All authors
Claude Skills by NVIDIA
github.com/NVIDIA445 skills0 installs889 views
- Jetson Optimize MemoryReclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.Votes: 0GitHub stars: 3,503
- Jetson PackagePick Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes; maps Orin SM 8.7 vs Thor SM 11.0 and JetPack-specific package choices.Votes: 0GitHub stars: 3,503
- Jetson Print Bsp InfoUse when you need to print Jetson BSP info (L4T version, board configs, rootfs state) from a Linux_for_Tegra root on the host PC. This is an example skill.Votes: 0GitHub stars: 3,503
- Jetson Print Device InfoUse when you need to print Jetson device info (module model, L4T version, kernel, OS version, current power mode) from a running Jetson target. This is an example skill.Votes: 0GitHub stars: 3,503
- Jetson Promote ImageUse to promote overlay files and built artifacts into the staged BSP image. Do NOT use to flash or build. Triggers: promote bsp image.Votes: 0GitHub stars: 3,503
- Jetson Quick StartEntry skill for Jetson / IGX BSP customization. Asks one core click-to-select setup questionnaire and passes prefilled answers to downstream setup skills.Votes: 0GitHub stars: 3,503
- Jetson Set TargetSwitch the active Jetson target-platform pointer to an existing profile YAML. Use before customize/build/flash to change target; not for authoring profiles — use jetson-init-target instead.Votes: 0GitHub stars: 3,503
- Jetson Speculative DecodingAdd EAGLE-3 or draft-model speculative decoding to a Jetson vLLM server when TPOT is the bottleneck.Votes: 0GitHub stars: 3,503
- Jetson Validate ImageUse after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.Votes: 0GitHub stars: 3,503
- Jetson Video BenchmarkUse when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.Votes: 0GitHub stars: 3,503
- Jetson Video CapabilityUse when Jetson codec, profile, chroma, bit-depth, dimension, engine-count, or operational support must be reconciled from live APIs, authenticated NVIDIA samples, and NVIDIA documentation; also applies the content-DRM scope.Votes: 0GitHub stars: 3,503
- Jetson Video PipelineUse when planning, executing, and independently validating Jetson Video Codec SDK or PyNvVideoCodec encode/decode, transcode, segmentation, container decode, AV1, or concise acceptance workflows.Votes: 0GitHub stars: 3,503
- Jetson Video RecipeUse when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.Votes: 0GitHub stars: 3,503
- Jetson Video SetupUse when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.Votes: 0GitHub stars: 3,503
- Medtech Model Evidence ExportExports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.Votes: 0GitHub stars: 3,503
- Nemo Automodel Distributed TrainingGuide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings.Votes: 0GitHub stars: 3,503
- Nemo Automodel Model OnboardingGuide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.Votes: 0GitHub stars: 3,503
- Nemo Automodel Recipe DevelopmentCreate and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.Votes: 0GitHub stars: 3,503
- Nemo Fabric Build AdapterBuild, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Use when creating adapter or target descriptors, mapping AgentConfig into an agent harness or custom-agent runtime, implementing start/invoke/stop, declaring schemas and capabilities, packaging discovery metadata, or assessing adapter conformance. Do not use for consumer applications that only call the NVIDIA NeMo Fabric SDK.Votes: 0GitHub stars: 3,503
- Nemo Fabric IntegrateUse this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig, choosing the single-invocation convenience API or an explicitly started runtime, validating with plan and doctor, and consuming normalized results, artifacts, and telemetry.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Mlm Bridge TrainingRun Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Activation RecomputeValidate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Cuda GraphsValidate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Expert Parallel OverlapValidate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Memory TuningTechniques 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 fixes.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Moe Comm OverlapMoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Moe Hardware ConfigsRepresentative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Moe Optimization WorkflowEvidence-gated workflow for MoE performance optimization in Megatron Bridge. Covers measurement contracts, the Three Walls framework, parallel folding, profiling, matched A/B tuning, and final validation.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Perf Sequence PackingValidate 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.Votes: 0GitHub stars: 3,503
