[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-i4h-workflow-dataset-teleop":3,"mdc--cde6jc-key":34,"related-org-nvidia-i4h-workflow-dataset-teleop":2119,"related-repo-nvidia-i4h-workflow-dataset-teleop":2278},{"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},"i4h-workflow-dataset-teleop","record agentic environment episodes via teleoperation","Record episodes for an agentic env via teleoperation (keyboard, SO-ARM leader, or VR) into HDF5. Use when the user wants to teleop or record human demos.",{"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},"Robotics","robotics","tag",{"name":17,"slug":18,"type":15},"Machine Learning","machine-learning",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Simulation","simulation",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-23T06:05:56.858982","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\u002Fi4h-workflow-dataset-teleop","---\nname: i4h-workflow-dataset-teleop\nversion: \"0.6.0\"\ndescription: Record episodes for an agentic env via teleoperation (keyboard, SO-ARM leader, or VR) into HDF5. Use when the user wants to teleop or record human demos.\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    - teleoperation\n    - recording\n---\n\n# i4h Workflow — Teleop Record\n\n## Purpose\n\nRecord episodes for an agentic env via teleoperation (keyboard, SO-ARM leader, or VR) into HDF5. Use when the user wants to teleop or record human demos.\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 \"$ROOT\"\n```\n\n## Basics\n\n- **Env config (source of truth):** `workflows\u002Fagentic\u002Fconfig\u002Fenvironments\u002F\u003Cenv>.yaml` — `robot.type`, `zenoh.camera_names`, and the task for `\u003Cenv>`.\n- Teleop runs through `arena\u002Frun.sh --teleop`.\n- Device support is env-specific. Check `arena\u002Frun.sh --env \u003Cenv> --help` for valid `--teleop-device` values.\n- Do not run teleop headless unless the user explicitly asks. Teleop is a GUI\u002Fhardware handoff: launching the recorder is not the same as recording successful demos.\n\n## Controls\n\nReserved keys (consistent across devices):\n\n| Key | Action |\n|---|---|\n| `B` | Start episode |\n| `N` | Mark success, save, advance |\n| `R` | Discard, reset |\n| `F` | Reserved by Isaac Sim — do not bind |\n\nDevice-specific keybindings (move, rotate, gripper, mode switches) are printed by the teleop process at startup and vary by `--teleop-device`. Report them to the user from the log; they cannot drive the sim without them. See \"Surface Device Keybindings\". For `keyboard_23d`, the banner can print late, after scene creation and recorder setup; wait for mode\u002Fcontrol markers such as `BOTH_HANDS`, `HAND MODE`, `BASE NAVIGATION MODE`, `SPECIAL KEYS`, or `Current Mode` before saying the controls are missing.\n\nStop from terminal:\n\n```bash\nworkflows\u002Fagentic\u002Fstop.sh arena --env \u003Cenv>\n```\n\n## Known Devices\n\n| Env | Devices |\n|---|---|\n| `scissor_pick_and_place` | `keyboard`, `so101_leader` |\n| `locomanip_tray_pick_and_place` | `keyboard_23d` |\n| `locomanip_push_cart` | `keyboard_23d` |\n\nFor other envs, consult `arena\u002Frun.sh --env \u003Cenv> --help`.\n\n## Run\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — setup\n\n```bash\nREPO_ROOT=\"${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\"\nRUN_DIR=\"${RUNS_ROOT}\u002Fteleop_${ENV_ID}_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Fdata\" \"${RUN_DIR}\u002Flogs\"\nln -sfn \"${RUN_DIR}\" \"${RUNS_ROOT}\u002F.latest\"\n```\n\n### Step 2 — teleop record\n\nUse the foreground form when the human operator is ready to drive immediately and the agent can stay attached until completion:\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Farena\u002Frun.sh\" \\\n  --env \"${ENV_ID}\" \\\n  --teleop \\\n  --teleop-device \u003Cdevice> \\\n  --episodes 3 \\\n  --record-to \"${RUN_DIR}\u002Fdata\u002Fdemo.hdf5\" \\\n  2>&1 | tee \"${RUN_DIR}\u002Flogs\u002Fteleop.log\"\n```\n\nIf no human operator is ready, do not fake demos and do not leave a teleop process behind. You may launch long enough to confirm the visible GUI, recorder, and controls, then stop cleanly and report that recording is waiting on a human operator.