[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-jetson-diagnostic":3,"mdc-hmiky2-key":34,"related-org-nvidia-jetson-diagnostic":2983,"related-repo-nvidia-jetson-diagnostic":3144},{"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},"jetson-diagnostic","diagnose NVIDIA Jetson hardware health","Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Monitoring","monitoring",{"name":20,"slug":21,"type":15},"Hardware","hardware",{"name":9,"slug":8,"type":15},2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:30:19.140652","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\u002Fjetson-diagnostic","---\nname: jetson-diagnostic\ndescription: Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.\nversion: 0.0.1\nlicense: \"Apache-2.0\"\nmetadata:\n  author: \"Jetson Team\"\n  tags: [jetson, diagnostic, telemetry]\n  languages: [bash]\n  data-classification: public\n---\n\n# Jetson Diagnostic\n\nA unified, agent-friendly view of a running Jetson device. Replaces the need to remember which of `tegrastats`, `jtop`, `procrank`, `\u002Fsys\u002Fkernel\u002Fdebug\u002Fnvmap`, `nvpmodel`, `free`, `swapon`, `df`, and `systemctl list-units` produces which slice of the truth.\n\n## Purpose\n\nCapture a read-only health snapshot from the Jetson host so agents can answer device identity, memory, GPU, thermal, power, storage, and service-state questions using live data instead of guesses.\n\n## When to use\n\nActivate when the user asks:\n\n- \"What is this Jetson? What SKU? How much memory?\"\n- \"What's running on this Jetson right now?\"\n- \"Why is my Jetson slow \u002F hot \u002F out of memory?\"\n- \"Give me a snapshot of GPU \u002F CPU \u002F power usage.\"\n- \"What does my tegrastats output mean?\"\n- \"Which services are running that I could turn off?\"\n- The user has installed `jetson-memory-audit`, `jetson-headless-mode`, `jetson-inference-mem-tune`, `jetson-llm-benchmark`, `jetson-llm-serve`, or `jetson-package` and needs a baseline measurement before running them.\n\nDo not use this skill to change power modes, drop caches, stop services, install packages, serve models, or tune inference flags. Report the observed state, then hand off to the action-oriented skill.\n\n## Prerequisites\n\n- Run on the Jetson host, or in a sandbox\u002Fcontainer with host-visible Jetson system paths and process data.\n\n## Available Scripts\n\n| Script | Purpose | Arguments |\n|--------|---------|-----------|\n| `scripts\u002Fsnapshot.sh` | Emits the all-in-one JSON snapshot for identity, memory, GPU, thermal, power, disk, top processes, and candidate services. | `--human`, `--tegra-secs N`, `--top-procs N`. |\n| `scripts\u002Fmem_summary.sh` | Emits a compact human-readable RAM\u002FGPU\u002Fswap summary. | `--short`, `--watch`, `--interval N`. |\n| `scripts\u002Fdetect_jetson.sh` | Exports or prints canonical Jetson SKU\u002Fgeneration\u002Fproduct-line fields for this repo. | No arguments. |\n\nIf your agent runtime supports `run_script`, use it to run `scripts\u002Fsnapshot.sh` or `scripts\u002Fmem_summary.sh` and summarize the returned output. Otherwise run the scripts with `bash` from the repository root.\n\n## Instructions\n\n1. Run `scripts\u002Fsnapshot.sh` for the all-in-one JSON view (preferred default).\n2. For a quick human-readable memory line, run `scripts\u002Fmem_summary.sh`.\n3. To explain a single tegrastats line the user has pasted, see `references\u002Ftegrastats-fields.md`.\n4. To explain the NvMap clients output, see `references\u002Fnvmap-clients.md`.\n\n## Reporting guidance\n\nRun the matching helper script before summarizing device state, and report only fields returned by that script. If direct execution is blocked by the runtime, run it with `bash {baseDir}\u002Fscripts\u002F\u003Cscript-name>` rather than trying to chmod files.\n\n- For \"what is this Jetson\" questions, quote `product_model` or `sku`, `variant`, `l4t_version`, and `mem_total_gb`.