[{"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":190},[874,886,899,908,919,930,941,953,966,976],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":254,"repoUrl":253,"updatedAt":885},"autofl-nvflare","develop NVFlare Auto-FL experiments","Help coding agents work on an NVFlare-based Auto-FL harness that follows an autoresearch-style loop. Use when the user wants to create, edit, debug, or extend program.md, task folders such as tasks\u002Fcifar10\u002F and tasks\u002Fvlm_med\u002F, task-local job.py, client.py, model.py, shared custom_aggregators.py, mutation policies, results.tsv logging, or coding-agent prompts for a bounded federated-learning research loop. This skill is specifically for NVFlare harness work where the Client API loop, DIFF upload contract, and NUM_STEPS_CURRENT_ROUND metadata must stay intact unless the user explicitly asks for a protocol upgrade.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883,884],{"name":156,"slug":155,"type":881},"tag",{"name":54,"slug":53,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},"2026-07-14T05:36:01.532575",{"slug":887,"name":887,"fn":888,"description":889,"org":890,"tags":891,"stars":254,"repoUrl":253,"updatedAt":898},"autofl-nvflare-report","generate NVFlare experiment reports","Generate and commit a markdown report after an Auto-FL NVFlare autoresearch experiment has been manually stopped. Use when the user asks to summarize a stopped campaign, report achieved improvements, explain implemented literature-derived ideas and sources, refresh progress plots, capture pasted agent model\u002Feffort\u002Fcost context when available, or commit the final report and progress plot to the current experiment branch.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[892,893,894,895],{"name":54,"slug":53,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":896,"slug":897,"type":881},"Reporting","reporting","2026-07-14T05:36:00.281416",{"slug":900,"name":900,"fn":901,"description":902,"org":903,"tags":904,"stars":254,"repoUrl":253,"updatedAt":275},"nvflare-autofl","optimize NVFLARE training jobs","Use for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[905,906,907],{"name":42,"slug":41,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"slug":909,"name":909,"fn":910,"description":911,"org":912,"tags":913,"stars":254,"repoUrl":253,"updatedAt":918},"nvflare-autofl-report","generate NVFLARE Auto-FL campaign reports","Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[914,915,916,917],{"name":42,"slug":41,"type":881},{"name":6,"slug":5,"type":881},{"name":896,"slug":897,"type":881},{"name":39,"slug":38,"type":881},"2026-07-30T05:26:14.661827",{"slug":920,"name":920,"fn":921,"description":922,"org":923,"tags":924,"stars":254,"repoUrl":253,"updatedAt":929},"nvflare-convert-lightning","convert PyTorch Lightning code to NVFLARE","Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; do not use for plain PyTorch, other frameworks, deployment, POC\u002Fproduction lifecycle, or experiment workflows.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[925,926,927,928],{"name":270,"slug":269,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":274,"slug":273,"type":881},"2026-07-30T05:26:15.761027",{"slug":931,"name":931,"fn":932,"description":933,"org":934,"tags":935,"stars":254,"repoUrl":253,"updatedAt":940},"nvflare-convert-pytorch","convert PyTorch code to NVFLARE","Convert existing PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; do not use for other frameworks, deployment, POC\u002Fproduction lifecycle, or experiment workflows.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[936,937,938,939],{"name":270,"slug":269,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":274,"slug":273,"type":881},"2026-07-30T05:26:20.645495",{"slug":942,"name":942,"fn":943,"description":944,"org":945,"tags":946,"stars":254,"repoUrl":253,"updatedAt":952},"nvflare-diagnose-job","diagnose failed NVFLARE jobs","Use when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[947,948,949,950],{"name":89,"slug":88,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":951,"slug":227,"type":881},"Observability","2026-07-30T05:26:17.665774",{"slug":954,"name":954,"fn":955,"description":956,"org":957,"tags":958,"stars":254,"repoUrl":253,"updatedAt":965},"nvflare-fed-stats","compute federated statistics on data","Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min\u002Fmax) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC\u002Fproduction lifecycle, or failed-job diagnosis.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[959,960,961,962],{"name":142,"slug":141,"type":881},{"name":42,"slug":41,"type":881},{"name":6,"slug":5,"type":881},{"name":963,"slug":964,"type":881},"Statistics","statistics","2026-07-30T05:26:19.63262",{"slug":967,"name":967,"fn":968,"description":969,"org":970,"tags":971,"stars":254,"repoUrl":253,"updatedAt":975},"nvflare-orient","route NVFLARE workflow requests","Route open-ended or ambiguous NVFLARE requests by inspecting the local project and recommending one specific workflow skill without editing files; explicit conversions normally route directly to a converter, except when inspection reports unresolved Trainer ownership or active Lightning and Hugging Face Trainer entrypoints.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[972,973,974],{"name":54,"slug":53,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},"2026-07-30T05:26:16.653576",{"slug":977,"name":977,"fn":978,"description":979,"org":980,"tags":981,"stars":254,"repoUrl":253,"updatedAt":986},"nvflare-shared","manage NVFLARE conversion and reporting templates","Shared NVFLARE conversion references and templates used by the other NVFLARE agent skills (conversion workflow, validation ladder, dependency install, model exchange, metrics\u002Fartifact reporting, and the custom aggregator template). Not a user-triggered skill; loaded via references from the conversion skills.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[982,983,984,985],{"name":54,"slug":53,"type":881},{"name":270,"slug":269,"type":881},{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},"2026-07-30T05:26:18.65149"]