Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 79 870 files from 1 769 authors, of which 62 217 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
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 structured data ingestion (Phase 1) and LLM-based claim extraction from papers (Phase 2).
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, modules, connections), performs mathematical derivations (equations, loss functions, proofs), and always outputs a structured METHOD.md.
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-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 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 neuroimaging I/O rather than full preprocessing.
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 NIfTI', 'nii to dicom series', or any request to take post-processed neuroimaging results (segmentation, registration, bias field correction, synthesis, etc.) and store/view them alongside original patient DICOM data. Also use when modality-specific metadata (especially MR, SVR) or preservation of patient/study information from a reference DICOM is needed. Do NOT use for the reverse conversion (DICOM to NIfTI), non-medical imaging file conversions, or any clinical diagnostic or treatment-related workflows.
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 **mandatory interface-layer LaTeX collaborator** in NeuroClaw: it strictly enforces pull-first workflow with diff reporting and per-operation user authorization for any write/create/delete action, uses pyoverleaf (cookie-based), preserves Overleaf version history, integrates directly after paper-writing, and never performs unauthorized modifications.
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 strictly follows the hierarchical manuscript drafting and iterative refinement process (section 4.4 + provided flowchart), never generates the full paper in one shot, saves every intermediate step as a separate file, and produces either clean plain-text or LaTeX output.
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 routes all execution through claw-shell.
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 discusses with the user to finalize a structured idea, always saving the result as IDEA.md.
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.
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 after a task succeeds (user confirms 'success' or tools report success). It extracts new experience from the session log + daily memory, locates relevant skills, and proposes diff-formatted updates to SKILL.md files with a clear summary for the user.
Use this skill whenever the user wants to process structural MRI (sMRI) such as T1w/T2w/FLAIR for brain extraction, bias correction, tissue segmentation (GM/WM/CSF), registration to MNI, cortical/subcortical parcellation, cortical thickness/volumetry (FreeSurfer), HCP-style structural preprocessing, WMH lesion segmentation (FLAIR+T1), ROI-wise feature extraction, or converting results back to DICOM. This is the NeuroClaw modality-layer interface: it plans WHAT to do and delegates execution to tool skills.
Use this model doc whenever the user wants to perform disease classification with SpaceNet. This is a non-deep-learning supervised route focused on voxel-wise neuroimaging-based case-control prediction with sparse and interpretable weight maps.
Use this model doc whenever the user wants to perform disease classification with SVM. This is a non-deep-learning supervised route focused on neuroimaging-based case-control prediction from ROI-wise or tabular features.
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.
Use this skill whenever the user wants to analyze already available UK Biobank data for brain-related research, including neurological outcomes, cognitive phenotypes, brain MRI derived phenotypes, survival analysis, subgroup analysis, propensity score analysis, mediation analysis, sensitivity analysis, machine learning, visualization, or manuscript-ready summaries. This skill only covers post-extraction analysis and explicitly excludes RAP access and data download guidance.
Use this skill whenever the user wants to perform automated white matter hyperintensity (WMH) segmentation on structural MRI data using the MARS-WMH nnU-Net model. Requires one FLAIR and one T1w NIfTI image (no contrast). Triggers include: 'wmh', 'white matter hyperintensities', 'WMH segmentation', 'MARS-WMH', 'wmh-nnunet', 'segment FLAIR T1', 'white matter lesions', 'vascular WMH', 'mars wmh', or any request to run nnU-Net WMH segmentation on FLAIR+T1w pair.
| 2021年诺贝尔生理学或医学奖得主，PIEZO1/PIEZO2机械力感受器发现者。 以功能性筛选策略鉴定全新的离子通道家族，揭示了触觉、本体感觉等机械力转导的分子基础。 触发词：「Patapoutian」「PIEZO」「mechanosensation」「mechanotransduction」「压力感受器」「触觉分子机制」。 信息源：诺奖官网、Nature/Science/Cell论文、PNAS/Quanta Magazine/Kavli Prize、Scripps/HHMI官方资料。 调研时间：2026-04-06。
2009年诺贝尔生理学或医学奖得主Carol W. Greider的智慧蒸馏——端粒酶发现者、分子生物学家、女性科学倡导者
| 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」。
| David Julius认知框架蒸馏 — 2021年诺贝尔生理学或医学奖得主，温度与触觉受体发现者。 以"自然界的分子工具"为核心方法论，从辣椒素出发开创了整个疼痛感知研究领域。 适用于：科学探索方法论、逆向工程思维、从日常现象发现深层机制的决策框架。 触发词：「Julius视角」「分子工具思维」「从现象到机制」「辣椒素范式」
| 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。
| Jack W. Szostak（2009年诺贝尔生理学或医学奖得主）思维框架。 端粒与端粒酶的共同发现者，RNA世界假说/生命起源领域的领军人物。 核心镜片：化学还原论+跨学科碰撞+问题选择艺术。 适用场景：科研方向选择、跨学科创新、从化学第一性原理思考生命问题、科研诚信决策。
Claude Skills — questions
Answers built from the skills we actually parsed.