[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-nvflare-shared":3,"mdc--nk2v6p-key":41,"related-org-nvidia-nvflare-shared":214,"related-repo-nvidia-nvflare-shared":372},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":36,"sourceUrl":39,"mdContent":40},"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":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Automation","automation","tag",{"name":17,"slug":18,"type":15},"Data Pipeline","data-pipeline",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Engineering","engineering",947,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare","2026-07-30T05:26:18.65149","Apache-2.0",266,[29,30,31,32,33,34,35],"decentralized","federated-analytics","federated-computing","federated-learning","pet","privacy-protection","python",{"repoUrl":24,"stars":23,"forks":27,"topics":37,"description":38},[29,30,31,32,33,34,35],"NVIDIA Federated Learning Application Runtime Environment","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNVFlare\u002Ftree\u002FHEAD\u002Fskills\u002Fnvflare-shared","---\nname: nvflare-shared\ndescription: 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.\nlicense: Apache-2.0\nmetadata:\n  author: \"NVIDIA FLARE Team \u003Cfederatedlearning@nvidia.com>\"\n  min_flare_version: \"2.9.0\"\n  blast_radius: read_only\n  status: internal\n  version: \"0.1.0\"\n  tags: \"nvflare, federated-learning, shared-references\"\n  languages: \"python\"\n  frameworks: \"nvflare\"\n  domain: ml\n---\n\n# NVFLARE Shared Skill References\n\nInternal, non-triggered skill that holds guidance and templates shared by the\nNVFLARE conversion skills so the same rules are authored once. It is installed\nalongside every NVFLARE skill and referenced by relative path; it is not\nselected or invoked on its own.\n\n## Contents\n\n- `references\u002Fconversion-common.md` — the framework-neutral rules every\n  converter applies on its standard path: source-evidence handling, output\n  locations, dependency ordering, site partitioning, custom aggregation,\n  source-of-truth boundary, and user input\u002Fauthorization.\n- `references\u002Fconversion-workflow.md` — non-standard conversion, rerun,\n  data-location, export, and authorization guidance.\n- `references\u002Fvalidation-evidence.md` — the local validation ladder.\n- `references\u002Fdependency-install.md` — dependency ordering and host-permission\n  guidance.\n- `references\u002Fpytorch-model-exchange.md` — PyTorch-family model\u002Fstate-dict\n  exchange details.\n- `references\u002Fpytorch-family-recipe-selection.md` — PyTorch-family recipe\n  discovery, algorithm guide, and catalog-based selection rules.\n- `references\u002Fpytorch-family-recipe-construction.md` — canonical\n  PyTorch-family recipe capability, metric, launch, transport, offload, and\n  simulator-concurrency rules.\n- `references\u002Fruntime-output-guidance.md` — runtime\u002Fexport output locations.\n- `references\u002Fmetrics-and-artifact-reporting.md` — metric and artifact reporting.\n- `assets\u002Faggregator.py` — the custom weighted-aggregator template.\n\nConsuming skills load these with relative paths such as\n`..\u002Fnvflare-shared\u002Freferences\u002Fconversion-workflow.md` and adapt\n`..\u002Fnvflare-shared\u002Fassets\u002Faggregator.py` rather than duplicating the guidance.\n",{"data":42,"body":52},{"name":4,"description":6,"license":26,"metadata":43},{"author":44,"min_flare_version":45,"blast_radius":46,"status":47,"version":48,"tags":49,"languages":35,"frameworks":50,"domain":51},"NVIDIA FLARE Team \u003Cfederatedlearning@nvidia.com>","2.9.0","read_only","internal","0.1.0","nvflare, federated-learning, shared-references","nvflare","ml",{"type":53,"children":54},"root",[55,64,70,77,193],{"type":56,"tag":57,"props":58,"children":60},"element","h1",{"id":59},"nvflare-shared-skill-references",[61],{"type":62,"value":63},"text","NVFLARE Shared Skill References",{"type":56,"tag":65,"props":66,"children":67},"p",{},[68],{"type":62,"value":69},"Internal, non-triggered skill that holds guidance and templates shared by the\nNVFLARE conversion skills so the same rules are authored once. It is installed\nalongside every NVFLARE skill and referenced by relative path; it is not\nselected or invoked on its own.",{"type":56,"tag":71,"props":72,"children":74},"h2",{"id":73},"contents",[75],{"type":62,"value":76},"Contents",{"type":56,"tag":78,"props":79,"children":80},"ul",{},[81,94,105,116,127,138,149,160,171,182],{"type":56,"tag":82,"props":83,"children":84},"li",{},[85,92],{"type":56,"tag":86,"props":87,"children":89},"code",{"className":88},[],[90],{"type":62,"value":91},"references\u002Fconversion-common.md",{"type":62,"value":93}," — the framework-neutral rules every\nconverter applies on its standard path: source-evidence handling, output\nlocations, dependency