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This is an example skill.\nversion: 0.0.1\nlicense: \"Apache-2.0\"\nmetadata:\n  author: \"Jetson Team\"\n  tags: [jetson, bsp, info]\n  languages: [bash]\n  data-classification: public\n---\n\n# jetson-print-bsp-info\n\nPrints a concise summary of a Jetson Linux_for_Tegra (BSP) tree on the host PC.\n\nThis skill is intended as a reference example for the `jetson-bsp-skills` repo and the NVIDIA-wide skills CI. It performs read-only inspection — no flashing, no rootfs changes.\n\n## Purpose\n\nCapture a baseline snapshot of a Linux_for_Tegra BSP tree (release, board configs, rootfs state) before flashing, so issues like \"wrong L4T version\" or \"rootfs never populated\" are caught early.\n\n## When to use\n\n- A user has unpacked a Jetson BSP tarball and wants to confirm the L4T version, supported boards, and rootfs state before flashing.\n- You need a quick sanity check that a `Linux_for_Tegra\u002F` directory looks valid (expected scripts and config files present).\n\n## Prerequisites\n\n- Running on the host PC (Linux), not on the Jetson target.\n- A `Linux_for_Tegra\u002F` directory extracted from a Jetson BSP tarball.\n- Standard CLIs available: `ls`, `head`, `cat`, `paste`, `sed`.\n\n## Inputs\n\n- `L4T_ROOT` (optional): absolute path to the `Linux_for_Tegra\u002F` directory. If unset, use the current working directory.\n\n## Instructions\n\nRun each step in order and print the captured values into the report shown under [Output format](#output-format).\n\n1. **Resolve** `L4T_ROOT` and **validate** the directory is a Linux_for_Tegra root — exit early otherwise. `flash.sh` and `nv_tegra\u002F` are the two anchor artifacts that every BSP ships:\n   ```bash\n   L4T_ROOT=\"${L4T_ROOT:-$PWD}\"\n   if [ ! -f \"$L4T_ROOT\u002Fflash.sh\" ] || [ ! -d \"$L4T_ROOT\u002Fnv_tegra\" ]; then\n     echo \"Not a Linux_for_Tegra root: '$L4T_ROOT' (missing flash.sh or nv_tegra\u002F)\"\n     exit 1\n   fi\n   echo \"$L4T_ROOT\"\n   ```\n2. **Extract** the L4T release header line. The canonical host-side location is `nv_tegra\u002Fnv_tegra_release`; the same file is copied into the rootfs by `apply_binaries.sh`. Only the first line is useful — the rest is a long list of library SHAs:\n   ```bash\n   head -1 \"$L4T_ROOT\u002Fnv_tegra\u002Fnv_tegra_release\" 2>\u002Fdev\u002Fnull \\\n     || head -1 \"$L4T_ROOT\u002Frootfs\u002Fetc\u002Fnv_tegra_release\" 2>\u002Fdev\u002Fnull \\\n     || echo \"L4T release info not found\"\n   ```\n3. **List** supported board config files and **join** them onto one comma-separated line:\n   ```bash\n   (cd \"$L4T_ROOT\" && ls *.conf 2>\u002Fdev\u002Fnull) | paste -sd, -\n   ```\n4. **Check** whether the rootfs has been populated. An empty `rootfs\u002F` means `apply_binaries.sh` has not been run yet:\n   ```bash\n   if [ -f \"$L4T_ROOT\u002Frootfs\u002Fetc\u002Fpasswd\" ]; then\n     echo \"populated\"\n   else\n     echo \"empty\"\n   fi\n   ```\n\n## Output format\n\nPrint a short report with these sections, one line each where possible:\n\n```text\nL4T root:        \u003Cpath>\nL4T release:     \u003Crelease header line>\nBoard configs:   \u003Ccomma-separated list>\nRootfs:          populated | empty\n```\n\n## Examples\n\nExample output on an Orin AGX BSP (L4T R36):\n\n```text\nL4T root:        $HOME\u002FLinux_for_Tegra\nL4T release:     # R36 (release), REVISION: 3.0\nBoard configs:   jetson-agx-orin-devkit.conf,jetson-orin-nano-devkit.conf\nRootfs:          populated\n```\n\nExample output on a freshly untarred BSP where `apply_binaries.sh` has not been run yet:\n\n```text\nL4T root:        \u002Ftmp\u002FLinux_for_Tegra\nL4T release:     # R39 (release), REVISION: 0.0\nBoard configs:   jetson-agx-thor-devkit.conf\nRootfs:          empty\n```\n\n## Error handling\n\nEach command falls back to a clearly labeled `\"... not found\"` string if the underlying file is missing — the skill never errors out mid-report. If `L4T_ROOT` does not contain `flash.sh` and `nv_tegra\u002F`, exit early with a clear \"not a Linux_for_Tegra root\" message rather than printing misleading info.\n\n## Limitations\n\n- Read-only inspection only — does not validate signatures, kernel images, or device-tree overlays.\n- Only checks the presence of `rootfs\u002Fetc\u002Fpasswd` as a populated-rootfs proxy; will not detect a half-populated rootfs.\n- Lists all `*.conf` board configs in `L4T_ROOT\u002F`; does not try to infer which one the user intends to flash.\n\n## Troubleshooting\n\n- **Error:** `Not a Linux_for_Tegra root: '...' 