\n- For \"slow and hot\" questions, run `snapshot.sh` and summarize both sides of the symptom: `thermal_c` for heat, plus `top_processes`, `gpu_processes`, `nvmap.top_clients`, or `gpu_source` for load. End with a concrete handoff such as `jetson-memory-audit`, `jetson-headless-mode`, or `jetson-inference-mem-tune`.\n- For \"which process is using memory\" questions, run `snapshot.sh` and name the leading process as `pid \u003Cnumber>`, `cmd`, and its `pss_kb` \u002F MiB value. If NvMap GPU memory is the relevant signal, also quote `gpu_source` and the top `nvmap.top_clients` or `gpu_processes` entry.\n\nIf your agent runtime does not automatically execute helper scripts relative to this skill directory, resolve script paths with the AgentSkills `{baseDir}` placeholder:\n\n```bash\n{baseDir}\u002Fscripts\u002Fsnapshot.sh\n{baseDir}\u002Fscripts\u002Fmem_summary.sh\n```\n\nDo not call `jetson-diagnostic` as a tool name unless the runtime explicitly registers skills as callable tools; Agent Skills are normally instructions plus files, not direct tool functions.\n\nAll scripts source the canonical platform detector at `skills\u002Fjetson-diagnostic\u002Fscripts\u002Fdetect_jetson.sh` (exports `JETSON_SKU`, `JETSON_GENERATION`, `JETSON_PRODUCT_LINE`, `JETSON_VARIANT`, `JETSON_MEM_GB`, `JETSON_L4T_VERSION`, `JETSON_PRODUCT_MODEL`). Other skills may source this detector rather than duplicating Jetson identification logic. Exits 2 with a remediation message off-platform.\n\n## Limitations\n\n- Seeing this skill file does not guarantee access to Jetson host hardware. If `\u002Fproc\u002Fdevice-tree\u002Fmodel`, `\u002Fetc\u002Fnv_tegra_release`, `tegrastats`, `nvpmodel`, `nvidia-smi`, or `\u002Fsys\u002Fkernel\u002Fdebug\u002Fnvmap` are missing inside a NemoClaw\u002FOpenClaw sandbox, say the sandbox lacks Jetson host visibility and ask the user to run on the Jetson host or relaunch with a host-visible sandbox profile.\n- NvMap debugfs often requires root, so unprivileged runs may report `gpu_source: \"none\"` or incomplete `nvmap` fields.\n- This skill reports observed state only. Do not fabricate memory, GPU, thermal, service, or reclamation data when a tool is missing or inaccessible.\n\n## Error handling\n\n- If a helper exits off-platform, report that the current environment is not a Jetson host or lacks host visibility; do not substitute generic Linux values.\n- If `tegrastats`, `nvpmodel`, `nvidia-smi`, or NvMap debugfs are unavailable, preserve the corresponding `null`, `false`, or empty fields from the JSON and explain which signal is limited.\n- If `snapshot.sh` emits malformed JSON, report the raw failure and rerun after fixing the helper output; do not hand-edit a synthetic device snapshot.\n\n## Output contract for `snapshot.sh`\n\n```json\n{\n  \"sku\": \"orin-nano\",\n  \"generation\": \"orin\",\n  \"product_line\": \"orin-nano\",\n  \"variant\": \"orin-nano-8gb\",\n  \"mem_total_gb\": 8,\n  \"l4t_version\": \"36.4.0\",\n  \"product_model\": \"nvidia jetson orin nano developer kit\",\n  \"memory_kb\": { \"total\": 8123456, \"available\": 4123456, \"swap_total\": 0, \"swap_free\": 0, \"cached\": 1234567 },\n  \"tegrastats_sample\": \"RAM 4011\u002F8138MB (lfb 8x4MB) ...\",\n  \"thermal_c\": { \"CPU\": 52.3, \"GPU\": 49.0, \"AO\": 47.0 },\n  \"power\": { \"nvpmodel_id\": 0, \"nvpmodel_name\": \"MAXN\" },\n  \"disk\": [ { \"mount\": \"\u002F\", \"used_pct\": 41 } ],\n  \"gpu_source\": \"nvmap:iovmm-clients\",\n  \"gpu_devices\": [],\n  \"gpu_processes\": [],\n  \"nvmap\": {\n    \"readable\": true,\n    \"total_kb\": 654321,\n    \"stats_total_bytes\": 669985280,\n    \"top_clients\": [ { \"pid\": 1234, \"cmd\": \"vlm-server\", \"kb\": 524288 } ]\n  },\n  \"top_processes\": [ { \"pid\": 4321, \"cmd\": \"vllm\", \"pss_kb\": 4000000 } ],\n  \"candidate_services\": { \"gdm3\": { \"active\": \"inactive\", \"enabled\": \"disabled\" } }\n}\n```\n\n`gpu_source` names