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",[],[707,708,709,710],{"slug":32,"name":33},{"slug":29,"name":30},{"slug":5,"name":6},{"slug":233,"name":711},"Security","2026-07-14T05:36:09.251774",{"name":714,"fullName":715,"repoUrl":716,"skillCount":392,"stars":717,"forks":208,"description":718,"topics":719,"topTags":720,"topTagCount":192,"lastUpdatedAt":725},"holodeck","NVIDIA\u002Fholodeck","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fholodeck",31,"Holodeck is a project to create test environments optimised for GPU projects.",[],[721,722,723,724],{"slug":243,"name":244},{"slug":446,"name":447},{"slug":5,"name":6},{"slug":116,"name":117},"2026-07-14T05:36:29.548299",{"name":727,"fullName":728,"repoUrl":729,"skillCount":392,"stars":730,"forks":731,"description":732,"topics":733,"topTags":734,"topTagCount":192,"lastUpdatedAt":741},"infra-controller","NVIDIA\u002Finfra-controller","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Finfra-controller",226,150,"NVIDIA Infra Controller - Hardware Lifecycle Management and multitenant networking",[],[735,736,737,738],{"slug":159,"name":160},{"slug":29,"name":30},{"slug":5,"name":6},{"slug":739,"name":740},"rest-api","REST API","2026-07-14T05:32:52.235912",{"name":743,"fullName":744,"repoUrl":745,"skillCount":392,"stars":190,"forks":478,"description":746,"topics":747,"topTags":748,"topTagCount":192,"lastUpdatedAt":753},"NeMo-Fabric","NVIDIA\u002FNeMo-Fabric","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Fabric","Project NVIDIA NeMo Fabric",[],[749,750,751,752],{"slug":88,"name":89},{"slug":29,"name":30},{"slug":5,"name":6},{"slug":116,"name":117},"2026-07-23T06:06:22.954737",{"name":755,"fullName":756,"repoUrl":757,"skillCount":392,"stars":758,"forks":759,"description":760,"topics":761,"topTags":766,"topTagCount":478,"lastUpdatedAt":774},"NemoClaw","NVIDIA\u002FNemoClaw","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw",21777,2940,"Run agents like Hermes, LangChain Deep Agents, and OpenClaw more securely inside NVIDIA OpenShell with managed inference",[762,763,5,228,764,627,765],"ai-agents","hermes","openshell","typescript",[767,768,771],{"slug":85,"name":86},{"slug":769,"name":770},"mcp","MCP",{"slug":772,"name":773},"search","Search","2026-07-20T06:00:01.461044",{"name":776,"fullName":777,"repoUrl":778,"skillCount":392,"stars":779,"forks":780,"description":781,"topics":782,"topTags":783,"topTagCount":478,"lastUpdatedAt":789},"nvidia-kaggle","NVIDIA\u002Fnvidia-kaggle","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvidia-kaggle",253,22,"NVIDIA Kaggle Plugin gives agents end-to-end Kaggle competition workflows through a single skill, nvidia-kaggle-skill. It can gather competition context, study public writeups and notebooks, reproduce kernels locally, submit to competitions, and manage Ka",[],[784,785,786],{"slug":41,"name":42},{"slug":5,"name":6},{"slug":787,"name":788},"research","Research","2026-07-14T05:36:16.524177",{"name":791,"fullName":792,"repoUrl":793,"skillCount":392,"stars":794,"forks":795,"description":612,"topics":796,"topTags":797,"topTagCount":192,"lastUpdatedAt":802},"OpenShell-Community","NVIDIA\u002FOpenShell-Community","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell-Community",172,63,[],[798,799,800,801],{"slug":243,"name":244},{"slug":29,"name":30},{"slug":107,"name":108},{"slug":739,"name":740},"2026-07-14T05:36:26.866398",{"name":804,"fullName":805,"repoUrl":806,"skillCount":392,"stars":392,"forks":392,"description":807,"topics":808,"topTags":809,"topTagCount":192,"lastUpdatedAt":816},"paidf-augmentation","NVIDIA\u002Fpaidf-augmentation","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpaidf-augmentation","Containerized generative-AI pipeline that transforms video, image, and text inputs into large, diverse, physically grounded datasets for model training and evaluation",[],[810,813,814,815],{"slug":811,"name":812},"data-engineering","Data Engineering",{"slug":41,"name":42},{"slug":5,"name":6},{"slug":38,"name":39},"2026-07-14T05:36:30.828379",{"name":818,"fullName":819,"repoUrl":820,"skillCount":392,"stars":592,"forks":392,"description":821,"topics":822,"topTags":823,"topTagCount":192,"lastUpdatedAt":828},"paidf-auto-labeling","NVIDIA\u002Fpaidf-auto-labeling","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpaidf-auto-labeling","Auto-labeling