[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-i4h-workflow-dataset-mimic":3,"mdc-nfj2uk-key":37,"related-repo-nvidia-i4h-workflow-dataset-mimic":1936,"related-org-nvidia-i4h-workflow-dataset-mimic":2038},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"i4h-workflow-dataset-mimic","augment HDF5 datasets with cloned trajectories","Expand an HDF5 recording by cloning trajectories with action\u002Fstate noise. 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Use when asked to mimic, expand, or augment a dataset; not for recording new demos (use [[i4h-workflow-dataset-teleop]]).\nlicense: Apache-2.0\nmetadata:\n  author: \"Isaac for Healthcare Team \u003Cisaac-for-healthcare-support@nvidia.com>\"\n  tags:\n    - isaac-for-healthcare\n    - i4h\n    - dataset\n    - mimic\n    - augmentation\n---\n\n# i4h Workflow — Mimic Dataset\n\n## Purpose\n\nExpand an HDF5 recording by replicating trajectories with small action and state noise. Use when the user asks to mimic, expand, or augment a dataset without recording new episodes. If the same prompt also asks to visualize the dataset, finish mimic first, then compose [[i4h-workflow-dataset-convert]] and [[i4h-lerobot-viz]] on the mimic output.\n\n## Base Code\n\nThese steps drive the i4h-workflows base code (the `workflows\u002Fagentic\u002F` tree). To reuse an existing checkout, set `I4H_WORKFLOWS` to its path (no clone happens). Otherwise this resolves the current repo, or clones to `~\u002Fi4h-workflows` — pick that default without prompting. Run every command below from the resolved root:\n\n```bash\n# Resolve the i4h-workflows base code (provides workflows\u002Fagentic\u002F).\nROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"\nif [ ! -d \"$ROOT\u002Fworkflows\u002Fagentic\" ]; then\n  ROOT=\"${I4H_WORKFLOWS:-$HOME\u002Fi4h-workflows}\"\n  [ -d \"$ROOT\u002Fworkflows\u002Fagentic\" ] || git clone https:\u002F\u002Fgithub.com\u002Fisaac-for-healthcare\u002Fi4h-workflows \"$ROOT\"\nfi\nexport I4H_WORKFLOWS=\"$ROOT\"; cd \"$ROOT\"\n```\n\n## Basics\n\n- **Env config (source of truth):** `workflows\u002Fagentic\u002Fconfig\u002Fenvironments\u002F\u003Cenv>.yaml` defines the `\u003Cenv>` robot and task the mimicked trajectories replay against.\n- Mimic perturbs action\u002Fstate, not visuals.\n- Default `--include-source` keeps the original demos in the output.\n- In a chained workflow after teleop\u002Fvalidate, use the HDF5 produced by that chain. Do not silently fall back to an older same-env recording if the latest\u002Fcurrent recording is empty or failed; stop and report that source demos are missing.\n- Count HDF5 episodes with the workflow venv, not system Python (`h5py` may not be installed globally).\n\n## Run\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — setup and resolve input HDF5\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nENV_ID=scissor_pick_and_place\nRUNS_ROOT=\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\"\n\n# Point IN at a real recording to expand (absolute path). Recordings come from teleop or\n# validate (which writes data\u002Fverify.hdf5 under each runs\u002Feval_* dir). List candidates newest-first:\n#   find \"${RUNS_ROOT}\" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM  %p\\n' | sort -r | head\nIN=\"${IN:-}\"\nif [ ! -f \"${IN}\" ]; then\n  echo \"mimic: set IN to an existing .hdf5 (got '${IN:-\u003Cunset>}'). Candidates:\" >&2\n  find \"${RUNS_ROOT}\" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM  %p\\n' 2>\u002Fdev\u002Fnull | sort -r | head\n  exit 1\nfi\n\n# Verify the selected input is a real recording before mimic. A tiny HDF5 or\n# `0\u002FN episodes succeeded` teleop artifact is not usable source data.\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fmimic\u002F.venv\u002Fbin\u002Fpython\" - \"${IN}\" \u003C\u003C'PY'\nimport h5py\nimport sys\npath = sys.argv[1]\nwith h5py.File(path, \"r\") as f:\n    count = len(f[\"data\"]) if \"data\" in f else 0\nprint(f\"source episodes: {count}\")\nif count \u003C= 0:\n    raise SystemExit(\"mimic: input HDF5 has no episodes; record successful demos first\")\nPY\n\nRUN_DIR=\"${RUNS_ROOT}\u002Fmimic_${ENV_ID}_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Fdata\" \"${RUN_DIR}\u002Flogs\"\nln -sfn \"${RUN_DIR}\" \"${RUNS_ROOT}\u002F.latest\"\nOUT=\"${RUN_DIR}\u002Fdata\u002Fdemo_mimic.hdf5\"\n```\n\nWhen resolving \"my dataset\" from prior prompts, inspect candidates newest-first and prefer the current chain's latest successful teleop\u002Fvalidate HDF5. If the newest HDF5 for the target env has 0 episodes, do not skip back to an older run unless the user explicitly asks to reuse that older dataset.