- Nemo Mbridge Recipe RecommenderRecommend and customize Megatron Bridge library and benchmark recipes for a user's model, GPU count, hardware, sequence length, and pretrain/SFT/PEFT goal. Use when selecting a starting recipe, comparing library and benchmark configs, resizing parallelism for a GPU allocation, or distinguishing convergence changes, semantics-preserving execution tuning, and benchmark-only shortcuts.Votes: 0GitHub stars: 3,503
- Nemo Relay InstallUse this skill when choosing or running NeMo Relay installation for the CLI, Python, Node.js, Rust, OpenClaw, Hermes, or maintained framework integrations before runtime configuration or quick-start setup.Votes: 0GitHub stars: 3,503
- Nemo Relay Instrument Typed WrappersUse this skill when adding NeMo Relay typed wrappers, domain types, or provider codecs while preserving JSON middleware semantics and caller-visible behavior.Votes: 0GitHub stars: 3,503
- Nemo RetrieverUse when searching, extracting, ingesting, or querying a document collection with the NeMo Retriever 26.8.1 CLI, including local LanceDB indexes and deployed Retriever services. Use for PDFs, images, Office files, HTML, text, audio, and video; not for editing documents or web search.Votes: 0GitHub stars: 3,503
- Nemoclaw User GuideGuides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.Votes: 0GitHub stars: 3,503
- Nemotron Asr FinetuneOrchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. Given a goal like "improve/fine-tune ASR for my domain or language", it scopes the task, picks the cheapest sufficient path (word boosting → n-gram LM → fine-tuning), delegates each stage to the right sub-skill (data generation, training, evaluation, deployment), and answers cost/time/data questions along the way.Votes: 0GitHub stars: 3,503
- Nemotron Retrieval RecipesUse when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.Votes: 0GitHub stars: 3,503
- Nemotron SpeechRoutes NVIDIA Nemotron Speech (Formerly Riva) NIM tasks — deploys, runs, and tests ASR, TTS, and NMT NIMs on build.nvidia.com or self-hosted.Votes: 0GitHub stars: 3,503
- Nv Generate Ct RflowUsed for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.Votes: 0GitHub stars: 3,503
- Nv Generate Mr Brain FinetuneUsed for finetuning NV-Generate-CTMR MR-Brain v1 for T1, T2, FLAIR, SWI, or MRA data from a NIfTI datalist. Not for clinical or production data approval.Votes: 0GitHub stars: 3,503
- Nv Generate Mr BrainUsed for generating synthetic T1, T2, FLAIR, SWI, or MRA brain MRI volumes with NV-Generate-CTMR MR-Brain v1. Not for production training data.Votes: 0GitHub stars: 3,503
- Nv Generate MrUsed for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.Votes: 0GitHub stars: 3,503
- Nv Generate Vae FinetuneUsed for finetuning the NV-Generate-CTMR MAISI VAE from CT/MRI NIfTI datalists. Not for clinical or production data approval.Votes: 0GitHub stars: 3,503
- Nv Reason CxrUsed for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.Votes: 0GitHub stars: 3,503
- Nv Segment Ct FinetuneRuns standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.Votes: 0GitHub stars: 3,503
- Nv Segment CtUsed for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.Votes: 0GitHub stars: 3,503
- Nv Segment CtmrUsed for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.Votes: 0GitHub stars: 3,503
- Omniverse Cad To SimreadyCoordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.Votes: 0GitHub stars: 3,503
- Omniverse Realtime ViewerUse as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.Votes: 0GitHub stars: 3,503
- Omniverse Usd Performance TuningTop-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.Votes: 0GitHub stars: 3,503
- ReferencesHow to write the request. For humans preparing a job, and for agents checking whether a brief has enough in it to proceed. The skill measures the asset and presents what it finds. It cannot know what the result is *for*, and that is what decides the strategy. Three converted CAD stages of comparable size need three different dominant operations — the right one for a rack destroys a production line. So the brief supplies intent and the skill supplies evidence; a brief that states nothing leave...Votes: 0GitHub stars: 3,503