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",[683,684,685,686,687,5,688,689],"aerodynamics","cae","cfd","fluid-dynamics","nim","nvidia-warp","physicsnemo",[691,692,693,694,695,696],{"slug":388,"name":389},{"slug":5,"name":6},{"slug":117,"name":118},{"slug":39,"name":40},{"slug":528,"name":529},{"slug":476,"name":477},"2026-08-05T05:37:17.769923",{"name":699,"fullName":700,"repoUrl":701,"skillCount":576,"stars":702,"forks":703,"description":704,"topics":705,"topTags":707,"topTagCount":461,"lastUpdatedAt":713},"warp","NVIDIA\u002Fwarp","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fwarp",6864,555,"A Python framework for GPU-accelerated simulation, robotics, and machine learning.",[311,706,329,487,5,688,226],"differentiable-programming",[708,709,710,711,712],{"slug":30,"name":31},{"slug":5,"name":6},{"slug":36,"name":37},{"slug":114,"name":115},{"slug":39,"name":40},"2026-08-15T03:23:23.196487",{"name":715,"fullName":716,"repoUrl":717,"skillCount":718,"stars":719,"forks":720,"description":721,"topics":722,"topTags":723,"topTagCount":461,"lastUpdatedAt":731},"cuopt","NVIDIA\u002Fcuopt","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuopt",2,974,209,"GPU accelerated decision optimization ",[311,329,330,281],[724,725,726,727,728],{"slug":5,"name":6},{"slug":281,"name":340},{"slug":205,"name":206},{"slug":54,"name":55},{"slug":729,"name":730},"routing","Routing","2026-08-15T03:24:21.550154",{"name":733,"fullName":734,"repoUrl":735,"skillCount":718,"stars":102,"forks":245,"description":736,"topics":737,"topTags":738,"topTagCount":356,"lastUpdatedAt":748},"halos-outside-in-safety","NVIDIA\u002Fhalos-outside-in-safety","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fhalos-outside-in-safety","NVIDIA Halos Outside-In Safety Blueprint extends robot perception beyond on-board sensors by using external infrastructure cameras and AI agents to dynamically control robot behavior and perform at maximum efficiency. ",[],[739,740,741,742,743,746],{"slug":5,"name":6},{"slug":33,"name":34},{"slug":30,"name":31},{"slug":162,"name":163},{"slug":744,"name":745},"reporting","Reporting",{"slug":284,"name":747},"Security","2026-08-05T05:58:23.522352",{"name":750,"fullName":751,"repoUrl":752,"skillCount":718,"stars":753,"forks":754,"description":755,"topics":756,"topTags":757,"topTagCount":175,"lastUpdatedAt":764},"infra-controller","NVIDIA\u002Finfra-controller","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Finfra-controller",226,150,"NVIDIA Infra Controller - Hardware Lifecycle Management and multitenant networking",[],[758,759,762,763],{"slug":205,"name":206},{"slug":760,"name":761},"rest-api","REST API",{"slug":30,"name":31},{"slug":5,"name":6},"2026-08-31T09:19:19.794717",{"name":766,"fullName":767,"repoUrl":768,"skillCount":718,"stars":769,"forks":484,"description":770,"topics":771,"topTags":772,"topTagCount":103,"lastUpdatedAt":785},"OpenShell","NVIDIA\u002FOpenShell","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell",7681,"OpenShell is the safe, private runtime for autonomous AI agents.",[],[773,774,777,778,781,782],{"slug":5,"name":6},{"slug":775,"name":776},"code-analysis","Code Analysis",{"slug":96,"name":97},{"slug":779,"name":780},"policy","Policy",{"slug":447,"name":448},{"slug":783,"name":784},"sandboxing","Sandboxing","2026-08-28T14:38:43.227007",{"name":787,"fullName":788,"repoUrl":789,"skillCount":462,"stars":416,"forks":396,"description":790,"topics":791,"topTags":792,"topTagCount":461,"lastUpdatedAt":806},"cloudxr-js-samples","NVIDIA\u002Fcloudxr-js-samples","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcloudxr-js-samples","The samples for CloudXR.js, a JavaScript Client SDK that enables developers to build enterprise web applications for streaming high-performance VR and AR content from the CloudXR Runtime.",[],[793,796,799,800,803],{"slug":794,"name":795},"ar","AR",{"slug":797,"name":798},"immersive","Immersive",{"slug":5,"name":6},{"slug":801,"name":802},"vr","VR",{"slug":804,"name":805},"webxr","WebXR","2026-07-14T05:36:21.571154",{"name":808,"fullName":809,"repoUrl":810,"skillCount":462,"stars":811,"forks":812,"description":813,"topics":814,"topTags":821,"topTagCount":576,"lastUpdatedAt":825},"cuda-quantum","NVIDIA\u002Fcuda-quantum","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuda-quantum",1089,420,"C++ and Python support for the CUDA Quantum programming model for heterogeneous quantum-classical