\n\n## Surface Device Keybindings\n\nThe teleop process prints its keybinding table to stdout shortly after launch (look for sections such as `Keybindings`, `Controls`, `Key Map`, `BOTH_HANDS`, `HAND MODE`, `BASE NAVIGATION MODE`, `SPECIAL KEYS`, `WORKSPACE LIMITS`, `Current Mode`, mode-switch lines, or any block enumerating keys\u002Factions). Wait for the table to appear, extract it from the log, and report it to the user before they need to drive the sim.\n\n```bash\n# After launching teleop and tailing the log:\nfor i in $(seq 1 180); do\n  if grep -qiE 'Keybind|Controls|Key Map|BOTH_HANDS|HAND MODE|BASE NAVIGATION MODE|SPECIAL KEYS|WORKSPACE LIMITS|Current Mode|Mode\\] Switched|run complete|Traceback|Error' \"${RUN_DIR}\u002Flogs\u002Fteleop.log\" 2>\u002Fdev\u002Fnull; then\n    break\n  fi\n  sleep 1\ndone\ngrep -n -A80 -E 'Keybind|Controls|Key Map|BOTH_HANDS|HAND MODE|BASE NAVIGATION MODE|SPECIAL KEYS|WORKSPACE LIMITS|Current Mode|Mode\\] Switched' \"${RUN_DIR}\u002Flogs\u002Fteleop.log\" | head -120\n```\n\nIf the block is multi-section (e.g. `BOTH_HANDS`, `BASE_NAV`, `LEFT_HAND`, `RIGHT_HAND` modes), include every mode in the report. Append the reserved keys above so the user has one consolidated reference.\n\n## Notes\n\n- `--record-to` must be absolute. The recorder resolves relative paths against `workflows\u002Fagentic\u002Farena` (its CWD) and writes to a nested orphan dir. `${RUN_DIR}\u002Fdata\u002Fdemo.hdf5` built from `${REPO_ROOT}` is absolute.\n- Use `--save-all-episodes` only when failed attempts must be kept.\n- If the prompt asks \"Run teleop for N episodes\" and no human\u002Foperator input is available, launch the visible recorder only for readiness\u002Fcontrol verification, stop it cleanly, and report that no demos were recorded; do not claim N episodes were recorded.\n\n## Verify\n\n- `${RUN_DIR}\u002Fdata\u002Fdemo.hdf5` exists.\n- Log contains `run complete: N\u002FM episodes succeeded`, and N must equal the requested episode count before reporting success. A tiny HDF5 with `0\u002FM episodes succeeded` is only a failed\u002Fempty recording artifact, not a usable dataset.\n- Before final response, verify no unintended teleop process is left running unless the user explicitly asked to keep it open.\n\n## Prerequisites\n\n- Workflow set up via [[i4h-workflow-setup]] (the `.venv` must exist).\n- A valid env id and a `--teleop-device` it supports (see Known Devices or `arena\u002Frun.sh --env \u003Cenv> --help`).\n- The chosen teleop device available (keyboard, SO-ARM leader, or VR).\n- An absolute `--record-to` HDF5 path (relative paths resolve against `arena`'s CWD).\n\n## Limitations\n\n- Device support is env-specific; not every device works with every env.\n- `--record-to` must be absolute or the recording lands in a nested orphan dir.\n- By default only successful episodes are saved; use `--save-all-episodes` to keep failed attempts.\n- Device-specific keybindings are only printed at startup; they cannot be known before launching.\n\n## Troubleshooting\n\n- **Error:** `.venv` not found \u002F teleop fails to launch - Cause: workflow not set up. 