Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torch_geometric / PyG. Also trigger when you see imports from torch_geometric, or the user mentions graph convolutions (GCN, GAT, GraphSAGE, GIN), graph data structures, or working with relational/network data. Even if the user just says 'graph learning' or 'geometric deep learning', use this skill.
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Real-time hand detection in egocentric videos using victordibia/handtracking. Outputs bounding boxes for hands, specifically trained on EgoHands dataset. Supports video input/output with labeled hand boxes. Lightweight and fast for egocentric view applications.
Use this skill whenever the user wants an end-to-end workflow for the ABCD Study dataset, including download via NIMH Data Archive, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'ABCD Study', 'ABCD data', 'process ABCD', 'ABCD fMRI', 'ABCD sMRI', 'ABCD diffusion', or any request to run the ABCD multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for ABCD.
Use this skill whenever the user wants an end-to-end workflow for the ABIDE (Autism Brain Imaging Data Exchange) dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ABIDE', 'ABIDE data', 'process ABIDE', 'ABIDE fMRI', 'ABIDE sMRI', 'autism imaging', or any request to run the ABIDE pipeline. This is the NeuroClaw dataset-orchestration layer for ABIDE.
Use this skill whenever the user wants an end-to-end workflow for the ADHD-200 dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ADHD-200', 'ADHD200', 'process ADHD data', 'ADHD fMRI', or any request to run the ADHD-200 pipeline. This is the NeuroClaw dataset-orchestration layer for ADHD-200.
Use this skill whenever the user wants an end-to-end workflow for ADNI data (fMRI + T1), including BIDS preparation, fMRIPrep preprocessing, and DK68 ROI pipeline. This is the NeuroClaw dataset-orchestration layer for ADNI.
Use this skill whenever the user wants an end-to-end workflow for the AIBL (Australian Imaging, Biomarkers and Lifestyle) dataset, including data access guidance, BIDS organization, and multimodal processing of sMRI and PET (PiB, FDG, tau). Triggers include: 'AIBL', 'AIBL data', 'process AIBL', 'AIBL PET', 'AIBL MRI', or any request to run the AIBL multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for AIBL.
Use this skill whenever the user wants an end-to-end workflow for the AOMIC (Amsterdam Open MRI Collection) dataset, including data access, BIDS organization, and multimodal processing of sMRI, rs-fMRI, and task-fMRI. Triggers include: 'AOMIC', 'AOMIC data', 'process AOMIC', 'AOMIC fMRI', 'AOMIC resting state', or any request to run the AOMIC multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for AOMIC.
Use this skill whenever the user wants to automatically organize raw neuroimaging data (DICOM, NIfTI, EEG, etc.) into a valid BIDS (Brain Imaging Data Structure) dataset. Triggers include: 'organize to BIDS', 'BIDS organizer', 'convert to BIDS', 'BIDS conversion', 'bidsify', 'create BIDS dataset', 'raw data to BIDS', or any request to structure data according to BIDS specification.
Use this model doc whenever the user wants to run BrainNetworkTransformer for fMRI phenotype prediction, including data loading, training, and evaluation. BNT uses dense FC matrices (no PyG dependency) with DEC pooling + interpretable transformer encoder.
Use this model doc whenever the user wants to run BrainGNN for fMRI phenotype prediction, including graph construction, training, and evaluation. This document focuses on model-level usage and delegates upstream preprocessing to fmri-skill (and optionally hcpya-skill for HCP data).
Use this skill whenever the user wants an end-to-end workflow for the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, task-fMRI, and MEG, phenotype extraction, and QC integration. Triggers include: 'Cam-CAN', 'CamCAN', 'process Cam-CAN data', 'Cam-CAN MEG', 'Cam-CAN fMRI', or any request to run the Cam-CAN multimodal pipeline.
Use this skill whenever the user wants to create, activate, list, export, update, clone, remove, or otherwise manage conda environments, or when a deep-learning / model skill requires a clean, isolated conda environment (e.g. 'create conda env for torch 2.3 cuda', 'export current env to yml', 'list all my conda envs', 'update packages in neuroclaw-dl', 'remove old env', 'clone env for reproducibility', 'install pytorch in new env'). Triggers include: 'conda create', 'conda env', 'make new environment', 'export yml', 'activate env', 'conda list envs', 'update conda env', 'clean environment', 'reproduce env', 'conda remove'. This skill is the mandatory gatekeeper for conda operations: it ALWAYS plans first, shows commands + risks + best practices, and waits for explicit user confirmation before executing anything.
Use this skill whenever the user wants an end-to-end workflow for the DMT-HAR-MED dataset (ds006644), including download, BIDS organization, and processing of rs-fMRI data from a psychedelic intervention study. Triggers include: 'DMT-HAR-MED', 'DMT HAR MED', 'ds006644', 'process DMT data', 'psychedelic fMRI', or any request to run the DMT-HAR-MED pipeline. This is the NeuroClaw dataset-orchestration layer for DMT-HAR-MED.