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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},[1222,1223,1224],{"name":1212,"slug":1213,"type":15},{"name":1197,"slug":1198,"type":15},{"name":1225,"slug":1226,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1229,"name":1229,"fn":1230,"description":1231,"org":1232,"tags":1233,"stars":1202,"repoUrl":1203,"updatedAt":1241},"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},[1234,1237,1238],{"name":1235,"slug":1236,"type":15},"Debugging","debugging",{"name":1225,"slug":1226,"type":15},{"name":1239,"slug":1240,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1243,"name":1243,"fn":1244,"description":1245,"org":1246,"tags":1247,"stars":1202,"repoUrl":1203,"updatedAt":1254},"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},[1248,1251],{"name":1249,"slug":1250,"type":15},"Best Practices","best-practices",{"name":1252,"slug":1253,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1256,"name":1256,"fn":1257,"description":1258,"org":1259,"tags":1260,"stars":1202,"repoUrl":1203,"updatedAt":1268},"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},[1261,1264,1267],{"name":1262,"slug":1263,"type":15},"Machine Learning","machine-learning",{"name":1265,"slug":1266,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1270,"name":1270,"fn":1271,"description":1272,"org":1273,"tags":1274,"stars":1202,"repoUrl":1203,"updatedAt":1281},"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},[1275,1278],{"name":1276,"slug":1277,"type":15},"QA","qa",{"name":1279,"slug":1280,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":1283,"name":1283,"fn":1284,"description":1285,"org":1286,"tags":1287,"stars":1202,"repoUrl":1203,"updatedAt":1292},"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},[1288,1289],{"name":1197,"slug":1198,"type":15},{"name":1290,"slug":1291,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":1294,"name":1294,"fn":1295,"description":1296,"org":1297,"tags":1298,"stars":1202,"repoUrl":1203,"updatedAt":1306},"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},[1299,1302,1303],{"name":1300,"slug":1301,"type":15},"Code Review","code-review",{"name":1225,"slug":1226,"type":15},{"name":1304,"slug":1305,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1308,"name":1308,"fn":1309,"description":1310,"org":1311,"tags":1312,"stars":1202,"repoUrl":1203,"updatedAt":1315},"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},[1313,1314],{"name":1276,"slug":1277,"type":15},{"name":1279,"slug":1280,"type":15},"2026-07-14T05:25:54.928983",{"slug":1317,"name":1317,"fn":1318,"description":1319,"org":1320,"tags":1321,"stars":1202,"repoUrl":1203,"updatedAt":1326},"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},[1322,1325],{"name":1323,"slug":1324,"type":15},"Automation","automation",{"name":1212,"slug":1213,"type":15},"2026-07-30T05:29:03.275638",496,{"items":1329,"total":1425},[1330,1347,1357,1371,1381,1396,1411],{"slug":1331,"name":1331,"fn":1332,"description":1333,"org":1334,"tags":1335,"stars":20,"repoUrl":21,"updatedAt":1346},"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},[1336,1339,1342,1343],{"name":1337,"slug":1338,"type":15},"Data Analysis","data-analysis",{"name":1340,"slug":1341,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":1344,"slug":1345,"type":15},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":1348,"name":1348,"fn":1349,"description":1350,"org":1351,"tags":1352,"stars":20,"repoUrl":21,"updatedAt":1356},"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},[1353,1354,1355],{"name":1197,"slug":1198,"type":15},{"name":1290,"slug":1291,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":1358,"name":1358,"fn":1359,"description":1360,"org":1361,"tags":1362,"stars":20,"repoUrl":21,"updatedAt":1370},"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},[1363,1366,1367],{"name":1364,"slug":1365,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":1368,"slug":1369,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":1372,"name":1372,"fn":1373,"description":1374,"org":1375,"tags":1376,"stars":20,"repoUrl":21,"updatedAt":1380},"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},[1377,1378,1379],{"name":1337,"slug":1338,"type":15},{"name":9,"slug":8,"type":15},{"name":1279,"slug":1280,"type":15},"2026-07-17T05:29:03.913266",{"slug":1382,"name":1382,"fn":1383,"description":1384,"org":1385,"tags":1386,"stars":20,"repoUrl":21,"updatedAt":1395},"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},[1387,1388,1391,1392],{"name":1323,"slug":1324,"type":15},{"name":1389,"slug":1390,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1393,"slug":1394,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1397,"name":1397,"fn":1398,"description":1399,"org":1400,"tags":1401,"stars":20,"repoUrl":21,"updatedAt":1410},"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},[1402,1403,1406,1407],{"name":1197,"slug":1198,"type":15},{"name":1404,"slug":1405,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1408,"slug":1409,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1412,"name":1412,"fn":1413,"description":1414,"org":1415,"tags":1416,"stars":20,"repoUrl":21,"updatedAt":1424},"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},[1417,1418,1421],{"name":9,"slug":8,"type":15},{"name":1419,"slug":1420,"type":15},"Quantum Computing","quantum-computing",{"name":1422,"slug":1423,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]