nvidia
52 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
nvidiajetson-customize-pinmux
- Per-pin SFIO / direction / initial-state configurator for a Jetson Orin or Thor custom carrier from the pinmux XLSM. Do NOT use for kernel-DT overlay or ODMDATA edits.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-derive-carrier
- Bootstrap a custom carrier board by forking carrier files and scaffolding a DT overlay from the reference devkit. Use after jetson-init-source; not for module-level or kernel-DTB changes.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-diagnostic
Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-init-image
- Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile. Use after jetson-init-target; not for source-tree setup.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-init-source
- Set up the BSP source workspace: LinuxforTegra overlay tracker, bspsources, Crosstool-NG toolchain. Use after jetson-init-image; not for fetching inputs.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-optimize-memory
- Reclaim 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.
- 導入数
- 1
- GitHub Stars
- 3349
- 更新日
- 9月19日
nvidiajetson-quick-start
- Entry skill for Jetson / IGX BSP customization. Asks one core click-to-select setup questionnaire and passes prefilled answers to downstream setup skills.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidiajetson-set-target
- Switch 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.
- 導入数
- 1
- GitHub Stars
- 3358
- 更新日
- 9月19日
nvidiajetson-validate-image
- Use 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.
- 導入数
- 1
- GitHub Stars
- 3525
- 更新日
- 10月2日
nvidianemo-relay-plugin-observability
Use this skill when choosing or configuring NeMo Relay 0.6 or 0.7 observability through the built-in plugin, subscribers, or exporters, including raw ATOF events, ATIF trajectories, OpenTelemetry, OpenInference, or custom event handling.
- 導入数
- 1
- GitHub Stars
- 3322
- 更新日
- 9月17日
nvidianemo-retriever
Use 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.
- 導入数
- 1
- GitHub Stars
- 3400
- 更新日
- 9月22日
nvidianemo-rl-session-memory
Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. Do NOT use for: simple questions, short tasks, one-off commands, linting, or code review.
- 導入数
- 1
- GitHub Stars
- 3471
- 更新日
- 9月25日
nvidianemoclaw-user-guide
Guides 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.
- 導入数
- 1
- GitHub Stars
- 3349
- 更新日
- 9月19日
nvidianvidia-skill-finder
- Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaomniverse-cad-to-simready
Coordinate 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.
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaomniverse-realtime-viewer
Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaomniverse-usd-performance-tuning
Top-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.
- 導入数
- 1
- GitHub Stars
- 3471
- 更新日
- 9月25日
nvidiaphysical-ai-defect-image-generation
- Use when the user wants to orchestrate defect image generation with NVIDIA Cosmos AnomalyGen (Cosmos-Predict2-derived) on OSMO for PCBA, metal surface, and glass inspection. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect im
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaphysical-ai-image-attribute-augmentation
Run the PAIDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiaphysical-ai-neural-reconstruction
Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, carline adaptation, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
- 導入数
- 1
- GitHub Stars
- 3469
- 更新日
- 9月25日
nvidiarag-blueprint
NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).
- 導入数
- 1
- GitHub Stars
- 3471
- 更新日
- 9月25日
nvidiarag-eval
- Filesystem RAG benchmarks: corpus/, train.json, evaluaterag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
- 導入数
- 1
- GitHub Stars
- 3400
- 更新日
- 9月22日
nvidiatao-finetune-clip
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT. This is a single-action model skill; do not use it for an iterative weak-attribute improvement loop that keeps retraining and evaluating until progress stops, which belongs to tao-run-deft-pas.
- 導入数
- 1
- GitHub Stars
- 3471
- 更新日
- 9月25日
nvidiatao-port-huggingface-model
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check,
- 導入数
- 1
- GitHub Stars
- 3471
- 更新日
- 9月25日