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NVIDIA AI Platform · NVIDIA/skills
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 193 | How to swap the VLM in the VSS Alerts Blueprint — covers RTVI-VLM microservice deployment methods, all three VLM consumers (rtvi-vlm, vlm-as-verifier, vss-agent), and health checks. | NVIDIA/ | 3.6k | — | ~5k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 194 | Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). | NVIDIA/ | 3.6k | — | ~4.5k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 195 | Deploy and operate the RTVI-CV-3D microservice as MV3DT (MODE=mv3dt): per-camera DeepStream perception plus BEV Fusion over calibrated cameras. | NVIDIA/ | 3.6k | — | ~4.8k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 196 | Build Holoscan SDK from source via the in-tree ./run script. | NVIDIA/ | 3.6k | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 197 | Bootstrap a custom carrier board by forking carrier files and scaffolding a DT overlay from the reference devkit. | NVIDIA/ | 3.6k | — | ~4.2k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 198 | Build a per-target knowledge-base markdown next to the active profile by walking the BSP root and source tree. | NVIDIA/ | 3.6k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 199 | Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile. | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 200 | Entry skill for Jetson / IGX BSP customization. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 201 | Switch the active Jetson target-platform pointer to an existing profile YAML. | NVIDIA/ | 3.6k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 202 | Guide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings. | NVIDIA/ | 3.6k | — | ~4.8k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 203 | Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. | NVIDIA/ | 3.6k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 204 | Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer. | NVIDIA/ | 3.6k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 205 | Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules. | NVIDIA/ | 3.6k | — | ~3.5k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 206 | Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP. | NVIDIA/ | 3.6k | — | ~3.5k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 207 | Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification. | NVIDIA/ | 3.6k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 208 | Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification. | NVIDIA/ | 3.6k | — | ~973 | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 209 | Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM… | NVIDIA/ | 3.6k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 210 | MoE expert-parallel communication overlap in Megatron Bridge. | NVIDIA/ | 3.6k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 211 | Long-context MoE training guidance for Megatron Bridge. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 212 | Evidence-gated workflow for MoE performance optimization in Megatron Bridge. | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 213 | Practical guidance for training MoE VLMs in Megatron Bridge. | NVIDIA/ | 3.6k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 214 | Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration. | NVIDIA/ | 3.6k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 215 | Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints. | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 216 | Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification. | NVIDIA/ | 3.6k | — | ~924 | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 217 | Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine. | NVIDIA/ | 3.6k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 218 | A skill your agent uses when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS… | NVIDIA/ | 3.6k | — | ~2.8k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 219 | Kubernetes execution platform — submits TAO container jobs as k8s Jobs with NVIDIA GPU scheduling; single-pod for one node, Indexed Jobs for multi-node distributed training. | NVIDIA/ | 3.6k | — | ~4.9k | Automated safety check: Warn | Apache-2.0 | 2 days ago |
| 220 | Expert cuTile programming assistant. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~5k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 221 | Test system for Megatron-LM. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 222 | Mod or remaster a game with RTX Remix - open and edit projects, swap textures and models. | NVIDIA/ | 3.6k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 223 | One-time session setup and orchestration map for the TAO skill bank. | NVIDIA/ | 3.6k | — | ~1.8k | Automated safety check: Warn | Apache-2.0 | 2 days ago |
| 224 | Linting and formatting for Megatron-LM. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | — | ~406 | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 225 | Bind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block. | NVIDIA/ | 3.6k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 226 | 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 | 2 days ago |
| 227 | The data-mover for TAO jobs — decides the storage tier (A pre-positioned mount with zero fetch / B volume-from-S3 / C ephemeral in-compute fetch), stages inputs (bulk + annotation-selective +… | NVIDIA/ | 3.6k | — | ~1.5k | Automated safety check: Warn | Apache-2.0 | 2 days ago |
| 228 | The Docker execution platform for TAO jobs — a local daemon or a remote GPU box via DOCKERHOST=ssh://user@host. | NVIDIA/ | 3.6k | — | ~5k | Automated safety check: Warn | Apache-2.0 | 2 days ago |
| 229 | Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups. | NVIDIA/ | 3.6k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | 2 days ago |