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Fix: pick a value from ",{"type":49,"tag":81,"props":2048,"children":2050},{"className":2049},[],[2051],{"type":55,"value":523},{"type":55,"value":2053}," (or Known Devices).",{"type":49,"tag":458,"props":2055,"children":2056},{},[2057,2061,2063,2068,2070,2075],{"type":49,"tag":462,"props":2058,"children":2059},{},[2060],{"type":55,"value":2022},{"type":55,"value":2062}," HDF5 written to an unexpected\u002Fnested location - Cause: ",{"type":49,"tag":81,"props":2064,"children":2066},{"className":2065},[],[2067],{"type":55,"value":1809},{"type":55,"value":2069}," was relative. 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Fix: record successful source demos with an operator; do not continue to mimic\u002Fconvert\u002Ffinetune from an empty HDF5.",{"type":49,"tag":58,"props":2103,"children":2105},{"id":2104},"final-response",[2106],{"type":55,"value":2107},"Final Response",{"type":49,"tag":65,"props":2109,"children":2110},{},[2111],{"type":55,"value":2112},"Report env, device, the device-specific keybinding table extracted from the log, requested vs saved episodes, HDF5 path, log path.",{"type":49,"tag":2114,"props":2115,"children":2116},"style",{},[2117],{"type":55,"value":2118},"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":2120,"total":2277},[2121,2139,2157,2168,2180,2194,2207,2219,2232,2243,2257,2266],{"slug":2122,"name":2122,"fn":2123,"description":2124,"org":2125,"tags":2126,"stars":2136,"repoUrl":2137,"updatedAt":2138},"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},[2127,2130,2133],{"name":2128,"slug":2129,"type":15},"Documentation","documentation",{"name":2131,"slug":2132,"type":15},"MCP","mcp",{"name":2134,"slug":2135,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2140,"name":2140,"fn":2141,"description":2142,"org":2143,"tags":2144,"stars":2154,"repoUrl":2155,"updatedAt":2156},"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},[2145,2148,2151],{"name":2146,"slug":2147,"type":15},"Containers","containers",{"name":2149,"slug":2150,"type":15},"Deployment","deployment",{"name":2152,"slug":2153,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2158,"name":2158,"fn":2159,"description":2160,"org":2161,"tags":2162,"stars":2154,"repoUrl":2155,"updatedAt":2167},"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},[2163,2166],{"name":2164,"slug":2165,"type":15},"CI\u002FCD","ci-cd",{"name":2149,"slug":2150,"type":15},"2026-07-14T05:25:59.97109",{"slug":2169,"name":2169,"fn":2170,"description":2171,"org":2172,"tags":2173,"stars":2154,"repoUrl":2155,"updatedAt":2179},"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},[2174,2175,2176],{"name":2164,"slug":2165,"type":15},{"name":2149,"slug":2150,"type":15},{"name":2177,"slug":2178,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2181,"name":2181,"fn":2182,"description":2183,"org":2184,"tags":2185,"stars":2154,"repoUrl":2155,"updatedAt":2193},"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},[2186,2189,2190],{"name":2187,"slug":2188,"type":15},"Debugging","debugging",{"name":2177,"slug":2178,"type":15},{"name":2191,"slug":2192,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2195,"name":2195,"fn":2196,"description":2197,"org":2198,"tags":2199,"stars":2154,"repoUrl":2155,"updatedAt":2206},"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},[2200,2203],{"name":2201,"slug":2202,"type":15},"Best Practices","best-practices",{"name":2204,"slug":2205,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2208,"name":2208,"fn":2209,"description":2210,"org":2211,"tags":2212,"stars":2154,"repoUrl":2155,"updatedAt":2218},"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},[2213,2214,2217],{"name":17,"slug":18,"type":15},{"name":2215,"slug":2216,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2220,"name":2220,"fn":2221,"description":2222,"org":2223,"tags":2224,"stars":2154,"repoUrl":2155,"updatedAt":2231},"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},[2225,2228],{"name":2226,"slug":2227,"type":15},"QA","qa",{"name":2229,"slug":2230,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":2233,"name":2233,"fn":2234,"description":2235,"org":2236,"tags":2237,"stars":2154,"repoUrl":2155,"updatedAt":2242},"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},[2238,2239],{"name":2149,"slug":2150,"type":15},{"name":2240,"slug":2241,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":2244,"name":2244,"fn":2245,"description":2246,"org":2247,"tags":2248,"stars":2154,"repoUrl":2155,"updatedAt":2256},"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},[2249,2252,2253],{"name":2250,"slug":2251,"type":15},"Code