[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"org-nvidia":3,"repo-skills-v-0-3-0":872},{"org":4,"repos":56},{"slug":5,"name":6,"logoUrl":7,"githubOrg":6,"website":8,"skillCount":9,"repoCount":10,"topRepos":11,"topTags":26,"lastUpdatedAt":55},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png","https:\u002F\u002Fwww.nvidia.com",496,41,[12,15,18,21,24],{"name":13,"skillCount":14},"NVIDIA\u002Fskills",305,{"name":16,"skillCount":17},"NVIDIA\u002Fsimready-foundation",26,{"name":19,"skillCount":20},"NVIDIA\u002FMegatron-LM",13,{"name":22,"skillCount":23},"NVIDIA\u002Fdgx-spark-playbooks",11,{"name":25,"skillCount":23},"NVIDIA\u002FNeMo-Agent-Toolkit",[27,28,31,34,37,40,43,46,49,52],{"slug":5,"name":6},{"slug":29,"name":30},"engineering","Engineering",{"slug":32,"name":33},"deployment","Deployment",{"slug":35,"name":36},"performance","Performance",{"slug":38,"name":39},"simulation","Simulation",{"slug":41,"name":42},"machine-learning","Machine Learning",{"slug":44,"name":45},"deep-learning","Deep Learning",{"slug":47,"name":48},"computer-vision","Computer Vision",{"slug":50,"name":51},"hardware","Hardware",{"slug":53,"name":54},"automation","Automation","2026-07-30T05:29:49.984149",[57,73,94,121,145,167,186,210,250,276,306,322,346,367,387,409,436,454,474,493,509,528,548,566,588,607,630,651,671,700,713,726,742,754,775,790,803,817,829,841,857],{"name":58,"fullName":13,"repoUrl":59,"skillCount":14,"stars":60,"forks":61,"description":62,"topics":63,"topTags":64,"topTagCount":71,"lastUpdatedAt":72},"skills","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills",2473,281,"AI agent skills published by NVIDIA",[],[65,66,67,68,69,70],{"slug":5,"name":6},{"slug":29,"name":30},{"slug":35,"name":36},{"slug":32,"name":33},{"slug":44,"name":45},{"slug":47,"name":48},106,"2026-07-30T05:28:49.403675",{"name":74,"fullName":16,"repoUrl":75,"skillCount":17,"stars":76,"forks":77,"description":78,"topics":79,"topTags":80,"topTagCount":23,"lastUpdatedAt":93},"simready-foundation","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fsimready-foundation",52,8,"SimReady Foundation is a central repository for defining simulation content specifications based on various runtime use cases. ",[],[81,82,83,84,87,90],{"slug":5,"name":6},{"slug":38,"name":39},{"slug":29,"name":30},{"slug":85,"name":86},"documentation","Documentation",{"slug":88,"name":89},"debugging","Debugging",{"slug":91,"name":92},"physics","Physics","2026-07-14T05:34:27.068303",{"name":95,"fullName":19,"repoUrl":96,"skillCount":20,"stars":97,"forks":98,"description":99,"topics":100,"topTags":104,"topTagCount":119,"lastUpdatedAt":120},"Megatron-LM","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM",17049,4230,"Ongoing research training transformer models at scale",[101,102,103],"large-language-models","model-para","transformers",[105,106,109,112,115,118],{"slug":32,"name":33},{"slug":107,"name":108},"github","GitHub",{"slug":110,"name":111},"ci-cd","CI\u002FCD",{"slug":113,"name":114},"qa","QA",{"slug":116,"name":117},"testing","Testing",{"slug":53,"name":54},21,"2026-07-30T05:29:03.275638",{"name":122,"fullName":22,"repoUrl":123,"skillCount":23,"stars":124,"forks":125,"description":126,"topics":127,"topTags":128,"topTagCount":143,"lastUpdatedAt":144},"dgx-spark-playbooks","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdgx-spark-playbooks",1144,249,"Collection of step-by-step playbooks for setting up AI\u002FML workloads on NVIDIA DGX Spark devices with Blackwell architecture.",[],[129,130,133,136,139,140],{"slug":5,"name":6},{"slug":131,"name":132},"healthcare","Healthcare",{"slug":134,"name":135},"ai-infrastructure","AI Infrastructure",{"slug":137,"name":138},"fhir","FHIR",{"slug":32,"name":33},{"slug":141,"name":142},"data-analysis","Data Analysis",20,"2026-07-14T05:35:59.037962",{"name":146,"fullName":25,"repoUrl":147,"skillCount":23,"stars":148,"forks":149,"description":150,"topics":151,"topTags":152,"topTagCount":165,"lastUpdatedAt":166},"NeMo-Agent-Toolkit","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Agent-Toolkit",2491,704,"The