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  • 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.
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  • Choose a physics engine and integration for robot learning, manipulation, assembly, differentiable optimization, or other Physical AI simulation. Compare Newton, PhysX, MuJoCo/MJX, Bullet/PyBullet, VSim Robotics, Genesis, and Drake. Evaluate Newton first
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  • Add agent-ready vision capabilities — dense captioning, detection, search, alerting, summarization — to an agent or application through a customizable, self-contained vision stack built on the NVIDIA VSS Blueprint. Use this skill when a developer or agent
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  • 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 A
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  • Use when running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO
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  • Use 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, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, work
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  • Set up or troubleshoot the Auto Ontology runtime. Use for Helm (the official install), Docker Compose, developer setup, and MCP connection to an existing deployment.
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  • 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/Op
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  • Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
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  • Run the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models: baseline evaluate, RCA, Cosmos AnomalyGen / AMP synthetic defects, k-NN mining, retraining, and deployment gating against a customer-defined primary m
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  • cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
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  • LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
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  • Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighbour source images for downstream augmentation. Use as the immediate next step after `tao-route-visual-cha
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  • Run the disk-backed DEFT AOI improvement loop for NVIDIA Cosmos Reason 3 / Cosmos3 models, using Nano by default and Edge or Super when explicitly requested: evaluate the base model on Proxy and frozen Benchmark splits, mine real image pairs from Proxy ga
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  • Run a binary industrial-inspection DEFT loop with TAO RT-DETR: measure on frozen KPI/test roles, mine gap-similar real and clean images with SigLIP, accumulate admitted COCO data, retrain from one base checkpoint, and select by KPI AP50. Use for iterative
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  • Create and validate an NVIDIA PVA operator specification, test matrix, evidence manifest, and deterministic OpGen handoff. Use after Doctor for new operators, changes, or repairs, using requirements, a partial spec, reference code, or an existing operator
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  • Use this skill to bring a supported object-detection vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream b
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  • Use this skill to check or change this machine's NVIDIA display and GPU settings, such as resolution, refresh rate, V-Sync, G-SYNC, brightness, color, and GPU performance.
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  • Writes an AI agent's ethos.md — a durable snapshot of its purpose, intended behavior, boundaries, and success criteria — from the answers gathered by ethos-explore. Renders the schema-v1 template, validates the front matter and required markdown sections,
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  • Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; n
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  • NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
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  • Deploy and configure the NVIDIA DOCA Telemetry Service (DTS) as it is actually run: (A) BlueField (built-in static pod), (B) Host (docker) on x86_64 / aarch64, or (C) DPF (DPUService / helm chart) — plus the shared configuration layer (`dts_config.ini`, p
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  • Integrate a new diffusion or flow model, or a task variant such as I2V, editing or causal generation, into NVIDIA FastGen as a FastGenNetwork adapter with preprocessors, configs, method capabilities, reference parity and trainability. Use it whenever a mo
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  • Implement, refactor or review NVIDIA FastGen library code using its shared design, formatting, documentation and testing conventions. Use for source changes and code reviews; configuring or running an existing workflow alone does not need this skill.
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