Use this skill whenever the user wants to perform standardized preprocessing of functional MRI (fMRI) and anatomical MRI data using fMRIPrep. Triggers include: 'fmriprep', 'fMRIPrep', 'fMRI preprocessing', 'BIDS fMRI', 'run fmriprep', 'preprocess bold', 'BOLD preprocessing', 'anatomical preprocessing', or any request involving BIDS-organized fMRI datasets.
Use this skill whenever the user wants to remove site/scanner/batch effects from neuroimaging features before running downstream models, run mega-analysis across multiple datasets, or evaluate models with leave-site-out / site-stratified protocols. Triggers include: 'harmonize', 'ComBat', 'CovBat', 'site effect', 'scanner effect', 'batch effect', 'leave-site-out', 'mega-analysis', 'multi-site', 'cross-site', 'neuroHarmonize'. This is a horizontal cross-cutting layer between dataset skills and model skills.
Use this skill whenever the user wants an end-to-end workflow for the Healthy Brain Network (HBN) dataset, including download, BIDS organization, and multimodal processing of sMRI, dMRI, rs-fMRI, task-fMRI, and EEG data. Triggers include: 'HBN', 'Healthy Brain Network', 'process HBN', 'HBN fMRI', 'HBN EEG', or any request to run the HBN multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for HBN.
Use this skill whenever the user wants an end-to-end workflow for the HCP Aging (HCP-A) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Aging', 'HCP-A', 'process HCP Aging data', 'HCP Aging sMRI fMRI', or any request to run the HCP-A multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the HCP Development (HCP-D) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Development', 'HCP-D', 'process HCP Development data', 'HCP Development sMRI fMRI', or any request to run the HCP-D multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the HCP Early Psychosis (HCP-EP) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Early Psychosis', 'HCP-EP', 'process HCP Early Psychosis data', 'HCP EP sMRI fMRI', or any request to run the HCP-EP multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the HCP Young Adult (HCP-YA / HCP1200) dataset, including dataset download, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'HCP Young Adult', 'HCP-YA', 'HCP1200', 'process HCP data', 'HCP sMRI fMRI DTI', or any request to run the HCP-YA multimodal pipeline.
Use this model doc whenever the user wants to run IBGNN (Interpretable Brain Graph Neural Network) for fMRI phenotype prediction. IBGNN is a PyG-based GNN with a learnable MLP message function over [x_i, x_j, edge_attr], designed for connectome-based brain disorder analysis with post-hoc edge-mask explainer support.
Use this skill whenever the user wants an end-to-end workflow for the Motor Neuron Disease (MND) dataset from OpenNeuro ds005874, including BIDS validation, multimodal processing of rs-fMRI and task-fMRI, phenotype extraction, and QC integration. Triggers include: 'MND', 'Motor Neuron Disease', 'ALS', 'Amyotrophic Lateral Sclerosis', 'process MND data', 'MND fMRI', or any request to run the MND multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the Longitudinal MS Lesion Segmentation Challenge dataset, including data validation, multimodal processing of T1w, T2w, FLAIR, and PD, lesion segmentation, and QC integration. Triggers include: 'MS Lesion Challenge', 'MS Lesion', 'ISBI MS', 'longitudinal MS', 'multiple sclerosis lesion', or any request to run the MS lesion segmentation pipeline.
Use this skill whenever the user wants to run the NeuroSTORM multi-model fMRI platform: preprocessing, pretraining (MAE or contrastive), fine-tuning, inference, or benchmarking. It covers 8 built-in models — NeuroSTORM, SwiFT, BrainGNN, BrainNetworkTransformer (BNT), LG-GNN, Com-BrainTF, IBGNN, BrainNetCNN — across 3 input modalities (voxel 4D, ROI time series 2D, functional connectivity 2D). Triggers include: 'fMRI', 'NeuroSTORM', 'SwiFT', 'BrainGNN', 'BNT', 'BrainNetCNN', 'LG-GNN', 'Com-BrainTF', 'IBGNN', 'fMRI preprocessing', 'fMRI foundation model', 'ROI time series', 'functional connectivity', 'brain graph', 'HCP', 'ABCD', 'UKB', 'ADHD200', 'COBRE', 'UCLA', 'NSD', 'BOLD5000', 'disease diagnosis from fMRI', 'pretrain fMRI model', 'fine-tune fMRI', or any request involving .nii/.nii.gz fMRI volume files.
Use this skill whenever the user wants an end-to-end workflow for the Neuroimaging in Frontotemporal Dementia (NIFD) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'NIFD', 'frontotemporal dementia', 'FTD', 'bvFTD', 'PPA', 'process NIFD data', or any request to run the NIFD multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the Natural Scenes Dataset (NSD), including data access, BIDS validation, multimodal processing of task-fMRI and structural MRI, stimulus metadata extraction, and QC integration. Triggers include: 'NSD', 'Natural Scenes Dataset', 'process NSD data', 'NSD fMRI', 'visual neuroscience', or any request to run the NSD multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the Philadelphia Neurodevelopmental Cohort (PNC) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, task-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'PNC', 'Philadelphia Neurodevelopmental Cohort', 'process PNC data', 'PNC fMRI', or any request to run the PNC multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the Parkinson's Progression Markers Initiative (PPMI) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'PPMI', 'Parkinson', 'Parkinson disease', 'process PPMI data', 'PPMI fMRI', or any request to run the PPMI multimodal pipeline.