NVIDIA NeMo Agent toolkit is an open-source library for efficiently connecting and optimizing teams of AI agents.",[],[153,154,157,158,161,162],{"slug":5,"name":6},{"slug":155,"name":156},"agents","Agents",{"slug":29,"name":30},{"slug":159,"name":160},"api-development","API Development",{"slug":53,"name":54},{"slug":163,"name":164},"best-practices","Best Practices",23,"2026-07-14T05:31:45.038525",{"name":168,"fullName":169,"repoUrl":170,"skillCount":23,"stars":171,"forks":172,"description":173,"topics":174,"topTags":175,"topTagCount":184,"lastUpdatedAt":185},"sop-monitoring-blueprints","NVIDIA\u002Fsop-monitoring-blueprints","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fsop-monitoring-blueprints",39,12,"Industrial SOP Monitoring Blueprints for Training & Inference",[],[176,177,180,181,182,183],{"slug":5,"name":6},{"slug":178,"name":179},"monitoring","Monitoring",{"slug":41,"name":42},{"slug":53,"name":54},{"slug":88,"name":89},{"slug":32,"name":33},19,"2026-07-14T05:36:28.162686",{"name":187,"fullName":188,"repoUrl":189,"skillCount":190,"stars":191,"forks":192,"description":193,"topics":194,"topTags":195,"topTagCount":208,"lastUpdatedAt":209},"k8s-launch-kit","NVIDIA\u002Fk8s-launch-kit","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fk8s-launch-kit",10,14,4,"K8s Launch Kit (l8k) is a CLI tool for deploying and managing NVIDIA cloud-native solutions on Kubernetes. The tool helps provide flexible deployment workflows for optimal network performance with SR-IOV, RDMA, and other networking technologies.",[],[196,199,200,203,204,207],{"slug":197,"name":198},"kubernetes","Kubernetes",{"slug":5,"name":6},{"slug":201,"name":202},"networking","Networking",{"slug":32,"name":33},{"slug":205,"name":206},"configuration","Configuration",{"slug":29,"name":30},15,"2026-07-30T05:29:15.229216",{"name":211,"fullName":212,"repoUrl":213,"skillCount":190,"stars":214,"forks":10,"description":215,"topics":216,"topTags":234,"topTagCount":248,"lastUpdatedAt":249},"NeMo-Relay","NVIDIA\u002FNeMo-Relay","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Relay",75,"Multi-language agent runtime and library for execution scope management, lifecycle events, and middleware on tool and LLM calls.",[155,217,218,219,220,221,222,223,224,225,226,5,227,228,229,230,231,232,233],"ai","atif","atof","claude-code","codex","deepagents","hermes-agent","langchain","langgraph","middleware","observability","openclaw","openinference","optimization","otel","runtime","security",[235,236,237,239,242,245],{"slug":5,"name":6},{"slug":29,"name":30},{"slug":226,"name":238},"Middleware",{"slug":240,"name":241},"python","Python",{"slug":243,"name":244},"cli","CLI",{"slug":246,"name":247},"nodejs","Node.js",24,"2026-07-23T05:43:40.456621",{"name":251,"fullName":252,"repoUrl":253,"skillCount":190,"stars":254,"forks":255,"description":256,"topics":257,"topTags":264,"topTagCount":191,"lastUpdatedAt":275},"NVFlare","NVIDIA\u002FNVFlare","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare",947,266,"NVIDIA Federated Learning Application Runtime Environment",[258,259,260,261,262,263,240],"decentralized","federated-analytics","federated-computing","federated-learning","pet","privacy-protection",[265,266,267,268,271,272],{"slug":5,"name":6},{"slug":29,"name":30},{"slug":53,"name":54},{"slug":269,"name":270},"data-pipeline","Data Pipeline",{"slug":41,"name":42},{"slug":273,"name":274},"pytorch","PyTorch","2026-07-30T05:26:21.697612",{"name":277,"fullName":278,"repoUrl":279,"skillCount":77,"stars":280,"forks":281,"description":282,"topics":283,"topTags":293,"topTagCount":172,"lastUpdatedAt":305},"cuopt-examples","NVIDIA\u002Fcuopt-examples","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuopt-examples",462,81,"NVIDIA cuOpt examples for decision