- Nat InstallationUse when installing or configuring NVIDIA NeMo Agent Toolkit, verifying the `nat` CLI, setting up optional extras, or creating a first hello-world workflow.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat Mcp And ServingUse when serving NeMo Agent Toolkit workflows, exposing workflows through FastAPI, configuring MCP clients or servers, or troubleshooting transport and server setup.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat OptimizationUse when configuring or running NeMo Agent Toolkit optimization with `nat optimize`, including Optuna parameter tuning, prompt evolution, optimizer sizing, output interpretation, and optimizer datasets.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat Path ChecksUse when fixing NeMo Agent Toolkit documentation path-check failures, especially failed `ci/scripts/path_checks.py` output, slash-delimited text mistaken for paths, relative path references, Markdown code escaping, and path-check allowlist decisions.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat TelemetryUse when adding, configuring, or troubleshooting NeMo Agent Toolkit logging, tracing, telemetry exporters, OpenTelemetry, Langfuse, LangSmith, Weave, Phoenix, profiling, or observability provider integrations.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat Tools And FunctionsUse when authoring, registering, composing, or testing custom NeMo Agent Toolkit tools, functions, function groups, Python components, custom agents, custom evaluators, or advanced extension patterns.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat User RulesUse first for general NVIDIA NeMo Agent Toolkit coding-agent behavior, task routing, naming conventions, component discovery rules, and cross-skill guidance.NVIDIA/NeMo-Agent-Toolkit2,473
- Nat Workflow CreationUse when creating, editing, validating, running, or troubleshooting NeMo Agent Toolkit workflow YAML, component discovery, LLM configuration, and common `nat` CLI commands.NVIDIA/NeMo-Agent-Toolkit2,473
- Skill EvolutionUse before creating, editing, or deciding whether to update any AI coding agent skill in this repository, including corrections to existing skill behavior, references, or routing.NVIDIA/NeMo-Agent-Toolkit2,473
- Accelerated Computing CudfOfficial NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.NVIDIA/skills2,205
- Aiq DeployUse when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.NVIDIA/skills2,205
- Aiq DeployUse when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.NVIDIA/skills2,205
- Aiq ResearchUse when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.NVIDIA/skills2,205
- Aiq ResearchUse when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.NVIDIA/skills2,205
- Amc Run Sample CalibrationRun end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.NVIDIA/skills2,205
- Amc Run Video CalibrationCalibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar.NVIDIA/skills2,205
- Amc Setup Calibration StackLaunch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.NVIDIA/skills2,205
- Cudaq GuideCUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.NVIDIA/skills2,205
- CufolioUse when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, efficient frontiers, scenario generation, or NVIDIA cuOpt.NVIDIA/skills2,205
- Cuopt DeveloperModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.NVIDIA/skills2,205
- Cuopt InstallInstall cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.NVIDIA/skills2,205
- Cuopt Multi Objective ExplorationTrace and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).NVIDIA/skills2,205
- Cuopt Numerical Optimization Api CLP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.NVIDIA/skills2,205
- Cuopt Numerical Optimization Api CliLP, MILP, and QP (beta) with cuOpt — CLI only (MPS/LP/QPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from an MPS or LP file via command line.NVIDIA/skills2,205
- Cuopt Numerical Optimization Api PythonSolve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.NVIDIA/skills2,205
- Cuopt Numerical Optimization Api PythonSolve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.NVIDIA/skills2,205
- Cuopt Numerical Optimization FormulationLP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.NVIDIA/skills2,205
- Cuopt Routing Api PythonVehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.NVIDIA/skills2,205
- Cuopt Routing FormulationVehicle routing (VRP, TSP, PDP) — problem types and data requirements. Domain concepts; no API or interface.NVIDIA/skills2,205
- Cuopt Server Api PythoncuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.NVIDIA/skills2,205
- Cuopt Server CommoncuOpt REST server — what it does and how requests flow. Domain concepts; no deploy or client code.NVIDIA/skills2,205
