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",[],[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":350},[874,885,896,907,918,931],{"slug":875,"name":875,"fn":876,"description":877,"org":878,"tags":879,"stars":371,"repoUrl":370,"updatedAt":386},"analysis-scripts","analyze quantum experiment data","Write and run Python scripts to analyze quantum experiment data stored in HDF5 files. Use when the user asks to analyze experiment results, fit peaks or curves, extract features from measurement arrays, or when reusable analysis logic should be saved alongside an experiment for future reuse.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[880,882,883,884],{"name":142,"slug":141,"type":881},"tag",{"name":6,"slug":5,"type":881},{"name":241,"slug":240,"type":881},{"name":379,"slug":378,"type":881},{"slug":886,"name":886,"fn":887,"description":888,"org":889,"tags":890,"stars":371,"repoUrl":370,"updatedAt":895},"experiment-execution","run quantum calibration experiments","Reference guide for running individual quantum calibration experiments and interpreting their results. Use when the user asks to run a single experiment (e.g. resonator spectroscopy, qubit spectroscopy, T1, T2), inspect experiment plots with the VLM, or query experiment history and schemas via the lab tool.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[891,892,893,894],{"name":343,"slug":342,"type":881},{"name":6,"slug":5,"type":881},{"name":379,"slug":378,"type":881},{"name":39,"slug":38,"type":881},"2026-07-14T05:32:41.013079",{"slug":897,"name":897,"fn":898,"description":899,"org":900,"tags":901,"stars":371,"repoUrl":370,"updatedAt":906},"vlm-configuration","configure Vision Language Models","Configure the Vision Language Model (VLM) used for analyzing experiment plots. Use when the user asks to change the VLM provider or model, adjust temperature or max_tokens, enable or disable thinking mode, or troubleshoot VLM-related behavior in config.yaml.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[902,903,905],{"name":135,"slug":134,"type":881},{"name":904,"slug":418,"type":881},"LLM",{"name":6,"slug":5,"type":881},"2026-07-14T05:32:43.482864",{"slug":908,"name":908,"fn":909,"description":910,"org":911,"tags":912,"stars":371,"repoUrl":370,"updatedAt":917},"workflow-execution","execute quantum calibration workflows","Execute a previously planned calibration workflow node by node, tracking progress and handling failures. Use when the user asks to run, resume, or continue an existing workflow, or to execute a sequence of calibration steps that has already been defined in data\u002Fworkflows\u002F.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[913,914,915,916],{"name":54,"slug":53,"type":881},{"name":6,"slug":5,"type":881},{"name":379,"slug":378,"type":881},{"name":383,"slug":382,"type":881},"2026-07-14T05:32:44.734587",{"slug":919,"name":919,"fn":920,"description":921,"org":922,"tags":923,"stars":371,"repoUrl":370,"updatedAt":930},"workflow-planning","plan quantum calibration workflows","Plan a new calibration workflow by discussing experiment sequences, success\u002Ffailure criteria, and extracted parameters with the user before any files are created. Use when the user asks to plan, design, or create a new calibration sequence, or to build a multi-step workflow for a qubit or device. Requires explicit user confirmation before writing workflow files.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[924,925,928,929],{"name":6,"slug":5,"type":881},{"name":926,"slug":927,"type":881},"Planning","planning",{"name":379,"slug":378,"type":881},{"name":383,"slug":382,"type":881},"2026-07-14T05:32:39.734157",{"slug":932,"name":932,"fn":933,"description":934,"org":935,"tags":936,"stars":371,"repoUrl":370,"updatedAt":943},"writing-experiment-scripts","author quantum experiment scripts","Author new experiment scripts that are compatible with the lab system's auto-discovery. Use when the user asks to create a new experiment type, add a custom measurement, or write a Python script that should be discoverable via the `lab` and `run_experiment` tools. Covers required function signatures, type hints, docstrings, and return formats.",{"slug":5,"name":6,"logoUrl":7,"githubOrg":6},[937,940,941,942],{"name":938,"slug":939,"type":881},"Laboratory","laboratory",{"name":6,"slug":5,"type":881},{"name":241,"slug":240,"type":881},{"name":379,"slug":378,"type":881},"2026-07-14T05:32:42.253966"]