the *specific* datum the skill used to attribute per-process GPU memory, so the caller can tell exactly what the numbers represent:\n\n- `\"nvidia-smi:compute-apps\"` — per-process `used_memory` values from `nvidia-smi --query-compute-apps`. Used on the unified `nvidia.ko` stack (Thor family today). Note: on this stack `nvidia-smi`'s *device-level* `memory.used` query returns `[N\u002FA]` on some BSPs, which is why the skill sums the per-process list rather than reading a top-level total. The summed total appears in `gpu_processes[*].used_mib`.\n- `\"nvmap:iovmm-clients\"` — per-process sizes from `\u002Fsys\u002Fkernel\u002Fdebug\u002Fnvmap\u002Fiovmm\u002Fclients`. Used on the `nvgpu` stack (Orin family today), where `nvidia-smi` is a stub that returns `[N\u002FA]` for every compute\u002Fmemory query. Per-process entries appear in `nvmap.top_clients`; the kernel-side total is in `nvmap.total_kb` and (when readable) `nvmap.stats_total_bytes`.\n- `\"none\"` — no authoritative source reachable. Typical when running unprivileged on an `nvgpu`-stack Jetson (debugfs under `\u002Fsys\u002Fkernel\u002Fdebug\u002Fnvmap` needs `sudo`); rerun with `sudo` to populate the `nvmap` fields.\n\nThe agent should present the salient parts back to the user (SKU, available memory, top GPU consumer per `gpu_source`, hottest zone, power mode) and offer to drill into specifics (`top_processes`, `gpu_processes` \u002F `nvmap`, `services`).\n\n## Safety\n\nThis skill is **read-only**. It does not change `nvpmodel`, does not run `jetson_clocks`, does not modify services. To act on findings, hand off to:\n\n- `jetson-memory-audit` — focused memory snapshot + drop_caches verify loop\n- `jetson-headless-mode` — disable GUI + auxiliary daemons (safe, reversible)\n- `jetson-inference-mem-tune` — pick runtime + memory flags (vLLM \u002F SGLang \u002F llama.cpp \u002F TensorRT Edge-LLM)\n- `jetson-llm-serve` — vLLM and related GHCR images with Jetson defaults\n- `jetson-llm-benchmark` — reproducible latency \u002F throughput benchmarks\n- `jetson-package` — GHCR + Jetson AI Lab PyPI indexes vs generic ARM wheels\n\n## Cross-platform behavior\n\n| Family                | Variants the skill recognises                              | `tegrastats` | `nvidia-smi`          | `nvpmodel` | NvMap debugfs |\n|-----------------------|------------------------------------------------------------|--------------|-----------------------|------------|----------------|\n| Jetson Thor           | `thor-t5000`, `thor-t4000`                                 | yes          | yes (full)            | yes        | yes (root)     |\n| Jetson AGX Orin       | `orin-agx-64gb`, `orin-agx-32gb`, `orin-agx-industrial`    | yes          | yes (stub, `nvgpu`)*  | yes        | yes (root)     |\n| Jetson Orin NX        | `orin-nx-16gb`, `orin-nx-8gb`                              | yes          | yes (stub, `nvgpu`)*  | yes        | yes (root)     |\n| Jetson Orin Nano      | `orin-nano-8gb`, `orin-nano-4gb`                           | yes          | yes (stub, `nvgpu`)*  | yes        | yes (root)     |\n\n\\* On Jetsons whose GPU is driven by the in-tree `nvgpu` kernel driver, the `nvidia-smi` binary is present but most fields (`Memory-Usage`, power, utilisation, compute-process table) report `Not Supported` \u002F `N\u002FA`. To decide which source to trust at runtime, the script does a capability probe — `nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits` — and only uses `nvidia-smi` for per-process GPU memory when that query returns a real integer. When it doesn't, the script falls back to `\u002Fsys\u002Fkernel\u002Fdebug\u002Fnvmap\u002Fiovmm\u002Fclients`, which on `nvgpu`-stack Jetsons is the authoritative per-process GPU-memory source.