Claude Skills by BioTender-max

Use this skill whenever the user wants to pull, run, build, compose, list, prune, manage, or otherwise handle Docker containers, images, volumes, networks, or Docker Compose projects, or when a NeuroClaw skill (e.g. wmh-segmentation, freesurfer-processor in container mode) requires a clean, isolated, GPU-enabled Docker environment (e.g. 'pull mars-wmh image', 'run container with GPU', 'docker compose up', 'prune unused images', 'build custom dockerfile', 'manage nvidia docker'). Triggers incl...

Use this skill whenever the user wants to preprocess diffusion MRI / DWI data, compute diffusion metrics (FA/MD/AD/RD, etc.), extract ROI-wise diffusion features, or run tractography/connectome-related workflows. Triggers include: 'DWI', 'DTI', 'diffusion MRI', 'FA', 'MD', 'AD', 'RD', 'eddy', 'topup', 'QSIPrep', 'tractography', 'connectome', 'TBSS', 'white matter microstructure'. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills.

Use this skill whenever the user wants to load, preprocess, epoch, filter, or extract features from EEG data (resting-state, task-based, BCI, clinical, motor imagery, emotion, epilepsy, fatigue, etc.). Triggers include: 'eeg', 'EEG preprocessing', 'EEG feature extraction', 'band power', 'downsample to frequency bands', 'motor imagery BCI', 'emotion EEG', 'epilepsy detection', or any request involving .set/.edf/.bdf/.fif/.bids files.

Use this skill whenever the user wants to execute experiments based on a finalized method and record results. Triggers include: 'run experiment', 'experiment controller', 'implement experiment', 'run model', 'execute training', 'experiment-controller', 'record results', 'ablation study', or any request to turn METHOD.md into concrete runs and output to EXPERIMENT.md. This skill is the **mandatory interface-layer experiment executor** in NeuroClaw: it searches literature/GitHub for matching ex...

Use this model doc whenever the user wants to perform neuroimaging signal denoising with classical temporal filtering methods. This is a non-deep-learning preprocessing route focused on temporal cleaning, frequency selection, and preparation of cleaner time series for downstream analysis.

Use this model doc whenever the user wants to run FM-APP for phenotype prediction using fMRI ROI features and optional sMRI features. This document provides model-level usage and delegates preprocessing to fmri-skill and smri-skill.

Use this skill whenever the user wants to perform fMRI preprocessing, first-level analysis, ROI extraction, functional connectivity, effective connectivity, or atlas-based alignment to MNI152 space using either fMRIPrep, HCP-style pipelines, or CONN Toolbox. Triggers include: 'fmri', 'fMRI analysis', 'functional connectivity', 'effective connectivity', 'ROI extraction', 'seed-based correlation', 'PPI', 'DCM', 'atlas alignment', 'MNI152', 'HCP pipeline', 'CONN toolbox', or any request involvin...

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 process structural MRI data (T1w, T2w, FLAIR, etc.) with FreeSurfer, especially for cortical/subcortical segmentation, surface reconstruction, parcellation, cortical thickness, volume statistics, or full recon-all pipeline. Triggers include: 'freesurfer', 'recon-all', 'segment MRI', 'FreeSurfer processing', 'cortical segmentation', 'subcortical segmentation', 'run recon-all', 'freesurfer T1', 'process brain MRI with freesurfer', 'aseg aparc', or any r...

Use this skill whenever the user wants to process neuroimaging data with FSL (FMRIB Software Library), covering structural MRI, functional MRI (fMRI), and diffusion MRI (dMRI/DTI). Triggers include: 'use FSL', 'FSL processing', 'fsl_anat', 'FEAT', 'MELODIC', 'eddy', 'bedpostx', 'probtrackx', 'BET', 'FAST', 'FLIRT', 'FNIRT', 'run FSL pipeline'. This skill is the NeuroClaw interface-layer wrapper for FSL: checks installation, generates execution plan with concrete shell commands, waits for expl...

Essential Git commands and workflows for version control, branching, and collaboration.