pipeline that turns raw video and images into fine-tuning-ready scenes via super-resolution, detection and tracking, VLM scene understanding, and question generation",[],[824,825,826,827],{"slug":53,"name":54},{"slug":269,"name":270},{"slug":559,"name":560},{"slug":5,"name":6},"2026-07-14T05:32:21.766434",{"name":830,"fullName":831,"repoUrl":832,"skillCount":392,"stars":391,"forks":392,"description":833,"topics":834,"topTags":835,"topTagCount":192,"lastUpdatedAt":840},"paidf-simulation","NVIDIA\u002Fpaidf-simulation","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpaidf-simulation","Synthetic data generation engine using NVIDIA Isaac Sim and Omniverse Replicator to render photorealistic, fully labeled PCB inspection imagery, including golden and defect boards",[],[836,837,838,839],{"slug":811,"name":812},{"slug":559,"name":560},{"slug":5,"name":6},{"slug":38,"name":39},"2026-07-14T05:36:25.595046",{"name":842,"fullName":843,"repoUrl":844,"skillCount":392,"stars":845,"forks":846,"description":847,"topics":848,"topTags":849,"topTagCount":192,"lastUpdatedAt":856},"TileGym","NVIDIA\u002FTileGym","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTileGym",770,79,"Helpful kernel tutorials, examples and SKILLs for tile-based GPU programming",[],[850,851,852,853],{"slug":29,"name":30},{"slug":5,"name":6},{"slug":35,"name":36},{"slug":854,"name":855},"rust","Rust","2026-07-14T05:33:14.807388",{"name":858,"fullName":859,"repoUrl":860,"skillCount":392,"stars":861,"forks":862,"description":863,"topics":864,"topTags":867,"topTagCount":478,"lastUpdatedAt":871},"torch-harmonics","NVIDIA\u002Ftorch-harmonics","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Ftorch-harmonics",686,69,"Differentiable signal processing on the sphere for PyTorch",[41,273,865,866],"signal-processing","sphere",[868,869,870],{"slug":85,"name":86},{"slug":29,"name":30},{"slug":5,"name":6},"2026-07-14T05:32:03.82994",{"items":873,"total":14},[874,886,896,906,916,931,944,954,971,985,996,1006,1018,1028,1039,1050,1060,1070,1083,1095,1104,1115,1125,1136],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":60,"repoUrl":59,"updatedAt":885},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883,884],{"name":142,"slug":141,"type":881},"tag",{"name":812,"slug":811,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:28:43.176466",{"slug":887,"name":887,"fn":888,"description":889,"org":890,"tags":891,"stars":60,"repoUrl":59,"updatedAt":895},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[892,893,894],{"name":33,"slug":32,"type":881},{"name":447,"slug":446,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:29:06.667109",{"slug":897,"name":897,"fn":898,"description":899,"org":900,"tags":901,"stars":60,"repoUrl":59,"updatedAt":905},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[902,903,904],{"name":156,"slug":155,"type":881},{"name":6,"slug":5,"type":881},{"name":788,"slug":787,"type":881},"2026-07-14T05:28:06.816956",{"slug":907,"name":907,"fn":908,"description":909,"org":910,"tags":911,"stars":60,"repoUrl":59,"updatedAt":915},"amc-run-sample-calibration","run AMC sample dataset calibration","Run 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'.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[912,913,914],{"name":142,"slug":141,"type":881},{"name":6,"slug":5,"type":881},{"name":117,"slug":116,"type":881},"2026-07-17T05:29:03.913266",{"slug":917,"name":917,"fn":918,"description":919,"org":920,"tags":921,"stars":60,"repoUrl":59,"updatedAt":930},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate 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. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[922,923,926,927],{"name":54,"slug":53,"type":881},{"name":924,"slug":925,"type":881},"Imaging","imaging",{"name":6,"slug":5,"type":881},{"name":928,"slug":929,"type":881},"Video","video","2026-07-17T05:28:53.905004",{"slug":932,"name":932,"fn":933,"description":934,"org":935,"tags":936,"stars":60,"repoUrl":59,"updatedAt":943},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch 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.