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",[],[656,657,658,659,660,663],{"slug":5,"name":6},{"slug":32,"name":33},{"slug":29,"name":30},{"slug":163,"name":164},{"slug":661,"name":662},"reporting","Reporting",{"slug":287,"name":664},"Security","2026-08-05T05:58:23.522352",{"name":667,"fullName":668,"repoUrl":669,"skillCount":635,"stars":670,"forks":441,"description":671,"topics":672,"topTags":673,"topTagCount":77,"lastUpdatedAt":688},"OpenShell","NVIDIA\u002FOpenShell","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell",7681,"OpenShell is the safe, private runtime for autonomous AI agents.",[],[674,675,678,679,682,685],{"slug":5,"name":6},{"slug":676,"name":677},"code-analysis","Code Analysis",{"slug":157,"name":158},{"slug":680,"name":681},"policy","Policy",{"slug":683,"name":684},"pull-requests","Pull Requests",{"slug":686,"name":687},"sandboxing","Sandboxing","2026-07-30T05:29:34.278583",{"name":690,"fullName":691,"repoUrl":692,"skillCount":419,"stars":399,"forks":378,"description":693,"topics":694,"topTags":695,"topTagCount":418,"lastUpdatedAt":709},"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.",[],[696,699,702,703,706],{"slug":697,"name":698},"ar","AR",{"slug":700,"name":701},"immersive","Immersive",{"slug":5,"name":6},{"slug":704,"name":705},"vr","VR",{"slug":707,"name":708},"webxr","WebXR","2026-07-14T05:36:21.571154",{"name":711,"fullName":712,"repoUrl":713,"skillCount":419,"stars":714,"forks":715,"description":716,"topics":717,"topTags":725,"topTagCount":504,"lastUpdatedAt":729},"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",[718,719,207,720,721,405,722,723,724],"cpp","hacktoberfest","quantum","quantum-algorithms","quantum-machine-learning","quantum-programming-language","unitaryhack",[726,727,728],{"slug":5,"name":6},{"slug":207,"name":294},{"slug":405,"name":406},"2026-07-14T05:31:30.970327",{"name":731,"fullName":732,"repoUrl":733,"skillCount":419,"stars":734,"forks":735,"description":736,"topics":737,"topTags":754,"topTagCount":246,"lastUpdatedAt":759},"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.",[738,739,622,740,741,44,742,743,744,312,745,746,747,748,749,750,751,5,752,753],"attention","blackwell","cuda-kernels","cuda-toolkit","flash-attention","fp8","gemm","grouped-gemm","hopper","mixture-of-experts","moe","mxfp8","normalization","nvfp4","sdpa","transformer",[755,756,757,758],{"slug":186,"name":187},{"slug":85,"name":86},{"slug":5,"name":6},{"slug":207,"name":294},"2026-08-02T05:47:02.685859",{"name":761,"fullName":762,"repoUrl":763,"skillCount":419,"stars":764,"forks":20,"description":765,"topics":766,"topTags":767,"topTagCount":246,"lastUpdatedAt":772},"holodeck","NVIDIA\u002Fholodeck","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fholodeck",31,"Holodeck is a project to create test environments optimised for GPU projects.",[],[768,769,770,771],{"slug":296,"name":297},{"slug":472,"name":473},{"slug":5,"name":6},{"slug":166,"name":167},"2026-07-14T05:36:29.548299",{"name":774,"fullName":775,"repoUrl":776,"skillCount":419,"stars":777,"forks":778,"description":779,"topics":780,"topTags":781,"topTagCount":246,"lastUpdatedAt":788},"infra-controller","NVIDIA\u002Finfra-controller","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Finfra-controller",226,150,"NVIDIA Infra Controller - Hardware Lifecycle Management and multitenant networking",[],[782,783,784,785],{"slug":186,"name":187},{"slug":29,"name":30},{"slug":5,"name":6},{"slug":786,"name":787},"rest-api","REST API","2026-07-14T05:32:52.235912",{"name":790,"fullName":791,"repoUrl":792,"skillCount":419,"stars":244,"forks":504,"description":793,"topics":794,"topTags":795,"topTagCount":246,"lastUpdatedAt":800},"NeMo-Fabric","NVIDIA\u002FNeMo-Fabric","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNeMo-Fabric","Project