Use this skill whenever the user wants to run QSIPrep (BIDS App) for diffusion MRI (DWI) preprocessing with best-practice workflows (topup/eddy, denoising/unringing options, susceptibility/motion correction, coregistration/normalization, QC reports) on BIDS datasets. This skill is the NeuroClaw interface-layer wrapper for QSIPrep: it checks installation (Docker/Singularity/conda), generates an execution plan with exact commands and resource estimates, waits for explicit confirmation, then routes all execution through claw-shell.
Use this skill whenever the user wants an end-to-end workflow for the REST-meta-MDD (Resting-State Meta-Major Depressive Disorder) dataset, including BIDS validation, processing of rs-fMRI, phenotype extraction, and QC integration. Triggers include: 'REST-meta-MDD', 'MDD', 'Major Depressive Disorder', 'depression resting-state', 'process REST-meta-MDD', or any request to run the REST-meta-MDD pipeline.
Use this skill whenever the user wants an end-to-end workflow for the SEED-IV (SJTU Emotion EEG Dataset - 4 emotions) dataset, including EEG validation, preprocessing, feature extraction, and emotion classification. Triggers include: 'SEED-IV', 'SEED4', 'emotion EEG', 'EEG emotion recognition', 'process SEED-IV', or any request to run the SEED-IV pipeline.
Use this skill whenever the user wants an end-to-end workflow for the SEED-VIG (SJTU Emotion EEG Dataset - Vigilance) dataset, including EEG validation, preprocessing, feature extraction, and vigilance/fatigue detection. Triggers include: 'SEED-VIG', 'SEEDVIG', 'vigilance EEG', 'fatigue detection', 'drowsiness EEG', 'process SEED-VIG', or any request to run the SEED-VIG pipeline.
Use this skill whenever the user wants an end-to-end workflow for the Transdiagnostic Connectome Project (TCP) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'TCP', 'Transdiagnostic Connectome', 'process TCP data', 'TCP fMRI', or any request to run the TCP multimodal pipeline.
Use this skill whenever the user wants an end-to-end workflow for the UCLA CNP (Consortium for Neuropsychiatric Phenomics) dataset, including BIDS validation, multimodal processing of sMRI, task-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'UCLA CNP', 'Consortium Neuropsychiatric Phenomics', 'process UCLA CNP', or any request to run the UCLA CNP multimodal pipeline.
| Craig C. Mello (2006年诺贝尔生理学或医学奖) 的思维框架与决策视角。 核心镜片：简单模型的力量、RNA作为信息货币、跨学科对话。 调研来源：诺奖官网、学术论文、STAT News、NBC News等一手素材。 触发词：「Mello视角」「RNAi思维」「简单模型思维」「基因沉默」「Mello怎么想」。
| 2025年诺贝尔生理学或医学奖得主Fred Ramsdell的思维框架。 聚焦：免疫耐受机制发现、从单基因突变到疾病治疗的全链条思维、工业界科研的价值。 调研来源：7篇一手论文 + Nobel Prize官方资料。信息量有限（极低调的科学家），心智模型基于有限推断。 触发词：FOXP3、Treg、免疫耐受、自身免疫、IPEX、Fred Ramsdell、诺贝尔医学奖2025。
| Robert G. Edwards (1925-2013) 的思维框架与决策模式。2010年诺贝尔生理学或医学奖得主，体外受精(IVF)之父。 基于12个一手/二手来源的深度调研，提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Edwards的视角分析问题——特别是在科学创新、伦理争议、长期主义和跨学科协作场景中。 当用户提到「用Edwards的视角」「IVF之父怎么看」「Edwards模式」「Robert Edwards perspective」时使用。
| 诺贝尔医学奖得主屠呦呦（Tu Youyou, 2015）的思维框架蒸馏。 核心镜片：从传统智慧中提取科学灵感的"古今转化"思维；低温突破决策。 警告：信息密度低（公开演讲/访谈极少），心智模型基于有限素材推断，诚实边界篇幅大。 触发词：「屠呦呦视角」「青蒿素思维」「古今转化」「传统中药现代化」。
Functional annotation and taxonomy inference from sequence homology.
Aggregate results, train ML models, and produce reports with validated references.
Use omicverse's pyComBat wrapper to remove batch effects from merged bulk RNA-seq or microarray cohorts, export corrected matrices, and benchmark pre/post correction visualisations.
Turn bulk RNA-seq cohorts into synthetic single-cell datasets using omicverse's Bulk2Single workflow for cell fraction estimation, beta-VAE generation, and quality control comparisons against reference scRNA-seq.
Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq, training beta-VAE and GNN models, and interpolating missing states.