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[937,938,939,940],{"name":33,"slug":32,"type":881},{"name":560,"slug":559,"type":881},{"name":6,"slug":5,"type":881},{"name":941,"slug":942,"type":881},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":945,"name":945,"fn":946,"description":947,"org":948,"tags":949,"stars":60,"repoUrl":59,"updatedAt":953},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[950,951,952],{"name":6,"slug":5,"type":881},{"name":379,"slug":378,"type":881},{"name":39,"slug":38,"type":881},"2026-07-14T05:26:58.898253",{"slug":955,"name":955,"fn":956,"description":957,"org":958,"tags":959,"stars":60,"repoUrl":59,"updatedAt":970},"cufolio","build and optimize stock portfolios","Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, efficient frontiers, scenario generation, or NVIDIA cuOpt.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[960,963,964,967],{"name":961,"slug":962,"type":881},"Finance","finance",{"name":6,"slug":5,"type":881},{"name":965,"slug":966,"type":881},"Portfolio Management","portfolio-management",{"name":968,"slug":969,"type":881},"Trading","trading","2026-07-14T05:30:00.601648",{"slug":972,"name":972,"fn":973,"description":974,"org":975,"tags":976,"stars":60,"repoUrl":59,"updatedAt":984},"cuopt-developer","develop and debug NVIDIA cuOpt","Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++\u002FCUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[977,980,981,982,983],{"name":978,"slug":979,"type":881},"C#","c",{"name":89,"slug":88,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":241,"slug":240,"type":881},"2026-07-14T05:27:49.027084",{"slug":986,"name":986,"fn":987,"description":988,"org":989,"tags":990,"stars":60,"repoUrl":59,"updatedAt":995},"cuopt-install","install and verify cuOpt","Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[991,992,993,994],{"name":33,"slug":32,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":241,"slug":240,"type":881},"2026-07-14T05:27:21.724705",{"slug":997,"name":997,"fn":998,"description":999,"org":1000,"tags":1001,"stars":60,"repoUrl":59,"updatedAt":1005},"cuopt-multi-objective-exploration","explore Pareto frontiers with cuOpt","Trace and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1002,1003,1004],{"name":487,"slug":486,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},"2026-07-14T05:27:11.448811",{"slug":1007,"name":1007,"fn":1008,"description":1009,"org":1010,"tags":1011,"stars":60,"repoUrl":59,"updatedAt":1017},"cuopt-numerical-optimization-api","solve numerical optimization problems with cuOpt","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.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1012,1013,1014,1015,1016],{"name":160,"slug":159,"type":881},{"name":487,"slug":486,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":241,"slug":240,"type":881},"2026-07-14T05:30:16.593088",{"slug":1019,"name":1019,"fn":1020,"description":1021,"org":1022,"tags":1023,"stars":60,"repoUrl":59,"updatedAt":1027},"cuopt-numerical-optimization-formulation","formulate numerical optimization problems","LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1024,1025,1026],{"name":487,"slug":486,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},"2026-07-14T05:29:53.133853",{"slug":1029,"name":1029,"fn":1030,"description":1031,"org":1032,"tags":1033,"stars":60,"repoUrl":59,"updatedAt":1038},"cuopt-routing-api-python","solve vehicle routing problems with cuOpt","Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1034,1035,1036,1037],{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":241,"slug":240,"type":881},{"name":605,"slug":604,"type":881},"2026-07-14T05:29:01.696483",{"slug":1040,"name":1040,"fn":1041,"description":1042,"org":1043,"tags":1044,"stars":60,"repoUrl":59,"updatedAt":1049},"cuopt-server-api-python","deploy and call cuOpt REST APIs","cuOpt REST server — start server, endpoints, Python\u002Fcurl client examples. Use when the user is deploying or calling the REST API.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1045,1046,1047,1048],{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":241,"slug":240,"type":881},{"name":740,"slug":739,"type":881},"2026-07-14T05:26:52.704898",{"slug":1051,"name":1051,"fn":1052,"description":1053,"org":1054,"tags":1055,"stars":60,"repoUrl":59,"updatedAt":1059},"cupynumeric-hdf5","read and write cuPyNumeric arrays to HDF5","Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I\u002FO (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5\u002F.