\n\n### Step 2 — mimic expand\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fmimic\u002Frun.sh\" --env \"${ENV_ID}\" \\\n  --input \"${IN}\" \\\n  --output \"${OUT}\" \\\n  --episodes 3 \\\n  --noise-std 0.01 \\\n  --include-source \\\n  --overwrite \\\n  2>&1 | tee \"${RUN_DIR}\u002Flogs\u002Fmimic.log\"\n```\n\n## Verify\n\n```bash\nuv --directory \"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fmimic\" run python -c \\\n  \"import h5py; print('episodes:', len(h5py.File('${OUT}','r')['data']))\"\n```\n\nConfirm the output episode count equals the source demos plus `--episodes`.\nAlso confirm the input episode count was greater than zero; mimic output from an empty source is invalid.\n\nIf the prompt includes visualization (for example, \"Mimic 3 more episodes and visualize my dataset\"), continue after this verify step:\n\n1. Load [[i4h-workflow-dataset-convert]] and set `HDF5_PATH=\"${OUT}\"` so the augmented HDF5 is converted to LeRobot with `--video-codec h264`.\n2. Load [[i4h-lerobot-viz]] and set `DATASET_DIR` to the converted dataset directory (`${HF_LEROBOT_HOME}\u002Flocal\u002F${ENV_ID}` by default).\n3. Report both the augmented HDF5 and the visualizer URL.\n\n## Prerequisites\n\n- Workflow set up via [[i4h-workflow-setup]] (the `.venv` must exist).\n- An existing input HDF5 recording (`--input`).\n- The env id that produced the recording.\n\n## Limitations\n\n- Perturbs action\u002Fstate only, not visuals.\n- Augments an existing recording rather than recording new episodes.\n- Output state\u002Faction dimensions must match the source.\n\n## Troubleshooting\n\n- **Error:** `.venv` not found \u002F mimic fails to launch - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.\n- **Error:** input recording not found - Cause: wrong or missing `--input` HDF5 path. Fix: point to the existing recording file.\n- **Error:** input HDF5 has no episodes - Cause: teleop was launched but no successful episodes were saved. Fix: record successful source demos first; do not substitute an older run without explicit user direction.\n- **Error:** output already exists \u002F write refused - Cause: `--output` path is occupied. Fix: choose a new path or pass `--overwrite`.\n- **Error:** state\u002Faction dimension mismatch on inspect - Cause: `--env` differs from the env that produced the input. 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Fix: use the same env id as the source recording.",{"type":52,"tag":61,"props":1920,"children":1922},{"id":1921},"final-response",[1923],{"type":58,"value":1924},"Final Response",{"type":52,"tag":68,"props":1926,"children":1927},{},[1928],{"type":58,"value":1929},"Report input path, output path, generated episode count, noise std, whether source demos were included, and the visualizer URL if visualization was requested.",{"type":52,"tag":1931,"props":1932,"children":1933},"style",{},[1934],{"type":58,"value":1935},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1937,"total":2037},[1938,1953,1967,1981,1993,2010,2025],{"slug":1939,"name":1939,"fn":1940,"description":1941,"org":1942,"tags":1943,"stars":26,"repoUrl":27,"updatedAt":1952},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1944,1947,1948,1949],{"name":1945,"slug":1946,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},{"name":9,"slug":8,"type":15},{"name":1950,"slug":1951,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1954,"name":1954,"fn":1955,"description":1956,"org":1957,"tags":1958,"stars":26,"repoUrl":27,"updatedAt":1966},"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},[1959,1962,1965],{"name":1960,"slug":1961,"type":15},"Deployment","deployment",{"name":1963,"slug":1964,"type":15},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1968,"name":1968,"fn":1969,"description":1970,"org":1971,"tags":1972,"stars":26,"repoUrl":27,"updatedAt":1980},"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},[1973,1976,1977],{"name":1974,"slug":1975,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1978,"slug":1979,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1982,"name":1982,"fn":1983,"description":1984,"org":1985,"tags":1986,"stars":26,"repoUrl":27,"updatedAt":1992},"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},[1987,1988,1989],{"name":1945,"slug":1946,"type":15},{"name":9,"slug":8,"type":15},{"name":1990,"slug":1991,"type":15},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":1994,"name":1994,"fn":1995,"description":1996,"org":1997,"tags":1998,"stars":26,"repoUrl":27,"updatedAt":2009},"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},[1999,2002,2005,2006],{"name":2000,"slug":2001,"type":15},"Automation","automation",{"name":2003,"slug":2004,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":2007,"slug":2008,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":2011,"name":2011,"fn":2012,"description":2013,"org":2014,"tags":2015,"stars":26,"repoUrl":27,"updatedAt":2024},"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},[2016,2017,2020,2021],{"name":1960,"slug":1961,"type":15},{"name":2018,"slug":2019,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":2022,"slug":2023,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":2026,"name":2026,"fn":2027,"description":2028,"org":2029,"tags":2030,"stars":26,"repoUrl":27,"updatedAt":2036},"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},[2031,2032,2035],{"name":9,"slug":8,"type":15},{"name":2033,"slug":2034,"type":15},"Quantum