NVIDIA NeMo Fabric",[],[796,797,798,799],{"slug":88,"name":89},{"slug":29,"name":30},{"slug":5,"name":6},{"slug":166,"name":167},"2026-07-23T06:06:22.954737",{"name":802,"fullName":803,"repoUrl":804,"skillCount":419,"stars":805,"forks":806,"description":807,"topics":808,"topTags":813,"topTagCount":504,"lastUpdatedAt":821},"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",[809,810,5,282,811,686,812],"ai-agents","hermes","openshell","typescript",[814,815,818],{"slug":85,"name":86},{"slug":816,"name":817},"mcp","MCP",{"slug":819,"name":820},"search","Search","2026-07-20T06:00:01.461044",{"name":823,"fullName":824,"repoUrl":825,"skillCount":419,"stars":826,"forks":827,"description":828,"topics":829,"topTags":830,"topTagCount":504,"lastUpdatedAt":836},"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",[],[831,832,833],{"slug":41,"name":42},{"slug":5,"name":6},{"slug":834,"name":835},"research","Research","2026-07-14T05:36:16.524177",{"name":838,"fullName":839,"repoUrl":840,"skillCount":419,"stars":841,"forks":842,"description":671,"topics":843,"topTags":844,"topTagCount":246,"lastUpdatedAt":849},"OpenShell-Community","NVIDIA\u002FOpenShell-Community","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FOpenShell-Community",172,63,[],[845,846,847,848],{"slug":296,"name":297},{"slug":29,"name":30},{"slug":157,"name":158},{"slug":786,"name":787},"2026-07-14T05:36:26.866398",{"name":851,"fullName":852,"repoUrl":853,"skillCount":419,"stars":419,"forks":419,"description":854,"topics":855,"topTags":856,"topTagCount":246,"lastUpdatedAt":863},"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",[],[857,860,861,862],{"slug":858,"name":859},"data-engineering","Data Engineering",{"slug":41,"name":42},{"slug":5,"name":6},{"slug":38,"name":39},"2026-07-14T05:36:30.828379",{"name":865,"fullName":866,"repoUrl":867,"skillCount":419,"stars":635,"forks":419,"description":868,"topics":869,"topTags":870,"topTagCount":246,"lastUpdatedAt":875},"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",[],[871,872,873,874],{"slug":53,"name":54},{"slug":214,"name":215},{"slug":585,"name":586},{"slug":5,"name":6},"2026-07-14T05:32:21.766434",{"name":877,"fullName":878,"repoUrl":879,"skillCount":419,"stars":418,"forks":419,"description":880,"topics":881,"topTags":882,"topTagCount":246,"lastUpdatedAt":887},"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",[],[883,884,885,886],{"slug":858,"name":859},{"slug":585,"name":586},{"slug":5,"name":6},{"slug":38,"name":39},"2026-07-14T05:36:25.595046",{"name":889,"fullName":890,"repoUrl":891,"skillCount":419,"stars":301,"forks":418,"description":892,"topics":893,"topTags":894,"topTagCount":246,"lastUpdatedAt":901},"physical-ai-data-factory","NVIDIA\u002Fphysical-ai-data-factory","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fphysical-ai-data-factory","Synthetic Data Generation Workflows for Physical AI",[],[895,896,897,900],{"slug":53,"name":54},{"slug":47,"name":48},{"slug":898,"name":899},"images","Images",{"slug":5,"name":6},"2026-08-05T05:58:23.166103",{"name":903,"fullName":904,"repoUrl":905,"skillCount":419,"stars":906,"forks":907,"description":908,"topics":909,"topTags":910,"topTagCount":246,"lastUpdatedAt":917},"TileGym","NVIDIA\u002FTileGym","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTileGym",770,79,"Helpful kernel tutorials, examples and SKILLs for tile-based GPU