[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-physical-ai-people-attribute-search":3,"mdc-jw18i7-key":34,"related-repo-nvidia-physical-ai-people-attribute-search":2572,"related-org-nvidia-physical-ai-people-attribute-search":2674},{"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":29,"sourceUrl":32,"mdContent":33},"physical-ai-people-attribute-search","run people attribute search workflows on OSMO","Use when running people attribute search (PAS) image augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: people attribute search, PAS, person augmentation, attribute search, person re-identification, clothing augmentation, person crop augmentation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17,20],{"name":13,"slug":14,"type":15},"Automation","automation","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Imaging","imaging",{"name":21,"slug":22,"type":15},"Computer Vision","computer-vision",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:27:40.377801","CC-BY-4.0 AND Apache-2.0",281,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fphysical-ai-people-attribute-search","---\nname: physical-ai-people-attribute-search\ndescription: >-\n  Use when running people attribute search (PAS) image augmentation and\n  auto-labeling workflows on OSMO: flow selection, preflight, submit-time\n  interpolation, monitoring, and output retrieval. Trigger keywords: people\n  attribute search, PAS, person augmentation, attribute search, person\n  re-identification, clothing augmentation, person crop augmentation.\nlicense: CC-BY-4.0 AND Apache-2.0\nmetadata:\n  owner: NVIDIA\n  service: physical-ai-data-factory\n  version: 1.0.0\n  reviewed: '2026-06-05'\n  author: NVIDIA\n  tags:\n    - physical-ai\n    - people-attribute-search\n    - person-augmentation\n    - auto-labeling\n    - image-edit\n---\n\n# Physical AI People Attribute Search Workflow Orchestrator\n\nDefault workflow skill for PAS execution on OSMO. It owns flow selection,\npreflight, submit-time interpolation, monitoring, and output retrieval.\n\n## Purpose\n\nRun the PAS image augmentation and auto-labeling pipeline safely and\nreproducibly from preflight to output download.\n\nThe PAS pipeline augments existing person-crop datasets by generating\ncontrolled clothing\u002Fappearance variations (image-domain) and synonymous\nattribute captions (text-domain). It uses the `paidf-augmentation` container\nfor image-edit augmentation with MCQ verification, and the\n`paidf-auto-labeling` container for person-attribute captioning.\n\nDo NOT use this skill for container-internal tuning-only questions.\n\n## Prerequisites\n\nConfirm these before running preflight or any submit. Missing required secrets\nsurface as `USER_INPUT_REQUIRED:` from `scripts\u002Fpreflight_credentials.sh`.\n\n| Requirement | How it is satisfied | Used for |\n|---|---|---|\n| NGC API key (optional) | `NGC_API_KEY`, `NGC_CLI_API_KEY`, or compatible `nvapi-*` token | Optional for `nvcr_io` credential refresh; default PAS image refs are public |\n| Hugging Face token | `HF_TOKEN` (or `HUGGING_FACE_HUB_TOKEN`), or a cached token at `~\u002F.cache\u002Fhuggingface\u002Ftoken` | Creates the OSMO `hf_token` credential |\n| OSMO CLI access | `osmo` on `PATH`, logged in, with a default profile and a registered DATA credential profile matching `storage_url` | Submitting\u002Fmonitoring workflows and listing\u002Fdownloading objects |\n| GPU pool | At least one `ONLINE` pool in `osmo pool list --mode free` | Scheduling setup + worker tasks |\n| Image Edit endpoint | In-cluster NIM `qwen-image-edit-2511` (reused if healthy, else deployed via the NIM operator); external opt-in via `image_edit_url` | Image-domain augmentation |\n| VLM endpoint | In-cluster NIM `qwen3-vl` (shared with VDA); external opt-in via `vlm_url` | MCQ verification and person-attribute captioning |\n| LLM endpoint | In-cluster NIM `qwen25-14b` (shared with VDA); external opt-in via `llm_url` | MCQ question generation |\n\n## Instructions\n\n1. Select the workflow (`e2e`, `augmentation`, `auto_labeling`) from user intent.