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Data & Analytics · NVIDIA AI Platform · For developers
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Set up the NVIDIA "Build an Agent" DevX workshop as a working JupyterLab environment from INSIDE a locked-down OpenShell/NemoClaw sandbox, and hand the user the token URL + access commands. | brevdev/ | 146 | — | ~5.2k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 2 | Verify a new CV-CUDA operator against the deterministic final regression checklist (the /make-op done-gate). | CVCUDA/ | 2.7k | — | ~433 | Automated safety check: Pass | Unknown | 24 days ago |
| 3 | Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse… | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 4 | Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via docker run … gapanalysis vcnaoi … — picks the… | NVIDIA/ | 3.6k | — | ~4.3k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 5 | Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). | NVIDIA/ | 3.6k | — | ~3.8k | Automated safety check: Notes | Apache-2.0 | yesterday |
| 6 | GPU optimization for consumer NVIDIA GPUs (8-24GB VRAM) covering mixed precision, gradient checkpointing, XGBoost GPU, CuPy/cuDF migration, and torch.compile. | Mathews-Tom/ | 329 | — | ~3.5k | Automated safety check: Notes | MIT | 5 days ago |
| 7 | Audit, prepare, and deploy PAIDF Orchestration on a Kubernetes GPU cluster - single-GPU H100/L40S hosts, managed Kubernetes, kubeadm, and similar. | NVIDIA/ | 3.6k | — | ~3.8k | Automated safety check: Warn | Apache-2.0 | yesterday |