Advanced git operations beyond add/commit/push. Use when rebasing, bisecting bugs, using worktrees for parallel development, recovering with reflog, managing subtrees/submodules, resolving merge conflicts, cherry-picking across branches, or working with monorepos.

Use this model doc whenever the user wants to run a classical General Linear Model (GLM) for task-evoked fMRI activation analysis. This is a non-deep-learning model route focused on design matrices, first-level/second-level statistics, and statistical maps.

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...

Core harness library providing standardized self-verification, checkpoint management, drift detection, and audit logging utilities for all NeuroClaw skills. This is NOT directly called by users; instead, it is imported as a Python module by other skills for harness-compliant execution, validation, and reproducibility. Use this as a foundation/plugin SDK when building or enhancing other skills. Triggers: none (library import only). This skill provides: HarnessController class, VerificationRunn...

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 to perform high-quality, HCP-style preprocessing of multimodal MRI data (structural, functional, diffusion) using the official HCP Pipelines. Triggers include: 'HCP pipeline', 'HCP preprocessing', 'hcp-fmri', 'hcp-dwi', 'hcp-structural', 'MSMAll', 'ICA-FIX', 'bedpostx', 'probtrackx', or any request to run the Human Connectome Project preprocessing pipelines.

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 perform brain parcellation using Hierarchical clustering. This is a non-deep-learning unsupervised route focused on multi-scale parcel discovery, voxel or vertex grouping, and atlas-like region generation from neuroimaging features.

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 model doc whenever the user wants to perform resting-state network decomposition using ICA. This is a non-deep-learning unsupervised route focused on extracting intrinsic connectivity networks, component maps, and subject-level time series from resting-state fMRI.

Use this skill whenever the user wants an end-to-end workflow for the IXI (Information eXtraction from Images) dataset, including data download, BIDS organization, and multimodal processing of T1w, T2w, and MRA. Triggers include: 'IXI', 'IXI dataset', 'process IXI data', 'IXI MRI', or any request to run the IXI multimodal pipeline.

Use this model doc whenever the user wants to perform brain parcellation using K-means. This is a non-deep-learning unsupervised route focused on parcel discovery, voxel or vertex grouping, and atlas-like region generation from neuroimaging features.

Use this skill when users need to build, populate, or extend a domain-specific knowledge graph from literature and structured databases. Triggers include: 'build knowledge graph', 'extract claims from papers', 'ingest data into graph', 'batch extract claims', 'knowledge graph construction', 'populate graph from PubMed', 'extract structured claims', 'ingest atlas data', or any request involving knowledge graph population from scientific literature or biomedical databases. Covers both structure...

Use this model doc whenever the user wants to run LG-GNN (Local-to-Global GNN) for fMRI phenotype prediction. LG-GNN is a PyG-based GNN with SABP (Self-Attention Brain Pooling) and mutual-information regularization. NeuroClaw adapts the original population-graph version to single-subject brain graphs.

Use this skill whenever the user wants to process MEG (magnetoencephalography) data including source localization, time-frequency analysis, connectivity analysis, sensor-level preprocessing, or MEG-specific feature extraction. Triggers include: 'MEG', 'MEG processing', 'MEG source localization', 'MEG connectivity', 'magnetoencephalography', 'beamformer', 'time-frequency', 'MEG preprocessing', or any request involving MEG data files (.fif, .con, .ds).

Use this skill whenever the user wants to formalize a network architecture and derive theoretical components from a research idea. Triggers include: 'method design', 'design method', 'network architecture', 'formula derivation', 'method-design', 'theoretical framework', 'derive equations', or any request to transform IDEA.md into a detailed METHOD.md. This skill is the **mandatory interface-layer method formalizer** in NeuroClaw: it reads IDEA.md, designs concrete network structures (layers, ...

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 any NeuroClaw modality skill (especially eeg-skill) needs to execute concrete MNE-Python operations for EEG loading, preprocessing, filtering, artifact removal, epoching, frequency-band analysis, or feature extraction. This is the dedicated base/tool skill that contains all specific MNE-Python code and usage patterns.

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.