\n2. Provide a tentative execution-time overview before starting run actions.\n3. Run preflight and readiness checks before submit.\n4. Derive submit-time values from the active dataset backend (never guess\n   `storage_url`).\n5. Submit the workflow with explicit interpolation values and monitor to completion.\n6. Retrieve outputs and summarize task outcomes.\n\nUse `run_script(...)` for script execution. Canonical examples:\n\n```python\nrun_script(\"bash scripts\u002Fpreflight_credentials.sh --workflow assets\u002Fconfigs\u002Fosmo\u002Fe2e.yaml\")\n```\n\n## Available Scripts\n\nUse script-level `--help` for exact arguments.\n\n| Script | Role |\n|---|---|\n| `scripts\u002Fpreflight_credentials.sh` | Secrets\u002Fcontrol-plane preflight and workflow image access checks |\n| `scripts\u002Faugmentation_worker.sh` | Image-edit augmentation worker (preprocess, config gen, augment, post-process) |\n| `scripts\u002Fauto_labeling_worker.sh` | Person-attribute captioning worker |\n| `scripts\u002Fendpoint_common.sh` | Shared endpoint health\u002Fauth helpers |\n\n## Supported Flows\n\n| Flow | OSMO YAML | Group sequence | Typical use |\n|---|---|---|---|\n| `e2e` | `assets\u002Fconfigs\u002Fosmo\u002Fe2e.yaml` | setup -> augmentation -> auto_labeling | Full pipeline: augment person crops then generate captions |\n| `augmentation` | `assets\u002Fconfigs\u002Fosmo\u002Faugmentation.yaml` | setup -> augmentation | Image-edit augmentation only, no captioning |\n| `auto_labeling` | `assets\u002Fconfigs\u002Fosmo\u002Fauto_labeling.yaml` | setup -> auto_labeling | Captioning only on pre-augmented person crops |\n\n### Pick the right workflow for the user's request\n\n| User intent | Workflow |\n|---|---|\n| \"Augment person crops and generate captions\" \u002F \"full PAS pipeline\" | `e2e` |\n| \"Generate clothing variations\" \u002F \"augment only\" \u002F \"image edit\" | `augmentation` |\n| \"Caption augmented images\" \u002F \"generate search queries\" \u002F \"label only\" | `auto_labeling` |\n\n## Disambiguation: handle vague requests before committing\n\nDefault to autonomy: ask only when missing information blocks execution.\n\n### Autonomous defaults (do NOT ask)\n\n- If flow is not explicitly requested, default to `e2e`.\n- If cookbook is not specified, default to `default`.\n- If `n_augmentations` is not specified, default to `3`.\n- After any stage completes successfully, continue to the next stage immediately.\n\n### Triggers that should pause for disambiguation\n\n| Missing input | Why it matters | Ask |\n|---|---|---|\n| `USER_INPUT_REQUIRED` from preflight | Required secret is missing | Ask one concise unblock question |\n| Storage backend prefix cannot be derived | Wrong scheme causes runtime storage auth mismatch | \"What is the backend-native root prefix for this run?\" |\n| No ONLINE GPU pool\u002Fplatform | Workflow cannot schedule | \"Which GPU pool\u002Fplatform should this run target?\" |\n| NIM deploy fails and no external URLs given | Workers cannot connect to models | \"Provide Image Edit \u002F VLM \u002F LLM endpoint URLs, or grant GPU capacity for the NIM operator deploy.\" |\n\n## Step 0: Select Flow and Gather Inputs\n\n### Input data policy\n\n- PAS requires person-crop images organized as `\u003Cperson_id>\u002F\u003Cview>.jpg` subdirectories.\n- Always preserve user-provided dataset inputs as first-class.\n- Never replace an explicit user dataset with demo assets.\n- If no dataset is provided, ask for one (PAS has no built-in demo dataset).\n\nCollect only missing values:\n\n1. Dataset source (`storage_url` + `dataset` name).\n2. Flow (`e2e`, `augmentation`, `auto_labeling`); default to `e2e`.\n3. OSMO `gpu_platform` (auto-select when unambiguous).\n4. Endpoint URLs for Image Edit, VLM, and LLM — optional; default to in-cluster\n   NIMs and only set for external endpoints.\n5. Number of augmentations per person ID (default: 3).