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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":310},[874,885,895,907,918,928,938],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":311,"repoUrl":309,"updatedAt":884},"compileiq-author-objective","author CompileIQ objective functions","Use when writing the objective_function= passed to Search(). Covers the two legal signatures (compiler-only str vs mixed list), the baseline-knockout branch, per-eval cache busting, framework-specific --apply-controls injection (raw PTXAS, NVCC, Triton, Helion, cuTeDSL\u002FFA4, FlashInfer), correctness-before-timing, INVALID_SCORE handling, and the Debug-pack O0\u002FO3 ACF-injection canary that must pass before launching a search. Triggers on \"objective function\", \"apply-controls\", \"INVALID_SCORE\", \"save_compiler_config\", \"baseline knockout\", \"BASELINE_CONFIG\", \"every config returns the same score\", \"TypeError fromhex\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883],{"name":30,"slug":29,"type":881},"tag",{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:32:10.176779",{"slug":886,"name":886,"fn":887,"description":888,"org":889,"tags":890,"stars":311,"repoUrl":309,"updatedAt":321},"compileiq-booster-pack","apply CompileIQ booster packs to compilers","Use BEFORE running a full CompileIQ search. Walks through downloading a Booster Pack from NVIDIA\u002FCompileIQ GitHub Releases, applying ACF candidates one at a time to the user's compiler (raw PTXAS, NVCC, Triton, Helion, FlashInfer), and keeping only candidates that compile, pass correctness, and beat the no-ACF baseline. Includes the mandatory Debug-pack O0\u002FO3 ACF-injection canary that proves the ACF is reaching PTXAS. Triggers on \"booster pack\", \"ACF\", \"apply-controls\", \"speed up without searching\", \"helion fp8\", \"flashinfer batch decode\", \"debug pack\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[891,892,893,894],{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":296,"slug":230,"type":881},{"name":36,"slug":35,"type":881},{"slug":896,"name":896,"fn":897,"description":898,"org":899,"tags":900,"stars":311,"repoUrl":309,"updatedAt":906},"compileiq-bootstrap","bootstrap CompileIQ project environments","Use when starting a fresh CompileIQ project, hitting a socket timeout, or before running any other compileiq-* skill. Verifies CUDA 13.3+, ptxas, GPU access, that `from compileiq.ciq import Search` and friends resolve, and that `PtxasSearchSpace().retrieve()` returns a real path. Documents the env vars that control timeouts, caching, and search-space mirroring. Triggers on \"set up compileiq\", \"compileiq doesn't work\", \"socket timeout\", \"where do search spaces come from\", \"air-gapped compileiq\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[901,902,903],{"name":30,"slug":29,"type":881},{"name":6,"slug":5,"type":881},{"name":904,"slug":905,"type":881},"Onboarding","onboarding","2026-07-14T05:32:11.472149",{"slug":908,"name":908,"fn":909,"description":910,"org":911,"tags":912,"stars":311,"repoUrl":309,"updatedAt":917},"compileiq-debug","debug CompileIQ search and evaluation failures","Use when something is wrong: Search() hangs, all evaluations return INVALID_SCORE, scores aren't improving, every config returns the same number, ptxas errors fill the log, CV% is too high, or a winning ACF candidate needs NCU profiling to explain. Symptom-indexed table on top. Triggers on \"compileiq hang\", \"socket timeout\", \"INVALID_SCORE\", \"not converging\", \"every score is the same\", \"TypeError fromhex\", \"ncu profile\", \"register spill\", \"ptxas error\", \"not in expected format\", \"high cv\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[913,914,915,916],{"name":89,"slug":88,"type":881},{"name":564,"slug":563,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:32:05.08078",{"slug":919,"name":919,"fn":920,"description":921,"org":922,"tags":923,"stars":311,"repoUrl":309,"updatedAt":927},"compileiq-run-search","execute CompileIQ search workflows","Use when composing the Search(...) call and calling .start(). Covers the four worker classes (MultiProcessWorker \u002F IsoMultiProcessWorker \u002F RayWorker \u002F AsyncWorker) and when to pick each, SearchConfiguration sizing rules, dump_results checkpointing, tracker_config choice (Disabled \u002F Loguru \u002F MLflow), num_workers\u002Ftask_timeout semantics, and GPU clock locking for stable measurements. Triggers on \"Search()\", \"tuner.start()\", \"pool_size\", \"num_workers\", \"task_timeout\", \"IsoMultiProcessWorker\", \"RayWorker\", \"dump_results\", \"MLflow\", \"GPU clocks\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[924,925,926],{"name":54,"slug":53,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:32:08.913442",{"slug":929,"name":929,"fn":930,"description":931,"org":932,"tags":933,"stars":311,"repoUrl":309,"updatedAt":937},"compileiq-search-space","configure CompileIQ search spaces","Use when picking the search_space= argument for Search(). Covers the three provider classes (PtxasSearchSpace, NvccSearchSpace, LocalSearchSpaceBin), how to pin a version\u002Fvariant\u002Ftag, the attention-focused 'att' variant for attention kernels (FlashAttention, GQA, MHA, MLA, FlashInfer Batch Decode), air-gapped mirroring via CIQ_SEARCH_SPACES_DIR, and custom user-defined search spaces built from compileiq.search_spaces.base primitives. Triggers on \"search space\", \"PtxasSearchSpace\", \"NvccSearchSpace\", \"air-gapped compileiq\", \"offline compileiq\", \"CIQ_SEARCH_SPACES_DIR\", \"attention variant\", \"ptxas att\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[934,935,936],{"name":206,"slug":205,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},"2026-07-14T05:32:07.605639",{"slug":939,"name":939,"fn":940,"description":941,"org":942,"tags":943,"stars":311,"repoUrl":309,"updatedAt":948},"compileiq-validate-result","validate CompileIQ search results","Use AFTER a Search has completed and BEFORE claiming any speedup or shipping an ACF. Loads the dump_results CSV, extracts top-K candidates (single-objective) or the Pareto front (multi-objective), re-measures each against the no-ACF baseline with 100+ trials on fresh caches, runs Welch's t-test plus Cohen's d, rejects three classic false-positive patterns (lucky-min \u002F higher-variance \u002F multiple-comparisons-of-N), and saves the validated winner as best.acf. Triggers on \"validate result\", \"extract best config\", \"Welch's t-test\", \"is my speedup real\", \"save best ACF\", \"pareto front\", \"claim speedup\", \"ship config\".\n",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[944,945,946,947],{"name":142,"slug":141,"type":881},{"name":6,"slug":5,"type":881},{"name":36,"slug":35,"type":881},{"name":114,"slug":113,"type":881},"2026-07-14T05:32:06.343444"]