workflows",[310,815,226,816,817,422,818,819,820],"hacktoberfest","quantum","quantum-algorithms","quantum-machine-learning","quantum-programming-language","unitaryhack",[822,823,824],{"slug":5,"name":6},{"slug":226,"name":291},{"slug":422,"name":423},"2026-07-14T05:31:30.970327",{"name":827,"fullName":828,"repoUrl":829,"skillCount":462,"stars":830,"forks":831,"description":832,"topics":833,"topTags":850,"topTagCount":175,"lastUpdatedAt":855},"cudnn-frontend","NVIDIA\u002Fcudnn-frontend","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcudnn-frontend",865,201,"cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.",[834,835,311,836,837,45,838,839,840,329,841,842,843,844,845,846,847,5,848,849],"attention","blackwell","cuda-kernels","cuda-toolkit","flash-attention","fp8","gemm","grouped-gemm","hopper","mixture-of-experts","moe","mxfp8","normalization","nvfp4","sdpa","transformer",[851,852,853,854],{"slug":205,"name":206},{"slug":111,"name":112},{"slug":5,"name":6},{"slug":226,"name":291},"2026-08-02T05:47:02.685859",{"name":857,"fullName":858,"repoUrl":859,"skillCount":462,"stars":860,"forks":23,"description":861,"topics":862,"topTags":863,"topTagCount":175,"lastUpdatedAt":868},"holodeck","NVIDIA\u002Fholodeck","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fholodeck",31,"Holodeck is a project to create test environments optimised for GPU projects.",[],[864,865,866,867],{"slug":293,"name":294},{"slug":547,"name":548},{"slug":5,"name":6},{"slug":165,"name":166},"2026-07-14T05:36:29.548299",{"name":870,"fullName":871,"repoUrl":872,"skillCount":462,"stars":263,"forks":576,"description":873,"topics":874,"topTags":875,"topTagCount":175,"lastUpdatedAt":880},"NeMo-Fabric","NVIDIA\u002FNeMo-Fabric","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Fabric","Project NVIDIA NeMo Fabric",[],[876,877,878,879],{"slug":114,"name":115},{"slug":30,"name":31},{"slug":5,"name":6},{"slug":165,"name":166},"2026-07-23T06:06:22.954737",{"name":882,"fullName":883,"repoUrl":884,"skillCount":462,"stars":885,"forks":886,"description":887,"topics":888,"topTags":892,"topTagCount":576,"lastUpdatedAt":900},"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",[439,889,5,279,890,783,891],"hermes","openshell","typescript",[893,894,897],{"slug":111,"name":112},{"slug":895,"name":896},"mcp","MCP",{"slug":898,"name":899},"search","Search","2026-08-25T03:29:57.273192",{"name":902,"fullName":903,"repoUrl":904,"skillCount":462,"stars":905,"forks":906,"description":907,"topics":908,"topTags":909,"topTagCount":576,"lastUpdatedAt":915},"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",[],[910,911,912],{"slug":42,"name":43},{"slug":5,"name":6},{"slug":913,"name":914},"research","Research","2026-07-14T05:36:16.524177",{"name":917,"fullName":918,"repoUrl":919,"skillCount":462,"stars":920,"forks":921,"description":922,"topics":923,"topTags":928,"topTagCount":175,"lastUpdatedAt":939},"NVSentinel","NVIDIA\u002FNVSentinel","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVSentinel",378,117,"NVSentinel detects and remediates GPU faults on Kubernetes nodes",[924,54,925,329,926,180,278,927],"accelerated-computing","breakfix","gpu-monitoring","resilience",[929,932,933,936],{"slug":930,"name":931},"database","Database",{"slug":33,"name":34},{"slug":934,"name":935},"migration","Migration",{"slug":937,"name":938},"mongodb","MongoDB","2026-08-31T09:19:19.418291",{"name":941,"fullName":942,"repoUrl":943,"skillCount":462,"stars":944,"forks":945,"description":770,"topics":946,"topTags":947,"topTagCount":175,"lastUpdatedAt":952},"OpenShell-Community","NVIDIA\u002FOpenShell-Community","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell-Community",172,63,[],[948,949,950,951],{"slug":293,"name":294},{"slug":30,"name":31},{"slug":96,"name":97},{"slug":760,"name":761},"2026-07-14T05:36:26.866398",{"name":954,"fullName":955,"repoUrl":956,"skillCount":462,"stars":462,"forks":462,"description":957,"topics":958,"topTags":959,"topTagCount":175,"lastUpdatedAt":966},"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",[],[960,963,964,965],{"slug":961,"name":962},"data-engineering","Data Engineering",{"slug":42,"name":43},{"slug":5,"name":6},{"slug":39,"name":40},"2026-07-14T05:36:30.828379",{"name":968,"fullName":969,"repoUrl":970,"skillCount":462,"stars":718,"forks":462,"description":971,"topics":972,"topTags":973,"topTagCount":175,"lastUpdatedAt":978},"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",[],[974,975,976,977],{"slug":54,"name":55},{"slug":233,"name":234},{"slug":671,"name":672},{"slug":5,"name":6},"2026-07-14T05:32:21.766434",{"name":980,"fullName":981,"repoUrl":982,"skillCount":462,"stars":461,"forks":462,"description":983,"topics":984,"topTags":985,"topTagCount":175,"lastUpdatedAt":990},"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",[],[986,987,988,989],{"slug":961,"name":962},{"slug":671,"name":672},{"slug":5,"name":6},{"slug":39,"name":40},"2026-07-14T05:36:25.595046",{"name":992,"fullName":993,"repoUrl":994,"skillCount":462,"stars":298,"forks":461,"description":995,"topics":996,"topTags":997,"topTagCount":175,"lastUpdatedAt":1004},"physical-ai-data-factory","NVIDIA\u002Fphysical-ai-data-factory","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysical-ai-data-factory","Synthetic