\n\nGenerate run stamp before each submit:\n\n```bash\nSTAMP=$(cat \u002Fproc\u002Fsys\u002Fkernel\u002Frandom\u002Fuuid | cut -c1-8)\nRUN_ID=\"run-$STAMP\"\n```\n\n## Execution Time Overview (required before run)\n\nBefore running any mutating command, provide a short ETA overview.\n\nBaseline ranges:\n\n| Phase | Typical duration |\n|---|---|\n| Credentials + preflight | ~1-2 min |\n| Workflow submit + queue\u002Fstart | ~1-3 min |\n\nWorkflow runtime (depends on dataset size and endpoint latency):\n\n| Flow | Per-image time | Typical dataset (100 images, 3 augs) |\n|---|---|---|\n| `augmentation` | ~2.5-3 min\u002Fimage | ~4-5 hours |\n| `auto_labeling` | ~1-2 min\u002Fimage | ~2-3 hours |\n| `e2e` | ~3.5-5 min\u002Fimage | ~6-8 hours |\n\n## Common Preconditions (all flows)\n\n1. **Credential and control-plane preflight**\n\n   ```bash\n   bash scripts\u002Fpreflight_credentials.sh --workflow assets\u002Fconfigs\u002Fosmo\u002F\u003Cflow>.yaml\n   ```\n\n   If output contains `USER_INPUT_REQUIRED:`, ask one concise unblock question.\n\n2. **Storage interpolation policy**\n\n   `storage_url` must be derived from the actual dataset\u002Fupload backend.\n   Never silently default to stale values on mismatched backends.\n\n3. **Inference policy (non-negotiable)**\n\n   - Reuse healthy in-cluster persistent NIM endpoints by default\n     (`qwen-image-edit-2511`, `qwen3-vl`, `qwen25-14b`).\n   - If missing\u002Funhealthy, deploy automatically — this is a prerequisite, not a\n     user decision. Do NOT pause to ask. See `references\u002Fnim\u002FREADME.md` for the\n     image-edit NIMService manifest and the VLM\u002FLLM NIM operator install.\n   - PAS does NOT launch inference servers inside the OSMO workflow; workers\n     consume the `image_edit_url` \u002F `vlm_url` \u002F `llm_url` endpoints.\n   - External endpoints are opt-in only (explicit request or explicit URLs);\n     only then override the `*_url` values at submit.\n   - Never scale down\u002Fdelete existing NIMs to free GPUs.\n\n## Submit (all flows)\n\nEvery flow uses the same submit shape; only the workflow YAML changes.\n\n```bash\nSKILLS_DIR=\"$(cd \"$(git rev-parse --show-toplevel)\u002Fskills\u002Fphysical-ai-people-attribute-search\" && pwd)\"\nSTAMP=$(cat \u002Fproc\u002Fsys\u002Fkernel\u002Frandom\u002Fuuid | cut -c1-8)\nosmo workflow submit assets\u002Fconfigs\u002Fosmo\u002F\u003Cflow>.yaml \\\n  --pool \u003Cpool> \\\n  --set-string \\\n    dataset=\u003Cdataset> \\\n    run_id=run-$STAMP \\\n    storage_url=\u003Cbackend-prefix> \\\n    gpu_platform=\u003Cgpu-platform> \\\n    skills_dir=\"$SKILLS_DIR\"\n```\n\nEndpoints default to the in-cluster NIMs (`image_edit_url` \u002F `vlm_url` \u002F\n`llm_url`); deploy\u002Freuse them per the Inference policy above. Do not pass these\nunless using external endpoints.\n\nCompatibility note:\n- Use exactly one `--set-string` flag and pass all key\u002Fvalue pairs after it.\n- Do not repeat `--set`\u002F`--set-string` flags in the same command.\n\nCommon optional overrides (append to the same `--set-string` list):\n\n```bash\ncookbook=\u003Ccookbook_name> \\\nn_augmentations=\u003Ccount> \\\nimage_edit_url=\u003Cimage-edit-endpoint> \\\nvlm_url=\u003Cvlm-endpoint> \\\nllm_url=\u003Cllm-endpoint>\n```\n\n## OSMO Monitoring\n\n```bash\n# Workflow status + task states\nosmo workflow query \u003Cworkflow_id> --format-type json \\\n  | jq '{status, tasks: [.groups[].tasks[] | {name, status, exit_code}]}'\n\n# Logs for a specific task\nosmo workflow logs \u003Cworkflow_id> --task \u003Ctask_name> -n 200\n\n# Output retrieval\nosmo data list --no-pager \u003Coutput_url>\nosmo data download \u003Coutput_url> \u003Clocal_dir>\u002F\n```\n\nFor runs expected to exceed two minutes, send heartbeat updates at least every\ntwo minutes.