
PyG-based interpretable graph neural network for connectome (fMRI) phenotype prediction, with MPConv message MLP and support for post-hoc edge-mask explanation.
Scikit-learn Reference — Classical ML Workflows
Authoritative guidance for classical machine learning with scikit-learn: preprocessing, pipelines, model selection, evaluation, and example scripts for producti
ToolUniverse — Neuroscience Research
A neuroscience research skill that directs agents to verify facts via PubMed/Allen/WormBase, perform computational neuroscience calculations in Python, and reas