Review","code-review",{"name":2177,"slug":2178,"type":15},{"name":2254,"slug":2255,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2258,"name":2258,"fn":2259,"description":2260,"org":2261,"tags":2262,"stars":2154,"repoUrl":2155,"updatedAt":2265},"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},[2263,2264],{"name":2226,"slug":2227,"type":15},{"name":2229,"slug":2230,"type":15},"2026-07-14T05:25:54.928983",{"slug":2267,"name":2267,"fn":2268,"description":2269,"org":2270,"tags":2271,"stars":2154,"repoUrl":2155,"updatedAt":2276},"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},[2272,2275],{"name":2273,"slug":2274,"type":15},"Automation","automation",{"name":2164,"slug":2165,"type":15},"2026-07-30T05:29:03.275638",496,{"items":2279,"total":2373},[2280,2297,2307,2321,2331,2346,2361],{"slug":2281,"name":2281,"fn":2282,"description":2283,"org":2284,"tags":2285,"stars":23,"repoUrl":24,"updatedAt":2296},"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},[2286,2289,2292,2293],{"name":2287,"slug":2288,"type":15},"Data Analysis","data-analysis",{"name":2290,"slug":2291,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":2294,"slug":2295,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":2298,"name":2298,"fn":2299,"description":2300,"org":2301,"tags":2302,"stars":23,"repoUrl":24,"updatedAt":2306},"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},[2303,2304,2305],{"name":2149,"slug":2150,"type":15},{"name":2240,"slug":2241,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":2308,"name":2308,"fn":2309,"description":2310,"org":2311,"tags":2312,"stars":23,"repoUrl":24,"updatedAt":2320},"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},[2313,2316,2317],{"name":2314,"slug":2315,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":2318,"slug":2319,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":2322,"name":2322,"fn":2323,"description":2324,"org":2325,"tags":2326,"stars":23,"repoUrl":24,"updatedAt":2330},"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},[2327,2328,2329],{"name":2287,"slug":2288,"type":15},{"name":9,"slug":8,"type":15},{"name":2229,"slug":2230,"type":15},"2026-07-17T05:29:03.913266",{"slug":2332,"name":2332,"fn":2333,"description":2334,"org":2335,"tags":2336,"stars":23,"repoUrl":24,"updatedAt":2345},"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},[2337,2338,2341,2342],{"name":2273,"slug":2274,"type":15},{"name":2339,"slug":2340,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":2343,"slug":2344,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":2347,"name":2347,"fn":2348,"description":2349,"org":2350,"tags":2351,"stars":23,"repoUrl":24,"updatedAt":2360},"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},[2352,2353,2356,2357],{"name":2149,"slug":2150,"type":15},{"name":2354,"slug":2355,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":2358,"slug":2359,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":2362,"name":2362,"fn":2363,"description":2364,"org":2365,"tags":2366,"stars":23,"repoUrl":24,"updatedAt":2372},"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},[2367,2368,2371],{"name":9,"slug":8,"type":15},{"name":2369,"slug":2370,"type":15},"Quantum Computing","quantum-computing",{"name":21,"slug":22,"type":15},"2026-07-14T05:26:58.898253",305]