De la autor
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.
Instalare
npx skills add BioTender-max/awesome-bio-agent-skills --skill spacenet -a claude-code
Se instalează în .claude/skills/spacenet/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Fără descărcare: licența acestui skill (Personalizată sau neclară) nu ne permite să-i redistribuim fișierele. Comenzile de instalare le iau direct de pe GitHub.
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Verificări de siguranță pe SKILL.md
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De ce 74
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- svmBioTender-max/awesome-bio-agent-skillsUse 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.Altele★ 206
- kmeansBioTender-max/awesome-bio-agent-skillsUse 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.Altele★ 206
- bntBioTender-max/awesome-bio-agent-skillsUse 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.Altele★ 206
- filteringBioTender-max/awesome-bio-agent-skillsUse 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.Altele★ 206
- model-selectionAperivue/medsci-skillsUse when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.Știință★ 349
- icaBioTender-max/awesome-bio-agent-skillsUse 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.Altele★ 206
Note și recenzii
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