Multi search engine integration with 17 engines (8 CN + 9 Global). Supports advanced search operators, time filters, site search, privacy engines, and WolframAlpha knowledge queries. No API keys required.

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-Bra...

Use this skill whenever NeuroClaw needs concrete nibabel operations for neuroimaging files: loading and validating NIfTI images, inspecting shapes and affine matrices, saving derived images, converting voxel coordinates to MNI/world coordinates, or reading FreeSurfer geometry and annotation files. Triggers include: 'nibabel', 'inspect NIfTI', 'read affine', 'save nifti', 'voxel to MNI', 'atlas coordinates', 'read FreeSurfer surface', 'read annot', or any request focused on low-level neuroimag...

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 to convert NIfTI files (.nii or .nii.gz) to DICOM format, create DICOM series from processed neuroimaging results, write segmentation/registration/analysis outputs back to DICOM for PACS compatibility or clinical viewer comparison, or transfer metadata from reference DICOM files. Triggers include: mentions of 'NIfTI to DICOM', 'nii to dcm', 'convert nii.gz to DICOM', 'dicomify segmentation', 'nii2dcm', 'bring results back to DICOM', 'create DICOM from NI...

Use this skill whenever any NeuroClaw fMRI modality skill needs to execute concrete Nilearn operations: ROI/atlas time-series extraction, confounds handling (fMRIPrep), seed-based connectivity maps, ROI-to-ROI connectivity matrices, and optional GLM/decoding utilities. This is the dedicated base/tool skill that contains Nilearn usage patterns and lightweight wrappers. Never called directly by the user.

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 OASIS (Open Access Series of Imaging Studies) dataset, including BIDS validation, multimodal processing of sMRI, and phenotype extraction for aging and Alzheimer's disease research. Triggers include: 'OASIS', 'OASIS-1', 'OASIS-2', 'OASIS-3', 'process OASIS data', 'Alzheimer', or any request to run the OASIS pipeline.

Use this skill whenever the user wants to synchronize NeuroClaw-generated LaTeX manuscripts with Overleaf, read/write .tex files, download/upload projects, create/rename/archive projects, compare versions, or manage project structure. Triggers include: 'sync to Overleaf', 'upload paper to Overleaf', 'Overleaf project', 'LaTeX sync', 'push draft', 'download Overleaf', 'create Overleaf project', 'tex file to Overleaf', or any request involving paper_draft.tex / collaboration. This skill is the ...

Use this skill whenever the user wants to generate a full academic paper draft from existing research materials. Triggers include: 'write paper', 'generate manuscript', 'draft paper', 'paper-writing', 'hierarchical drafting', 'manuscript composer', 'create LaTeX paper', 'write research paper from IDEA METHOD EXPERIMENT', or any request to transform IDEA.md + METHOD.md + EXPERIMENT.md into a typeset-ready manuscript. This skill is the **mandatory interface-layer writer** in NeuroClaw: it stric...

Use this skill whenever the user wants to process PET neuroimaging data including spatial normalization to T1w/MNI space, SUVR computation, reference region quantification, partial volume correction, or tracer-specific workflows (PiB amyloid, FDG metabolism, tau). Triggers include: 'PET', 'PET processing', 'SUVR', 'amyloid PET', 'FDG PET', 'tau PET', 'PiB', 'flortaucipir', 'reference region', 'partial volume correction', or any request involving PET neuroimaging data.

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 rou...

Use this skill whenever the user wants to generate or refine a research idea through literature search and discussion. Triggers include: 'research idea', 'brainstorm idea', 'generate idea', 'research-idea', 'idea generation', 'discuss new direction', or any request to explore literature and output to IDEA.md. This skill is the **mandatory interface-layer idea generator** in NeuroClaw: it calls networking search skills to retrieve recent papers, identifies gaps/trends, then iteratively discuss...

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 to run phenotype-prediction models, browse model cards, map model inputs/outputs, or choose an execution route for fMRI/sMRI based models. This is a model-entry orchestration skill: it routes requests to model-specific docs and delegates preprocessing to modality skills.