optimization",[284,285,286,287,288,230,289,290,291,292],"combinatorial-optimization","gpu","linear-programming","mixed-integer-programming","operations-research","optimization-algorithms","route-optimization","traveling-salesman-problem","vehicle-routing-problem",[294,295,297,300,301,302],{"slug":5,"name":6},{"slug":230,"name":296},"Optimization",{"slug":298,"name":299},"data-modeling","Data Modeling",{"slug":53,"name":54},{"slug":141,"name":142},{"slug":303,"name":304},"analytics","Analytics","2026-07-30T05:29:20.240418",{"name":307,"fullName":308,"repoUrl":309,"skillCount":310,"stars":311,"forks":77,"description":312,"topics":313,"topTags":314,"topTagCount":23,"lastUpdatedAt":321},"CompileIQ","NVIDIA\u002FCompileIQ","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FCompileIQ",7,107,"An Optimizer for Nvidia Compilers.",[],[315,316,317,318,319,320],{"slug":5,"name":6},{"slug":35,"name":36},{"slug":29,"name":30},{"slug":53,"name":54},{"slug":205,"name":206},{"slug":141,"name":142},"2026-07-14T05:32:12.791444",{"name":323,"fullName":324,"repoUrl":325,"skillCount":310,"stars":326,"forks":327,"description":328,"topics":329,"topTags":333,"topTagCount":190,"lastUpdatedAt":345},"cudf-spark","NVIDIA\u002Fcudf-spark","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcudf-spark",987,289,"Spark RAPIDS plugin - accelerate Apache Spark with GPUs",[330,285,331,332],"big-data","rapids","spark",[334,335,336,338,341,344],{"slug":5,"name":6},{"slug":29,"name":30},{"slug":332,"name":337},"Spark",{"slug":339,"name":340},"java","Java",{"slug":342,"name":343},"benchmarking","Benchmarking",{"slug":35,"name":36},"2026-07-14T05:30:59.022808",{"name":347,"fullName":348,"repoUrl":349,"skillCount":350,"stars":351,"forks":192,"description":352,"topics":353,"topTags":354,"topTagCount":365,"lastUpdatedAt":366},"nurec-skills","NVIDIA\u002Fnurec-skills","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnurec-skills",6,16,"Agent skills for Neural Reconstruction Engine",[],[355,356,357,360,361,364],{"slug":5,"name":6},{"slug":38,"name":39},{"slug":358,"name":359},"3d","3D",{"slug":41,"name":42},{"slug":362,"name":363},"robotics","Robotics",{"slug":47,"name":48},9,"2026-07-14T05:32:38.465728",{"name":368,"fullName":369,"repoUrl":370,"skillCount":350,"stars":371,"forks":372,"description":373,"topics":374,"topTags":375,"topTagCount":172,"lastUpdatedAt":386},"Quantum-Calibration-Agent-Blueprint","NVIDIA\u002FQuantum-Calibration-Agent-Blueprint","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FQuantum-Calibration-Agent-Blueprint",57,17,"This is a reference agent blueprint for AI-powered quantum device calibration. It provides an intelligent agent interface for discovering, executing, and analyzing quantum calibration experiments with support for automated workflows and vision-based analysis.",[],[376,377,380,381,384,385],{"slug":5,"name":6},{"slug":378,"name":379},"quantum-computing","Quantum Computing",{"slug":240,"name":241},{"slug":382,"name":383},"workflow-automation","Workflow Automation",{"slug":134,"name":135},{"slug":53,"name":54},"2026-07-14T05:32:45.980229",{"name":388,"fullName":389,"repoUrl":390,"skillCount":391,"stars":350,"forks":392,"description":393,"topics":394,"topTags":395,"topTagCount":23,"lastUpdatedAt":408},"digital-health-skills","NVIDIA\u002Fdigital-health-skills","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdigital-health-skills",5,1,"NVIDIA Digital Health skills repo for agent-guided healthcare AI workflows, including evaluation, data generation, model adaptation, deployment guidance, and developer best practices.",[],[396,397,398,401,404,405],{"slug":5,"name":6},{"slug":134,"name":135},{"slug":399,"name":400},"speech","Speech",{"slug":402,"name":403},"audio","Audio",{"slug":141,"name":142},{"slug":406,"name":407},"datasets","Datasets","2026-07-14T05:35:50.295877",{"name":410,"fullName":411,"repoUrl":412,"skillCount":391,"stars":413,"forks":414,"description":415,"topics":416,"topTags":426,"topTagCount":172,"lastUpdatedAt":435},"GenerativeAIExamples","NVIDIA\u002FGenerativeAIExamples","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FGenerativeAIExamples",4115,1093,"Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.",[417,101