[{"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":478},[874,885,896],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":480,"repoUrl":496,"updatedAt":508},"sflow-code-review","review sflow code changes","Review sflow code changes for functional defects, modular by-purpose structure, duplicated logic that should be consolidated, and adequate unit + e2e CLI test coverage. Use when reviewing an sflow diff, branch, PR, staged changes, or when the user asks for a code review of sflow.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883,884],{"name":504,"slug":503,"type":881},"tag",{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":117,"slug":116,"type":881},{"slug":886,"name":886,"fn":887,"description":888,"org":889,"tags":890,"stars":480,"repoUrl":496,"updatedAt":895},"sflow-error-analysis","troubleshoot sflow workflow errors","Diagnose and troubleshoot sflow workflow errors from log output, error messages, and task failures. Covers config validation errors, expression resolution failures, backend issues (Slurm, Docker, Kubernetes\u002Fkubectl\u002FRBAC), probe timeouts, task crashes, S3 storage\u002Fupload failures, and batch submission problems. Use when the user encounters an sflow error, pastes error output, asks to debug a failed workflow, or asks about sflow troubleshooting.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[891,892,893,894],{"name":89,"slug":88,"type":881},{"name":471,"slug":470,"type":881},{"name":6,"slug":5,"type":881},{"name":383,"slug":382,"type":881},"2026-07-23T05:43:45.493836",{"slug":897,"name":897,"fn":898,"description":899,"org":900,"tags":901,"stars":480,"repoUrl":496,"updatedAt":908},"writing-sflow-yaml","configure sflow YAML workflows","Write, create, and modify sflow YAML workflow configuration files. Covers schema structure, variables, task DAGs, backends (local, slurm, docker, kubernetes), operators, probes, replicas, artifacts, result parsing, storage\u002Fuploads (S3), hardware monitoring, and modular composition. Use when the user asks to create an sflow YAML, configure a workflow, set up inference serving on Slurm or Kubernetes, or asks about sflow YAML syntax.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[902,903,904,905],{"name":206,"slug":205,"type":881},{"name":6,"slug":5,"type":881},{"name":383,"slug":382,"type":881},{"name":906,"slug":907,"type":881},"YAML","yaml","2026-07-23T05:43:46.500651"]