Computing","quantum-computing",{"name":24,"slug":25,"type":15},"2026-07-14T05:26:58.898253",305,{"items":2039,"total":2190},[2040,2058,2074,2085,2097,2111,2124,2138,2149,2158,2172,2181],{"slug":2041,"name":2041,"fn":2042,"description":2043,"org":2044,"tags":2045,"stars":2055,"repoUrl":2056,"updatedAt":2057},"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},[2046,2049,2052],{"name":2047,"slug":2048,"type":15},"Documentation","documentation",{"name":2050,"slug":2051,"type":15},"MCP","mcp",{"name":2053,"slug":2054,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2059,"name":2059,"fn":2060,"description":2061,"org":2062,"tags":2063,"stars":2071,"repoUrl":2072,"updatedAt":2073},"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},[2064,2067,2068],{"name":2065,"slug":2066,"type":15},"Containers","containers",{"name":1960,"slug":1961,"type":15},{"name":2069,"slug":2070,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2075,"name":2075,"fn":2076,"description":2077,"org":2078,"tags":2079,"stars":2071,"repoUrl":2072,"updatedAt":2084},"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},[2080,2083],{"name":2081,"slug":2082,"type":15},"CI\u002FCD","ci-cd",{"name":1960,"slug":1961,"type":15},"2026-07-14T05:25:59.97109",{"slug":2086,"name":2086,"fn":2087,"description":2088,"org":2089,"tags":2090,"stars":2071,"repoUrl":2072,"updatedAt":2096},"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},[2091,2092,2093],{"name":2081,"slug":2082,"type":15},{"name":1960,"slug":1961,"type":15},{"name":2094,"slug":2095,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2098,"name":2098,"fn":2099,"description":2100,"org":2101,"tags":2102,"stars":2071,"repoUrl":2072,"updatedAt":2110},"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},[2103,2106,2107],{"name":2104,"slug":2105,"type":15},"Debugging","debugging",{"name":2094,"slug":2095,"type":15},{"name":2108,"slug":2109,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2112,"name":2112,"fn":2113,"description":2114,"org":2115,"tags":2116,"stars":2071,"repoUrl":2072,"updatedAt":2123},"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},[2117,2120],{"name":2118,"slug":2119,"type":15},"Best Practices","best-practices",{"name":2121,"slug":2122,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":2071,"repoUrl":2072,"updatedAt":2137},"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},[2130,2133,2136],{"name":2131,"slug":2132,"type":15},"Machine Learning","machine-learning",{"name":2134,"slug":2135,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2139,"name":2139,"fn":2140,"description":2141,"org":2142,"tags":2143,"stars":2071,"repoUrl":2072,"updatedAt":2148},"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},[2144,2147],{"name":2145,"slug":2146,"type":15},"QA","qa",{"name":1990,"slug":1991,"type":15},"2026-07-14T05:25:53.673039",{"slug":2150,"name":2150,"fn":2151,"description":2152,"org":2153,"tags":2154,"stars":2071,"repoUrl":2072,"updatedAt":2157},"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},[2155,2156],{"name":1960,"slug":1961,"type":15},{"name":1963,"slug":1964,"type":15},"2026-07-14T05:25:49.362534",{"slug":2159,"name":2159,"fn":2160,"description":2161,"org":2162,"tags":2163,"stars":2071,"repoUrl":2072,"updatedAt":2171},"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},[2164,2167,2168],{"name":2165,"slug":2166,"type":15},"Code Review","code-review",{"name":2094,"slug":2095,"type":15},{"name":2169,"slug":2170,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2173,"name":2173,"fn":2174,"description":2175,"org":2176,"tags":2177,"stars":2071,"repoUrl":2072,"updatedAt":2180},"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},[2178,2179],{"name":2145,"slug":2146,"type":15},{"name":1990,"slug":1991,"type":15},"2026-07-14T05:25:54.928983",{"slug":2182,"name":2182,"fn":2183,"description":2184,"org":2185,"tags":2186,"stars":2071,"repoUrl":2072,"updatedAt":2189},"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},[2187,2188],{"name":2000,"slug":2001,"type":15},{"name":2081,"slug":2082,"type":15},"2026-07-30T05:29:03.275638",496]