,418,419,420,421,422,423,424,425],"gpu-acceleration","llm","llm-inference","microservice","nemo","rag","retrieval-augmented-generation","tensorrt","triton-inference-server",[427,428,429,430,433,434],{"slug":5,"name":6},{"slug":38,"name":39},{"slug":53,"name":54},{"slug":431,"name":432},"charts","Charts",{"slug":205,"name":206},{"slug":141,"name":142},"2026-07-14T05:32:19.171135",{"name":437,"fullName":438,"repoUrl":439,"skillCount":192,"stars":440,"forks":441,"description":442,"topics":443,"topTags":444,"topTagCount":310,"lastUpdatedAt":453},"deepops","NVIDIA\u002Fdeepops","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdeepops",1460,353,"Tools for building GPU clusters",[],[445,448,449,450,451,452],{"slug":446,"name":447},"infrastructure","Infrastructure",{"slug":5,"name":6},{"slug":32,"name":33},{"slug":88,"name":89},{"slug":197,"name":198},{"slug":178,"name":179},"2026-07-17T06:07:15.184954",{"name":455,"fullName":456,"repoUrl":457,"skillCount":192,"stars":458,"forks":459,"description":460,"topics":461,"topTags":462,"topTagCount":365,"lastUpdatedAt":473},"nvidia-resiliency-ext","NVIDIA\u002Fnvidia-resiliency-ext","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvidia-resiliency-ext",308,56,"NVIDIA Resiliency Extension is a python package for framework developers and users to implement fault-tolerant features. It improves the effective training time by minimizing the downtime due to failures and interruptions.",[],[463,464,467,468,469,472],{"slug":5,"name":6},{"slug":465,"name":466},"analysis","Analysis",{"slug":342,"name":343},{"slug":88,"name":89},{"slug":470,"name":471},"logs","Logs",{"slug":134,"name":135},"2026-07-14T05:36:19.02075",{"name":475,"fullName":476,"repoUrl":477,"skillCount":478,"stars":479,"forks":480,"description":481,"topics":482,"topTags":483,"topTagCount":310,"lastUpdatedAt":492},"cuEquivariance","NVIDIA\u002FcuEquivariance","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FcuEquivariance",3,409,36,"cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural networks. Also includes kernels for accelerated structure prediction.",[],[484,485,488,489,490,491],{"slug":44,"name":45},{"slug":486,"name":487},"mathematics","Mathematics",{"slug":5,"name":6},{"slug":29,"name":30},{"slug":91,"name":92},{"slug":240,"name":241},"2026-07-23T05:43:52.496117",{"name":494,"fullName":495,"repoUrl":496,"skillCount":478,"stars":480,"forks":350,"description":497,"topics":498,"topTags":499,"topTagCount":365,"lastUpdatedAt":508},"nv-sflow","NVIDIA\u002Fnv-sflow","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnv-sflow","A Python CLI workflow orchestrator with pluggable backends (e.g. local, Slurm) for running declarative YAML DAGs, collecting logs, and organizing outputs consistently.",[],[500,501,502,505,506,507],{"slug":5,"name":6},{"slug":382,"name":383},{"slug":503,"name":504},"code-review","Code Review",{"slug":205,"name":206},{"slug":88,"name":89},{"slug":29,"name":30},"2026-07-23T06:06:17.700089",{"name":510,"fullName":511,"repoUrl":512,"skillCount":478,"stars":513,"forks":165,"description":514,"topics":515,"topTags":519,"topTagCount":391,"lastUpdatedAt":527},"nvcf","NVIDIA\u002Fnvcf","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fnvcf",176,"Platform for deploying and routing GPU-accelerated inference, streaming, and batch workloads at scale.",[516,285,517,197,5,518],"cloud-functions","inference","serverless-gpu",[520,521,522,523,524],{"slug":243,"name":244},{"slug":32,"name":33},{"slug":5,"name":6},{"slug":446,"name":447},{"slug":525,"name":526},"cloud","Cloud","2026-07-30T05:29:41.025233",{"name":529,"fullName":530,"repoUrl":531,"skillCount":478,"stars":532,"forks":533,"description":534,"topics":535,"topTags":536,"topTagCount":190,"lastUpdatedAt":547},"OSMO","NVIDIA\u002FOSMO","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOSMO",192,40,"The developer-first platform for scaling complex Physical AI workloads across heterogeneous compute—unifying training GPUs, simulation clusters, and edge devices in a simple