ordering, site partitioning, custom aggregation,\nsource-of-truth boundary, and user input\u002Fauthorization.",{"type":56,"tag":82,"props":95,"children":96},{},[97,103],{"type":56,"tag":86,"props":98,"children":100},{"className":99},[],[101],{"type":62,"value":102},"references\u002Fconversion-workflow.md",{"type":62,"value":104}," — non-standard conversion, rerun,\ndata-location, export, and authorization guidance.",{"type":56,"tag":82,"props":106,"children":107},{},[108,114],{"type":56,"tag":86,"props":109,"children":111},{"className":110},[],[112],{"type":62,"value":113},"references\u002Fvalidation-evidence.md",{"type":62,"value":115}," — the local validation ladder.",{"type":56,"tag":82,"props":117,"children":118},{},[119,125],{"type":56,"tag":86,"props":120,"children":122},{"className":121},[],[123],{"type":62,"value":124},"references\u002Fdependency-install.md",{"type":62,"value":126}," — dependency ordering and host-permission\nguidance.",{"type":56,"tag":82,"props":128,"children":129},{},[130,136],{"type":56,"tag":86,"props":131,"children":133},{"className":132},[],[134],{"type":62,"value":135},"references\u002Fpytorch-model-exchange.md",{"type":62,"value":137}," — PyTorch-family model\u002Fstate-dict\nexchange details.",{"type":56,"tag":82,"props":139,"children":140},{},[141,147],{"type":56,"tag":86,"props":142,"children":144},{"className":143},[],[145],{"type":62,"value":146},"references\u002Fpytorch-family-recipe-selection.md",{"type":62,"value":148}," — PyTorch-family recipe\ndiscovery, algorithm guide, and catalog-based selection rules.",{"type":56,"tag":82,"props":150,"children":151},{},[152,158],{"type":56,"tag":86,"props":153,"children":155},{"className":154},[],[156],{"type":62,"value":157},"references\u002Fpytorch-family-recipe-construction.md",{"type":62,"value":159}," — canonical\nPyTorch-family recipe capability, metric, launch, transport, offload, and\nsimulator-concurrency rules.",{"type":56,"tag":82,"props":161,"children":162},{},[163,169],{"type":56,"tag":86,"props":164,"children":166},{"className":165},[],[167],{"type":62,"value":168},"references\u002Fruntime-output-guidance.md",{"type":62,"value":170}," — runtime\u002Fexport output locations.",{"type":56,"tag":82,"props":172,"children":173},{},[174,180],{"type":56,"tag":86,"props":175,"children":177},{"className":176},[],[178],{"type":62,"value":179},"references\u002Fmetrics-and-artifact-reporting.md",{"type":62,"value":181}," — metric and artifact reporting.",{"type":56,"tag":82,"props":183,"children":184},{},[185,191],{"type":56,"tag":86,"props":186,"children":188},{"className":187},[],[189],{"type":62,"value":190},"assets\u002Faggregator.py",{"type":62,"value":192}," — the custom weighted-aggregator template.",{"type":56,"tag":65,"props":194,"children":195},{},[196,198,204,206,212],{"type":62,"value":197},"Consuming skills load these with relative paths such as\n",{"type":56,"tag":86,"props":199,"children":201},{"className":200},[],[202],{"type":62,"value":203},"..\u002Fnvflare-shared\u002Freferences\u002Fconversion-workflow.md",{"type":62,"value":205}," and adapt\n",{"type":56,"tag":86,"props":207,"children":209},{"className":208},[],[210],{"type":62,"value":211},"..\u002Fnvflare-shared\u002Fassets\u002Faggregator.py",{"type":62,"value":213}," rather than duplicating the guidance.",{"items":215,"total":371},[216,234,251,262,274,288,301,315,328,339,353,362],{"slug":217,"name":217,"fn":218,"description":219,"org":220,"tags":221,"stars":231,"repoUrl":232,"updatedAt":233},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[222,225,228],{"name":223,"slug":224,"type":15},"Documentation","documentation",{"name":226,"slug":227,"type":15},"MCP","mcp",{"name":229,"slug":230,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":235,"name":235,"fn":236,"description":237,"org":238,"tags":239,"stars":248,"repoUrl":249,"updatedAt":250},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[240,243,246],{"name":241,"slug":242,"type":15},"Containers","containers",{"name":244,"slug":245,"type":15},"Deployment","deployment",{"name":247,"slug":35,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":252,"name":252,"fn":253,"description":254,"org":255,"tags":256,"stars":248,"repoUrl":249,"updatedAt":261},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[257,260],{"name":258,"slug":259,"type":15},"CI\u002FCD","ci-cd",{"name":244,"slug":245,"type":15},"2026-07-14T05:25:59.97109",{"slug":263,"name":263,"fn":264,"description":265,"org":266,"tags":267,"stars":248,"repoUrl":249,"updatedAt":273},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[268,269,270],{"name":258,"slug":259,"type":15},{"name":244,"slug":245,"type":15},{"name":271,"slug":272,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":275,"name":275,"fn":276,"description":277,"org":278,"tags":279,"stars":248,"repoUrl":249,"updatedAt":287},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[280,283,284],{"name":281,"slug":282,"type":15},"Debugging","debugging",{"name":271,"slug":272,"type":15},{"name":285,"slug":286,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":289,"name":289,"fn":290,"description":291,"org":292,"tags":293,"stars":248,"repoUrl":249,"updatedAt":300},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[294,297],{"name":295,"slug":296,"type":15},"Best