- Cuopt Skill EvolutionAfter solving a non-trivial problem, detect generalizable learnings and propose skill updates. Always active — applies to every interaction.NVIDIA/skills2,205
- Cuopt User RulesBase rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.NVIDIA/skills2,205
- Cupynumeric Hdf5Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layoNVIDIA/skills2,205
- Cupynumeric InstallInstall and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.NVIDIA/skills2,205
- Cupynumeric Migration ReadinessPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-NVIDIA/skills2,205
- Cupynumeric Parallel Data LoadLoad a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.NVIDIA/skills2,205
- Dali Dynamic ModeDALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.NVIDIA/skills2,205
- Dali Dynamic ModeDALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.NVIDIA/skills2,205
- Data DesignerUse when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.NVIDIA/skills2,205
- Deepstream DevNVIDIA DeepStream SDK 9.0 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.NVIDIA/skills2,205
- Deepstream Generate PipelineBuild DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).NVIDIA/skills2,205
- Deepstream Import Vision ModelUse this skill to bring any 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 benchmark, and PDF report. Object detection models only.NVIDIA/skills2,205
- Deepstream Profile PipelineProfile a DeepStream pipeline with Nsight Systems and derive its configs from the measurement. Use when the user asks for an efficient, performant, or profiled pipeline — or to benchmark, tune, or measure FPS.NVIDIA/skills2,205
- Deepstream SopUse this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on inNVIDIA/skills2,205
- Dicom Metadata ExtractUsed for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.NVIDIA/skills2,205
- Dicom Series PreflightUsed for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.NVIDIA/skills2,205
- Dicom Series To VolumeUsed for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.NVIDIA/skills2,205
- Digital Health Clinical Asr BuildStage 2 of the Clinical ASR Flywheel. Use when curating clinical terms, tagging IPA, and synthesizing a NeMo manifest. NOT for scoring (use /digital-health-clinical-asr-eval).NVIDIA/skills2,205
- Digital Health Clinical Asr EvalStage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).NVIDIA/skills2,205
- Digital Health Clinical Asr FinetuneStage 4 of the Clinical ASR Flywheel. Use when priority KER is above 0.3 to run stock NeMo SFT on Parakeet TDT v2 and offline cycle N+1 re-eval. NOT for generic word boosting (use /finetune-asr).NVIDIA/skills2,205
- Digital Health Clinical Asr SetupStage 1 of Clinical ASR Flywheel. Use when bootstrapping a cycle: NVCF+MW disclosure, NVIDIA_API_KEY check, deps install, TTS+ASR smoke test.NVIDIA/skills2,205
- Dynamo Interconnect CheckValidate that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after recipe-runner brings a deployment up (especially disagg/multi-node) to confirm the KV transport is correct; use troubleshoot for diagnosing already-failed pods.NVIDIA/skills2,205
- Dynamo Interconnect CheckValidate that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after recipe-runner brings a deployment up (especially disagg/multi-node) to confirm the KV transport is correct; use troubleshoot for diagnosing already-failed pods.NVIDIA/skills2,205
- Dynamo Recipe RunnerSelect, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.NVIDIA/skills2,205
- Dynamo Router StarterStart or patch Dynamo router modes and run router endpoint smoke checks. Use for round-robin, KV-aware, least-loaded, or device-aware routing setup; use recipe-runner for recipe deployment and troubleshoot for failure diagnosis.NVIDIA/skills2,205
- Dynamo Router StarterStart or patch Dynamo router modes and run router endpoint smoke checks. Use for round-robin, KV-aware, least-loaded, or device-aware routing setup; use recipe-runner for recipe deployment and troubleshoot for failure diagnosis.NVIDIA/skills2,205
- Dynamo TroubleshootDiagnose failed or unhealthy Dynamo deployments. Use when pods, model-cache jobs, PVCs, workers, frontend/router health, endpoints, or benchmark jobs fail; use recipe-runner/router-starter before this for normal bring-up.NVIDIA/skills2,205
- Earth2studio Create DatasourceCreate and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. Do NOT use for fetching data with existing sources, model inference, or installation tasks.NVIDIA/skills2,205
- Earth2studio Create DiagnosticCreate Earth2Studio diagnostic model wrappers for single-step data transformations, including simple derived diagnostics, packaged AutoModel diagnostics, and generative or diffusion diagnostics. Do NOT use for prognostic time-stepping models, data sources, or installation.NVIDIA/skills2,205