YAML",[],[537,538,539,542,545,546],{"slug":5,"name":6},{"slug":197,"name":198},{"slug":540,"name":541},"aws","AWS",{"slug":543,"name":544},"azure","Azure",{"slug":243,"name":244},{"slug":205,"name":206},"2026-07-14T05:33:26.717488",{"name":549,"fullName":550,"repoUrl":551,"skillCount":478,"stars":23,"forks":478,"description":552,"topics":553,"topTags":554,"topTagCount":365,"lastUpdatedAt":565},"paidf-anomalygen","NVIDIA\u002Fpaidf-anomalygen","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpaidf-anomalygen","Diffusion-based pipeline for generating photorealistic, mask-aligned synthetic anomaly images for industrial visual inspection from only a few real examples",[],[555,556,557,558,561,562],{"slug":5,"name":6},{"slug":41,"name":42},{"slug":32,"name":33},{"slug":559,"name":560},"docker","Docker",{"slug":29,"name":30},{"slug":563,"name":564},"evals","Evals","2026-07-14T05:31:20.483849",{"name":567,"fullName":568,"repoUrl":569,"skillCount":478,"stars":570,"forks":248,"description":571,"topics":572,"topTags":580,"topTagCount":310,"lastUpdatedAt":587},"physicsnemo-cfd","NVIDIA\u002Fphysicsnemo-cfd","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysicsnemo-cfd",131,"L​ibrary for using the models trained in PhysicsNeMo in Engineering and CFD workflows ",[573,574,575,576,577,5,578,579],"aerodynamics","cae","cfd","fluid-dynamics","nim","nvidia-warp","physicsnemo",[581,582,583,584,585,586],{"slug":342,"name":343},{"slug":5,"name":6},{"slug":91,"name":92},{"slug":38,"name":39},{"slug":465,"name":466},{"slug":406,"name":407},"2026-07-14T05:33:35.569433",{"name":589,"fullName":590,"repoUrl":591,"skillCount":592,"stars":593,"forks":594,"description":595,"topics":596,"topTags":598,"topTagCount":391,"lastUpdatedAt":606},"cuopt","NVIDIA\u002Fcuopt","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fcuopt",2,974,209,"GPU accelerated decision optimization ",[597,285,286,230],"cuda",[599,600,601,602,603],{"slug":5,"name":6},{"slug":230,"name":296},{"slug":159,"name":160},{"slug":53,"name":54},{"slug":604,"name":605},"routing","Routing","2026-07-14T05:31:12.318397",{"name":608,"fullName":609,"repoUrl":610,"skillCount":592,"stars":611,"forks":414,"description":612,"topics":613,"topTags":614,"topTagCount":77,"lastUpdatedAt":629},"OpenShell","NVIDIA\u002FOpenShell","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell",7681,"OpenShell is the safe, private runtime for autonomous AI agents.",[],[615,616,619,620,623,626],{"slug":5,"name":6},{"slug":617,"name":618},"code-analysis","Code Analysis",{"slug":107,"name":108},{"slug":621,"name":622},"policy","Policy",{"slug":624,"name":625},"pull-requests","Pull Requests",{"slug":627,"name":628},"sandboxing","Sandboxing","2026-07-30T05:29:34.278583",{"name":631,"fullName":632,"repoUrl":633,"skillCount":392,"stars":372,"forks":350,"description":634,"topics":635,"topTags":636,"topTagCount":391,"lastUpdatedAt":650},"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.",[],[637,640,643,644,647],{"slug":638,"name":639},"ar","AR",{"slug":641,"name":642},"immersive","Immersive",{"slug":5,"name":6},{"slug":645,"name":646},"vr","VR",{"slug":648,"name":649},"webxr","WebXR","2026-07-14T05:36:21.571154",{"name":652,"fullName":653,"repoUrl":654,"skillCount":392,"stars":655,"forks":656,"description":657,"topics":658,"topTags":666,"topTagCount":478,"lastUpdatedAt":670},"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",[659,660,240,661,662,378,663,664,665],"cpp","hacktoberfest","quantum","quantum-algorithms","quantum-machine-learning","quantum-programming-language","unitaryhack",[667,668,669],{"slug":5,"name":6},{"slug":240,"name":241},{"slug":378,"name":379},"2026-07-14T05:31:30.970327",{"name":672,"fullName":673,"repoUrl":674,"skillCount":392,"stars":675,"forks":676,"description":677,"topics":678,"topTags":695,"topTagCount":192,"lastUpdatedAt":55},"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.",[679,680,597,681,682,44,683,684,685,285,686,687,688,689,690,691,692,5,693,694],"attention","blackwell","cuda-kernels","cuda-