\n\nThe script handles each tool's presence gracefully and reports `null` \u002F `false` for tools it cannot reach (typical when the agent isn't running with the privilege needed for `\u002Fsys\u002Fkernel\u002Fdebug`). Variant detection uses the `\u002Fproc\u002Fdevice-tree\u002Fmodel` string first (recognising names like `T5000` \u002F `T4000`) and falls back to memory-size heuristics when the model string is generic.\n",{"data":35,"body":46},{"name":4,"description":6,"version":36,"license":26,"metadata":37},"0.0.1",{"author":38,"tags":39,"languages":43,"data-classification":45},"Jetson Team",[40,41,42],"jetson","diagnostic","telemetry",[44],"bash","public",{"type":47,"children":48},"root",[49,57,130,137,142,148,153,237,242,248,256,262,399,433,439,490,496,509,675,688,719,731,794,800,874,880,938,949,2274,2292,2476,2515,2521,2549,2612,2618,2858,2929,2977],{"type":50,"tag":51,"props":52,"children":53},"element","h1",{"id":4},[54],{"type":55,"value":56},"text","Jetson Diagnostic",{"type":50,"tag":58,"props":59,"children":60},"p",{},[61,63,70,72,78,79,85,86,92,93,99,100,106,107,113,114,120,122,128],{"type":55,"value":62},"A unified, agent-friendly view of a running Jetson device. 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Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[3138,3141],{"name":3139,"slug":3140,"type":15},"Automation","automation",{"name":3028,"slug":3029,"type":15},"2026-07-30T05:29:03.275638",496,{"items":3145,"total":3239},[3146,3161,3171,3185,3195,3210,3225],{"slug":3147,"name":3147,"fn":3148,"description":3149,"org":3150,"tags":3151,"stars":23,"repoUrl":24,"updatedAt":3160},"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},[3152,3155,3158,3159],{"name":3153,"slug":3154,"type":15},"Data Analysis","data-analysis",{"name":3156,"slug":3157,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":13,"slug":14,"type":15},"2026-07-14T05:28:43.176466",{"slug":3162,"name":3162,"fn":3163,"description":3164,"org":3165,"tags":3166,"stars":23,"repoUrl":24,"updatedAt":3170},"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},[3167,3168,3169],{"name":3013,"slug":3014,"type":15},{"name":3106,"slug":3107,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:29:06.667109",{"slug":3172,"name":3172,"fn":3173,"description":3174,"org":3175,"tags":3176,"stars":23,"repoUrl":24,"updatedAt":3184},"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},[3177,3180,3181],{"name":3178,"slug":3179,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":3182,"slug":3183,"type":15},"Research","research","2026-07-14T05:28:06.816956",{"slug":3186,"name":3186,"fn":3187,"description":3188,"org":3189,"tags":3190,"stars":23,"repoUrl":24,"updatedAt":3194},"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},[3191,3192,3193],{"name":3153,"slug":3154,"type":15},{"name":9,"slug":8,"type":15},{"name":3095,"slug":3096,"type":15},"2026-07-17T05:29:03.913266",{"slug":3196,"name":3196,"fn":3197,"description":3198,"org":3199,"tags":3200,"stars":23,"repoUrl":24,"updatedAt":3209},"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},[3201,3202,3205,3206],{"name":3139,"slug":3140,"type":15},{"name":3203,"slug":3204,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":3207,"slug":3208,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":3211,"name":3211,"fn":3212,"description":3213,"org":3214,"tags":3215,"stars":23,"repoUrl":24,"updatedAt":3224},"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},[3216,3217,3220,3221],{"name":3013,"slug":3014,"type":15},{"name":3218,"slug":3219,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":3222,"slug":3223,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":3226,"name":3226,"fn":3227,"description":3228,"org":3229,"tags":3230,"stars":23,"repoUrl":24,"updatedAt":3238},"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},[3231,3232,3235],{"name":9,"slug":8,"type":15},{"name":3233,"slug":3234,"type":15},"Quantum Computing","quantum-computing",{"name":3236,"slug":3237,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]