\n\n## Post-Run Output\n\nAfter successful completion, the output directory contains:\n\nFor `augmentation` \u002F `e2e`:\n- `\u003Cperson_id>\u002Faug_\u003Cn>\u002Foutput.jpg` — augmented multi-pane image\n- `\u003Cperson_id>\u002Faug_\u003Cn>\u002Foutput.txt` — natural-language caption\n- `\u003Cperson_id>\u002Faug_\u003Cn>\u002Foutput_metadata.json` — verification results\n- `dataset\u002Faugmented_data.json` — structured dataset with attributes and queries\n- `dataset\u002Faugmented_imgs\u002F` — split per-view crops\n\nFor `auto_labeling`:\n- `caption_\u003Cid>\u002Ftask\u002Fopen_qa.json` — person-attribute captions grouped by question bank\n\n## Supporting files\n\nUse these canonical locations:\n\n- Workflows: `assets\u002Fconfigs\u002Fosmo\u002F*.yaml`\n- Runtime scripts: `scripts\u002F*.sh`\n- Flow walkthroughs: `references\u002Fflows\u002F*.md`\n- Setup and triage: `references\u002Fsetup.md`, `references\u002Ftroubleshooting.md`\n- Images: `references\u002Fcontainer-images.md`\n- Cookbook tuning: `assets\u002Fcookbooks\u002Fdefault\u002FREADME.md`\n",{"data":35,"body":46},{"name":4,"description":6,"license":26,"metadata":36},{"owner":9,"service":37,"version":38,"reviewed":39,"author":9,"tags":40},"physical-ai-data-factory","1.0.0","2026-06-05",[41,42,43,44,45],"physical-ai","people-attribute-search","person-augmentation","auto-labeling","image-edit",{"type":47,"children":48},"root",[49,58,64,71,76,98,103,109,130,420,426,490,503,524,530,543,632,638,760,767,837,843,848,854,906,912,1017,1023,1029,1060,1065,1141,1146,1236,1242,1247,1252,1300,1305,1394,1400,1587,1593,1598,1929,1954,1959,1995,2007,2072,2078,2363,2368,2374,2379,2397,2455,2465,2479,2485,2490,2566],{"type":50,"tag":51,"props":52,"children":54},"element","h1",{"id":53},"physical-ai-people-attribute-search-workflow-orchestrator",[55],{"type":56,"value":57},"text","Physical AI People Attribute Search Workflow Orchestrator",{"type":50,"tag":59,"props":60,"children":61},"p",{},[62],{"type":56,"value":63},"Default workflow skill for PAS execution on OSMO. 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Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":2588,"slug":2589,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":2592,"name":2592,"fn":2593,"description":2594,"org":2595,"tags":2596,"stars":23,"repoUrl":24,"updatedAt":2604},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2597,2600,2603],{"name":2598,"slug":2599,"type":15},"Deployment","deployment",{"name":2601,"slug":2602,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":2606,"name":2606,"fn":2607,"description":2608,"org":2609,"tags":2610,"stars":23,"repoUrl":24,"updatedAt":2618},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2611,2614,2615],{"name":2612,"slug":2613,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":2616,"slug":2617,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":2620,"name":2620,"fn":2621,"description":2622,"org":2623,"tags":2624,"stars":23,"repoUrl":24,"updatedAt":2630},"amc-run-sample-calibration","run AMC sample dataset calibration","Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2625,2626,2627],{"name":2581,"slug":2582,"type":15},{"name":9,"slug":8,"type":15},{"name":2628,"slug":2629,"type":15},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":2632,"name":2632,"fn":2633,"description":2634,"org":2635,"tags":2636,"stars":23,"repoUrl":24,"updatedAt":2643},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2637,2638,2639,2640],{"name":13,"slug":14,"type":15},{"name":18,"slug":19,"type":15},{"name":9,"slug":8,"type":15},{"name":2641,"slug":2642,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":2645,"name":2645,"fn":2646,"description":2647,"org":2648,"tags":2649,"stars":23,"repoUrl":24,"updatedAt":2658},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2650,2651,2654,2655],{"name":2598,"slug":2599,"type":15},{"name":2652,"slug":2653,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":2656,"slug":2657,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":2660,"name":2660,"fn":2661,"description":2662,"org":2663,"tags":2664,"stars":23,"repoUrl":24,"updatedAt":2672},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2665,2666,2669],{"name":9,"slug":8,"type":15},{"name":2667,"slug":2668,"type":15},"Quantum