toolkit","flash-attention","fp8","gemm","grouped-gemm","hopper","mixture-of-experts","moe","mxfp8","normalization","nvfp4","sdpa","transformer",[696,697,698,699],{"slug":159,"name":160},{"slug":85,"name":86},{"slug":5,"name":6},{"slug":240,"name":241},{"name":701,"fullName":702,"repoUrl":703,"skillCount":392,"stars":76,"forks":172,"description":704,"topics":705,"topTags":706,"topTagCount":192,"lastUpdatedAt":712},"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. ",[],[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":23},[874,889,900,911,923,933,944,956,969,980,991],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":171,"repoUrl":170,"updatedAt":888},"sop-build","orchestrate end-to-end SOP monitoring pipelines","Orchestrate the end-to-end SOP pipeline, including preflight prerequisite checks, verifying models and downloading assets, generating the DeepStream SOP microservice with RTSP output, evaluating the microservice, and building, deploying, and testing the VSS SOP blueprint. Use when asked to run the full SOP pipeline, set up the SOP pipeline from scratch, execute preflight checks, verify models, download assets, generate the SOP microservice, evaluate the microservice, build the VSS blueprint, deploy the VSS blueprint, test the VSS blueprint, or manage the complete build-evaluate-deploy-test cycle.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883,884,885],{"name":54,"slug":53,"type":881},"tag",{"name":30,"slug":29,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},{"name":886,"slug":887,"type":881},"Video","video","2026-07-14T05:32:48.503678",{"slug":890,"name":890,"fn":891,"description":892,"org":893,"tags":894,"stars":171,"repoUrl":170,"updatedAt":899},"sop-by-action-eval","run by-action VLM evaluations","Use when running by-action VLM evaluation (per-action-clip inference + accuracy metrics) against the BP evaluation-ms HTTP API. Invoked as \u002Fsop-by-action-eval \u003Cinputs.yaml> [natural language parameter overrides]",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[895,896,897,898],{"name":564,"slug":563,"type":881},{"name":42,"slug":41,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:36:13.260585",{"slug":901,"name":901,"fn":902,"description":903,"org":904,"tags":905,"stars":171,"repoUrl":170,"updatedAt":910},"sop-cr-finetuning","fine-tune VLM models for SOP monitoring","Fine-tune Cosmos-Reason2 (CR2) VLM for SOP monitoring. Use when you need to launch and monitor a VLM training run with a given dataset ID.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[906,907,908,909],{"name":135,"slug":134,"type":881},{"name":42,"slug":41,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:36:22.874218",{"slug":912,"name":912,"fn":913,"description":914,"org":915,"tags":916,"stars":171,"repoUrl":170,"updatedAt":922},"sop-data-augmentation","augment annotated datasets","Use when the user wants to run data augmentation on an annotated dataset, configure augmentation parameters, check augmentation status, or understand what each QA augmentation type does (BCQ, MCQ, GQA, DMCQ, DSQA, ENQA)",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[917,918,919,920,921],{"name":812,"slug":811,"type":881},{"name":407,"slug":406,"type":881},{"name":42,"slug":41,"type":881},{"name":6,"slug":5,"type":881},{"name":114,"slug":113,"type":881},"2026-07-14T05:36:20.305657",{"slug":924,"name":924,"fn":925,"description":926,"org":927,"tags":928,"stars":171,"repoUrl":170,"updatedAt":932},"sop-ddm-finetuning","fine-tune DDM-Net models for SOP monitoring","Fine-tune DDM-Net temporal boundary detector for SOP monitoring. Use when you need to launch and monitor a DDM-Net training run with a given dataset ID.