programming",[],[911,912,913,914],{"slug":29,"name":30},{"slug":5,"name":6},{"slug":35,"name":36},{"slug":915,"name":916},"rust","Rust","2026-07-14T05:33:14.807388",{"name":919,"fullName":920,"repoUrl":921,"skillCount":419,"stars":922,"forks":923,"description":924,"topics":925,"topTags":928,"topTagCount":504,"lastUpdatedAt":932},"torch-harmonics","NVIDIA\u002Ftorch-harmonics","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Ftorch-harmonics",686,69,"Differentiable signal processing on the sphere for PyTorch",[41,217,926,927],"signal-processing","sphere",[929,930,931],{"slug":85,"name":86},{"slug":29,"name":30},{"slug":5,"name":6},"2026-07-14T05:32:03.82994",{"items":934,"total":504},[935,946,956],{"slug":936,"name":936,"fn":937,"description":938,"org":939,"tags":940,"stars":618,"repoUrl":617,"updatedAt":945},"warp-compile-time-optimizer","optimize Warp compile and startup times","Use when compile time or startup time is the problem in code that uses Warp: a request to improve, optimize, or cut compile times; an app that is slow to start or stalls at the first wp.launch; seconds of compiling before real work begins; JIT modules recompiling on every run or every CI job. Only applies when the code being optimized uses Warp kernels. Not for steady-state kernel runtime, memory, correctness, building Warp itself from source, or nvcc\u002FC++ build times.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[941,943,944],{"name":30,"slug":29,"type":942},"tag",{"name":6,"slug":5,"type":942},{"name":36,"slug":35,"type":942},"2026-08-05T05:58:13.178762",{"slug":947,"name":947,"fn":948,"description":949,"org":950,"tags":951,"stars":618,"repoUrl":617,"updatedAt":630},"warp-debug-gradients","debug gradients in Warp programs","Use to diagnose and fix incorrect gradients in differentiable Warp programs. Anything trained, optimized, calibrated, or fit through Warp kernels depends on wp.Tape gradients, so treat any misbehavior of such a workflow as a gradient problem until proven otherwise — use this when training diverges or NaNs, won't train at all, stalls or plateaus above the expected loss, converges to a wrong or biased answer, is worse than a reference implementation, works at small scale but fails at production scale, or fails a QA\u002Fvalidation recheck. Also for explicit symptoms — exploding, NaN\u002Finf, zero, or subtly wrong gradients, suspected wp.Tape\u002Fbackward issues, gradcheck failures — but users usually describe only the surface symptom (\"the sim explodes\", \"the fit gets dragged toward outliers\") without mentioning gradients: make that leap. Not for forward-only Warp work, build\u002Finstall problems, or autograd issues in other frameworks without Warp.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[952,953,954,955],{"name":89,"slug":88,"type":942},{"name":30,"slug":29,"type":942},{"name":6,"slug":5,"type":942},{"name":36,"slug":35,"type":942},{"slug":957,"name":957,"fn":958,"description":959,"org":960,"tags":961,"stars":618,"repoUrl":617,"updatedAt":966},"warp-eval","evaluate NVIDIA Warp simulation candidates","Evaluate whether an existing hot path is a credible NVIDIA Warp candidate. Use for irregular or spatial queries, particle or geometry simulation, branch-heavy loops, many small launches, host fallbacks, or large intermediates. CPU-only code and absent GPU dependencies are normal unless NVIDIA is prohibited. Exclude required cross-vendor or CPU-only deployment, vendor-lowered dense or NN layers, general Warp API questions, and already-selected Warp kernels. Contribution policy alone is not exclusion.\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[962,963,964,965],{"name":30,"slug":29,"type":942},{"name":6,"slug":5,"type":942},{"name":36,"slug":35,"type":942},{"name":39,"slug":38,"type":942},"2026-08-05T05:58:27.275636"]