Computing","quantum-computing",{"name":2670,"slug":2671,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305,{"items":2675,"total":2825},[2676,2694,2709,2720,2732,2746,2759,2773,2784,2793,2807,2816],{"slug":2677,"name":2677,"fn":2678,"description":2679,"org":2680,"tags":2681,"stars":2691,"repoUrl":2692,"updatedAt":2693},"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},[2682,2685,2688],{"name":2683,"slug":2684,"type":15},"Documentation","documentation",{"name":2686,"slug":2687,"type":15},"MCP","mcp",{"name":2689,"slug":2690,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2695,"name":2695,"fn":2696,"description":2697,"org":2698,"tags":2699,"stars":2706,"repoUrl":2707,"updatedAt":2708},"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},[2700,2703,2704],{"name":2701,"slug":2702,"type":15},"Containers","containers",{"name":2598,"slug":2599,"type":15},{"name":2705,"slug":508,"type":15},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2710,"name":2710,"fn":2711,"description":2712,"org":2713,"tags":2714,"stars":2706,"repoUrl":2707,"updatedAt":2719},"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},[2715,2718],{"name":2716,"slug":2717,"type":15},"CI\u002FCD","ci-cd",{"name":2598,"slug":2599,"type":15},"2026-07-14T05:25:59.97109",{"slug":2721,"name":2721,"fn":2722,"description":2723,"org":2724,"tags":2725,"stars":2706,"repoUrl":2707,"updatedAt":2731},"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},[2726,2727,2728],{"name":2716,"slug":2717,"type":15},{"name":2598,"slug":2599,"type":15},{"name":2729,"slug":2730,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2733,"name":2733,"fn":2734,"description":2735,"org":2736,"tags":2737,"stars":2706,"repoUrl":2707,"updatedAt":2745},"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},[2738,2741,2742],{"name":2739,"slug":2740,"type":15},"Debugging","debugging",{"name":2729,"slug":2730,"type":15},{"name":2743,"slug":2744,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2747,"name":2747,"fn":2748,"description":2749,"org":2750,"tags":2751,"stars":2706,"repoUrl":2707,"updatedAt":2758},"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},[2752,2755],{"name":2753,"slug":2754,"type":15},"Best Practices","best-practices",{"name":2756,"slug":2757,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2760,"name":2760,"fn":2761,"description":2762,"org":2763,"tags":2764,"stars":2706,"repoUrl":2707,"updatedAt":2772},"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},[2765,2768,2771],{"name":2766,"slug":2767,"type":15},"Machine Learning","machine-learning",{"name":2769,"slug":2770,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2774,"name":2774,"fn":2775,"description":2776,"org":2777,"tags":2778,"stars":2706,"repoUrl":2707,"updatedAt":2783},"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},[2779,2782],{"name":2780,"slug":2781,"type":15},"QA","qa",{"name":2628,"slug":2629,"type":15},"2026-07-14T05:25:53.673039",{"slug":2785,"name":2785,"fn":2786,"description":2787,"org":2788,"tags":2789,"stars":2706,"repoUrl":2707,"updatedAt":2792},"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},[2790,2791],{"name":2598,"slug":2599,"type":15},{"name":2601,"slug":2602,"type":15},"2026-07-14T05:25:49.362534",{"slug":2794,"name":2794,"fn":2795,"description":2796,"org":2797,"tags":2798,"stars":2706,"repoUrl":2707,"updatedAt":2806},"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},[2799,2802,2803],{"name":2800,"slug":2801,"type":15},"Code Review","code-review",{"name":2729,"slug":2730,"type":15},{"name":2804,"slug":2805,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2808,"name":2808,"fn":2809,"description":2810,"org":2811,"tags":2812,"stars":2706,"repoUrl":2707,"updatedAt":2815},"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},[2813,2814],{"name":2780,"slug":2781,"type":15},{"name":2628,"slug":2629,"type":15},"2026-07-14T05:25:54.928983",{"slug":2817,"name":2817,"fn":2818,"description":2819,"org":2820,"tags":2821,"stars":2706,"repoUrl":2707,"updatedAt":2824},"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},[2822,2823],{"name":13,"slug":14,"type":15},{"name":2716,"slug":2717,"type":15},"2026-07-30T05:29:03.275638",496]