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\u002FO with GPUDirect Storage (GDS). Do not use it for Parquet\u002FcuDF\u002Fraw-binary or other sharded\u002Fcustom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store\u002FS3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1056,1057,1058],{"name":812,"slug":811,"type":881},{"name":6,"slug":5,"type":881},{"name":241,"slug":240,"type":881},"2026-07-14T05:30:17.861772",{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1064,"tags":1065,"stars":60,"repoUrl":59,"updatedAt":1069},"cupynumeric-install","install and verify cuPyNumeric","Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1066,1067,1068],{"name":33,"slug":32,"type":881},{"name":6,"slug":5,"type":881},{"name":241,"slug":240,"type":881},"2026-07-14T05:26:22.718143",{"slug":1071,"name":1071,"fn":1072,"description":1073,"org":1074,"tags":1075,"stars":60,"repoUrl":59,"updatedAt":1082},"cupynumeric-migration-readiness","assess NumPy to cuPyNumeric migration readiness","Pre-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-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python\u002Fobject-heavy control flow, shape\u002Fdata-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1076,1077,1080,1081],{"name":142,"slug":141,"type":881},{"name":1078,"slug":1079,"type":881},"Migration","migration",{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:28:13.154911",{"slug":1084,"name":1084,"fn":1085,"description":1086,"org":1087,"tags":1088,"stars":60,"repoUrl":59,"updatedAt":1094},"cupynumeric-parallel-data-load","load distributed datasets into cuPyNumeric","Load a sharded, on-disk dataset (sharded .npy, Parquet\u002FArrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU\u002FOMP\u002FGPU 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.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1089,1090,1091,1092,1093],{"name":142,"slug":141,"type":881},{"name":812,"slug":811,"type":881},{"name":407,"slug":406,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:29:02.93907",{"slug":1096,"name":1096,"fn":1097,"description":1098,"org":1099,"tags":1100,"stars":60,"repoUrl":59,"updatedAt":1103},"dali-dynamic-mode","develop DALI pipelines with dynamic mode","DALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1101,1102],{"name":270,"slug":269,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:26:18.919555",{"slug":1105,"name":1105,"fn":1106,"description":1107,"org":1108,"tags":1109,"stars":60,"repoUrl":59,"updatedAt":1114},"data-designer","design and generate synthetic datasets","Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1110,1111,1112,1113],{"name":812,"slug":811,"type":881},{"name":270,"slug":269,"type":881},{"name":407,"slug":406,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:28:38.228625",{"slug":1116,"name":1116,"fn":1117,"description":1118,"org":1119,"tags":1120,"stars":60,"repoUrl":59,"updatedAt":1124},"deepstream-dev","build video analytics pipelines with DeepStream","NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection\u002Ftracking, or Kafka\u002Fmessage broker integration.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1121,1122,1123],{"name":270,"slug":269,"type":881},{"name":6,"slug":5,"type":881},{"name":928,"slug":929,"type":881},"2026-07-17T05:29:07.918471",{"slug":1126,"name":1126,"fn":1127,"description":1128,"org":1129,"tags":1130,"stars":60,"repoUrl":59,"updatedAt":1135},"deepstream-generate-pipeline","build DeepStream GStreamer inference pipelines","Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video\u002Fimage 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\u002FDeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1131,1132,1133,1134],{"name":270,"slug":269,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":928,"slug":929,"type":881},"2026-07-14T05:28:40.698105",{"slug":1137,"name":1137,"fn":1138,"description":1139,"org":1140,"tags":1141,"stars":60,"repoUrl":59,"updatedAt":1146},"deepstream-import-vision-model","import vision models into DeepStream pipelines","Use 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.\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1142,1143,1144,1145],{"name":54,"slug":53,"type":881},{"name":48,"slug":47,"type":881},{"name":45,"slug":44,"type":881},{"name":6,"slug":5,"type":881},"2026-07-17T05:29:08.906831"]