Practices","best-practices",{"name":298,"slug":299,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":302,"name":302,"fn":303,"description":304,"org":305,"tags":306,"stars":248,"repoUrl":249,"updatedAt":314},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[307,310,313],{"name":308,"slug":309,"type":15},"Machine Learning","machine-learning",{"name":311,"slug":312,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":316,"name":316,"fn":317,"description":318,"org":319,"tags":320,"stars":248,"repoUrl":249,"updatedAt":327},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[321,324],{"name":322,"slug":323,"type":15},"QA","qa",{"name":325,"slug":326,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":329,"name":329,"fn":330,"description":331,"org":332,"tags":333,"stars":248,"repoUrl":249,"updatedAt":338},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[334,335],{"name":244,"slug":245,"type":15},{"name":336,"slug":337,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":340,"name":340,"fn":341,"description":342,"org":343,"tags":344,"stars":248,"repoUrl":249,"updatedAt":352},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[345,348,349],{"name":346,"slug":347,"type":15},"Code Review","code-review",{"name":271,"slug":272,"type":15},{"name":350,"slug":351,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":354,"name":354,"fn":355,"description":356,"org":357,"tags":358,"stars":248,"repoUrl":249,"updatedAt":361},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[359,360],{"name":322,"slug":323,"type":15},{"name":325,"slug":326,"type":15},"2026-07-14T05:25:54.928983",{"slug":363,"name":363,"fn":364,"description":365,"org":366,"tags":367,"stars":248,"repoUrl":249,"updatedAt":370},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[368,369],{"name":13,"slug":14,"type":15},{"name":258,"slug":259,"type":15},"2026-07-30T05:29:03.275638",496,{"items":373,"total":462},[374,387,400,412,425,438,449],{"slug":375,"name":375,"fn":376,"description":377,"org":378,"tags":379,"stars":23,"repoUrl":24,"updatedAt":386},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[380,383,384,385],{"name":381,"slug":382,"type":15},"Agents","agents",{"name":13,"slug":14,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:36:01.532575",{"slug":388,"name":388,"fn":389,"description":390,"org":391,"tags":392,"stars":23,"repoUrl":24,"updatedAt":399},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[393,394,395,396],{"name":13,"slug":14,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":397,"slug":398,"type":15},"Reporting","reporting","2026-07-14T05:36:00.281416",{"slug":401,"name":401,"fn":402,"description":403,"org":404,"tags":405,"stars":23,"repoUrl":24,"updatedAt":411},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[406,407,408],{"name":308,"slug":309,"type":15},{"name":9,"slug":8,"type":15},{"name":409,"slug":410,"type":15},"Optimization","optimization","2026-07-30T05:26:21.697612",{"slug":413,"name":413,"fn":414,"description":415,"org":416,"tags":417,"stars":23,"repoUrl":24,"updatedAt":424},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[418,419,420,421],{"name":308,"slug":309,"type":15},{"name":9,"slug":8,"type":15},{"name":397,"slug":398,"type":15},{"name":422,"slug":423,"type":15},"Simulation","simulation","2026-07-30T05:26:14.661827",{"slug":426,"name":426,"fn":427,"description":428,"org":429,"tags":430,"stars":23,"repoUrl":24,"updatedAt":437},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[431,432,433,434],{"name":17,"slug":18,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":435,"slug":436,"type":15},"PyTorch","pytorch","2026-07-30T05:26:15.761027",{"slug":439,"name":439,"fn":440,"description":441,"org":442,"tags":443,"stars":23,"repoUrl":24,"updatedAt":448},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[444,445,446,447],{"name":17,"slug":18,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":435,"slug":436,"type":15},"2026-07-30T05:26:20.645495",{"slug":450,"name":450,"fn":451,"description":452,"org":453,"tags":454,"stars":23,"repoUrl":24,"updatedAt":461},"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":8,"name":9,"logoUrl":10,"githubOrg":9},[455,456,457,458],{"name":281,"slug":282,"type":15},{"name":21,"slug":22,"type":15},{"name":9,"slug":8,"type":15},{"name":459,"slug":460,"type":15},"Observability","observability","2026-07-30T05:26:17.665774",10]