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[929,930,931],{"name":45,"slug":44,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:35:16.203684",{"slug":934,"name":934,"fn":935,"description":936,"org":937,"tags":938,"stars":171,"repoUrl":170,"updatedAt":943},"sop-e2e-inference","run end-to-end inference evaluations","Use when running the e2e evaluation pipeline (temporal segmentation + action recognition + accuracy) against the BP evaluation-ms HTTP API. Invoked as \u002Fsop-e2e-inference \u003Cinputs.yaml> [natural language parameter overrides]",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[939,940,941,942],{"name":564,"slug":563,"type":881},{"name":42,"slug":41,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:36:15.23958",{"slug":945,"name":945,"fn":946,"description":947,"org":948,"tags":949,"stars":171,"repoUrl":170,"updatedAt":185},"sop-ft-orchestrate","orchestrate SOP fine-tuning pipelines","Autonomous end-to-end orchestrator for SOP fine-tuning. Runs the full Import → Augment → DDM Train → VLM Train → Evaluate → RCA loop. Interprets RCA findings across DDM, VLM and augment axes, applies config fixes autonomously, and iterates until success criteria are met or max_pipeline_iterations reached. Call with a path to an inputs.yaml or with natural language.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[950,951,952,953],{"name":54,"slug":53,"type":881},{"name":42,"slug":41,"type":881},{"name":6,"slug":5,"type":881},{"name":954,"slug":955,"type":881},"Orchestration","orchestration",{"slug":957,"name":957,"fn":958,"description":959,"org":960,"tags":961,"stars":171,"repoUrl":170,"updatedAt":968},"sop-rca","perform root cause analysis on SOP pipelines","Root cause analysis for SOP monitoring pipeline failures. Analyzes end-to-end evaluation logs, DDM temporal segmentation, VLM action recognition, training data, and fine-tuning configs to identify failure patterns and produce an evidence-driven RCA report with actionable improvement recommendations.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[962,963,964,965,966],{"name":270,"slug":269,"type":881},{"name":89,"slug":88,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},{"name":967,"slug":227,"type":881},"Observability","2026-07-14T05:34:28.340827",{"slug":970,"name":970,"fn":971,"description":972,"org":973,"tags":974,"stars":171,"repoUrl":170,"updatedAt":979},"vss-sop-build","build and customize VSS SOP blueprints","Build a custom VSS SOP blueprint from the VSS 3.1 base, then deploy and test in a loop until fully operational. Use when asked to create the SOP blueprint structure, customize VSS compose for SOP, configure SOP services, set up the VSS agent for SOP, or scaffold the SOP app layer on top of met-blueprints 3.1.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[975,976,977,978],{"name":33,"slug":32,"type":881},{"name":30,"slug":29,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:32:47.246466",{"slug":981,"name":981,"fn":982,"description":983,"org":984,"tags":985,"stars":171,"repoUrl":170,"updatedAt":990},"vss-sop-deploy","deploy VSS SOP monitoring pipelines","Build the DS-SOP Docker image and deploy the VSS SOP blueprint end-to-end. Use when asked to deploy SOP, build DS SOP, install SOP, set up the SOP pipeline, verify SOP models, start the SOP blueprint, simulate RTSP for SOP, run the SOP API test, or tear down SOP.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[986,987,988,989],{"name":33,"slug":32,"type":881},{"name":560,"slug":559,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:32:50.992812",{"slug":992,"name":992,"fn":993,"description":994,"org":995,"tags":996,"stars":171,"repoUrl":170,"updatedAt":1002},"vss-sop-test","test and debug VSS SOP deployments","Run post-deployment tests for the VSS SOP blueprint. Checks service health, ELK data pipeline, VIOS recording\u002Flivestream, and VSS agent (MCP, LLM, VLM, snapshot, video, report). Auto-debugs failures. Use when asked to test SOP, verify SOP deployment, check SOP services, validate SOP, run SOP health checks, or troubleshoot SOP after deploy.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[997,998,999,1000,1001],{"name":54,"slug":53,"type":881},{"name":89,"slug":88,"type":881},{"name":179,"slug":178,"type":881},{"name":6,"slug":5,"type":881},{"name":117,"slug":116,"type":881},"2026-07-14T05:32:49.746967"]