Data Generation Workflows for Physical AI",[],[998,999,1000,1003],{"slug":54,"name":55},{"slug":51,"name":52},{"slug":1001,"name":1002},"images","Images",{"slug":5,"name":6},"2026-08-15T03:23:20.17238",{"name":1006,"fullName":1007,"repoUrl":1008,"skillCount":462,"stars":1009,"forks":1010,"description":1011,"topics":1012,"topTags":1013,"topTagCount":175,"lastUpdatedAt":1020},"SkillSpector","NVIDIA\u002FSkillSpector","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FSkillSpector",13119,1065,"Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks.",[],[1014,1015,1018,1019],{"slug":86,"name":87},{"slug":1016,"name":1017},"audit","Audit",{"slug":775,"name":776},{"slug":284,"name":747},"2026-08-31T09:18:49.098967",{"name":1022,"fullName":1023,"repoUrl":1024,"skillCount":462,"stars":1025,"forks":1026,"description":1027,"topics":1028,"topTags":1029,"topTagCount":175,"lastUpdatedAt":1036},"TileGym","NVIDIA\u002FTileGym","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTileGym",770,79,"Helpful kernel tutorials, examples and SKILLs for tile-based GPU programming",[],[1030,1031,1032,1033],{"slug":30,"name":31},{"slug":5,"name":6},{"slug":36,"name":37},{"slug":1034,"name":1035},"rust","Rust","2026-08-15T03:24:29.570534",{"name":1038,"fullName":1039,"repoUrl":1040,"skillCount":462,"stars":1041,"forks":1042,"description":1043,"topics":1044,"topTags":1047,"topTagCount":576,"lastUpdatedAt":1051},"torch-harmonics","NVIDIA\u002Ftorch-harmonics","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Ftorch-harmonics",686,69,"Differentiable signal processing on the sphere for PyTorch",[42,236,1045,1046],"signal-processing","sphere",[1048,1049,1050],{"slug":111,"name":112},{"slug":30,"name":31},{"slug":5,"name":6},"2026-07-14T05:32:03.82994",{"items":1053,"total":576},[1054,1066,1078],{"slug":1055,"name":1055,"fn":1056,"description":1057,"org":1058,"tags":1059,"stars":594,"repoUrl":593,"updatedAt":1065},"day0-release","automate quantized checkpoint releases","Deterministic end-to-end driver for day-0 quantized-checkpoint releases — chains PTQ → evaluation → comparison with enforced gates between stages (the evaluation stage deploys the checkpoint itself), and returns a publish decision (ACCEPT \u002F REGRESSION \u002F ANOMALOUS \u002F INFEASIBLE). Use when the user asks to \"release a model at day-0\", \"quantize and validate model X is within N% of baseline and tell me if it's publishable\", or \"run the full day-0 workflow\". Do NOT use for single-stage requests — quantizing only (use ptq), serving only (use deployment), evaluating only (use evaluation), or comparing two existing runs (use compare-results).",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1060,1062,1063,1064],{"name":55,"slug":54,"type":1061},"tag",{"name":160,"slug":159,"type":1061},{"name":34,"slug":33,"type":1061},{"name":6,"slug":5,"type":1061},"2026-08-31T09:18:55.832925",{"slug":33,"name":33,"fn":1067,"description":1068,"org":1069,"tags":1070,"stars":594,"repoUrl":593,"updatedAt":1077},"deploy LLM checkpoints as API endpoints","Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says \"deploy model\", \"serve model\", \"start vLLM server\", \"launch SGLang\", \"TRT-LLM deploy\", \"AutoDeploy\", \"benchmark throughput\", \"serve checkpoint\", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1071,1072,1073,1074],{"name":34,"slug":33,"type":1061},{"name":142,"slug":141,"type":1061},{"name":6,"slug":5,"type":1061},{"name":1075,"slug":1076,"type":1061},"vLLM","vllm","2026-08-31T09:18:55.144088",{"slug":519,"name":519,"fn":1079,"description":1080,"org":1081,"tags":1082,"stars":594,"repoUrl":593,"updatedAt":605},"evaluate LLM accuracy with NeMo","Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on \"evaluate model\", \"benchmark accuracy\", \"run MMLU\", \"evaluate quantized model\", \"run nel\". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq), deploying\u002Fserving models (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[1083,1084,1085,1086],{"name":389,"slug":388,"type":1061},{"name":535,"slug":534,"type":1061},{"name":142,"slug":141,"type":1061},{"name":6,"slug":5,"type":1061}]