Jetson Diagnostic
NVIDIAOfficialCreated by Jetson TeamDistributed by NVIDIA
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Jetson Diagnostic provides a unified, read-only interface for gathering telemetry from NVIDIA Jetson hardware. It aggregates data from various system utilities including tegrastats, NvMap, and system process monitors to provide a comprehensive snapshot of device identity, memory usage, GPU load, thermal state, power settings, and active services. It is designed to help agents identify system bottlenecks and resource consumption patterns on Jetson platforms.
What it does
- Captures a consolidated JSON snapshot of Jetson system health.
- Identifies Jetson SKU, generation, and L4T version.
- Reports thermal, power, and disk usage metrics.
- Provides per-process GPU memory attribution via NvMap or nvidia-smi.
- Lists top processes and candidate services for resource management.
- Offers a compact summary for quick memory and swap status checks.
When to use it
- When investigating performance issues or unexpected system behavior on a Jetson device.
- To establish a baseline measurement before running memory-intensive tasks or benchmarks.
- When needing to identify which processes are consuming GPU or system memory.
- To verify the current power mode and thermal status of the hardware.
Advantages
- Unified output replaces the need to manually query multiple disparate system tools.
- Automated detection of the correct GPU memory source based on the driver stack.
- Provides actionable data for further troubleshooting with specialized Jetson tools.
- Read-only design ensures no unintended changes are made to system configuration.
Usage examples
- “What is the current health status of this Jetson device?”
- The agent executes the snapshot script and summarizes the SKU, thermal readings, memory usage, and top processes.
- “Why is my Jetson running slowly?”
- The agent runs the diagnostic snapshot to report thermal levels and identify processes with high CPU or NvMap memory consumption.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/jetson-diagnostic/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip jetson-diagnostic.zip -d .agents/skills
Documentation by Jetson Team
Jetson Diagnostic
A unified, agent-friendly view of a running Jetson device. Replaces the need to remember which of tegrastats, jtop, procrank, /sys/kernel/debug/nvmap, nvpmodel, free, swapon, df, and systemctl list-units produces which slice of the truth.
Purpose
Capture 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.
When to use
Activate when the user asks:
"What is this Jetson? What SKU? How much memory?"
"What's running on this Jetson right now?"
"Why is my Jetson slow / hot / out of memory?"
"Give me a snapshot of GPU / CPU / power usage."
"What does my tegrastats output mean?"
"Which services are running that I could turn off?"
The user has installed
jetson-memory-audit,jetson-headless-mode,jetson-inference-mem-tune,jetson-llm-benchmark,jetson-llm-serve, orjetson-packageand needs a baseline measurement before running them.
Do 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.
Prerequisites
Run on the Jetson host, or in a sandbox/container with host-visible Jetson system paths and process data.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/snapshot.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. |
scripts/mem_summary.sh | Emits a compact human-readable RAM/GPU/swap summary. | --short, --watch, --interval N. |
scripts/detect_jetson.sh | Exports or prints canonical Jetson SKU/generation/product-line fields for this repo. | No arguments. |
If your agent runtime supports run_script, use it to run scripts/snapshot.sh or scripts/mem_summary.sh and summarize the returned output. Otherwise run the scripts with bash from the repository root.
Instructions
Run
scripts/snapshot.shfor the all-in-one JSON view (preferred default).For a quick human-readable memory line, run
scripts/mem_summary.sh.To explain a single tegrastats line the user has pasted, see
references/tegrastats-fields.md.To explain the NvMap clients output, see
references/nvmap-clients.md.
Reporting guidance
Run 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}/scripts/<script-name> rather than trying to chmod files.
For "what is this Jetson" questions, quote
product_modelorsku,variant,l4t_version, andmem_total_gb.For "slow and hot" questions, run
snapshot.shand summarize both sides of the symptom:thermal_cfor heat, plustop_processes,gpu_processes,nvmap.top_clients, orgpu_sourcefor load. End with a concrete handoff such asjetson-memory-audit,jetson-headless-mode, orjetson-inference-mem-tune.For "which process is using memory" questions, run
snapshot.shand name the leading process aspid <number>,cmd, and itspss_kb/ MiB value. If NvMap GPU memory is the relevant signal, also quotegpu_sourceand the topnvmap.top_clientsorgpu_processesentry.
If your agent runtime does not automatically execute helper scripts relative to this skill directory, resolve script paths with the AgentSkills {baseDir} placeholder:
{baseDir}/scripts/snapshot.sh
{baseDir}/scripts/mem_summary.shDo 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.
All scripts source the canonical platform detector at skills/jetson-diagnostic/scripts/detect_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.
Limitations
Seeing this skill file does not guarantee access to Jetson host hardware. If
/proc/device-tree/model,/etc/nv_tegra_release,tegrastats,nvpmodel,nvidia-smi, or/sys/kernel/debug/nvmapare missing inside a NemoClaw/OpenClaw 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.NvMap debugfs often requires root, so unprivileged runs may report
gpu_source: "none"or incompletenvmapfields.This skill reports observed state only. Do not fabricate memory, GPU, thermal, service, or reclamation data when a tool is missing or inaccessible.
Error handling
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.
If
tegrastats,nvpmodel,nvidia-smi, or NvMap debugfs are unavailable, preserve the correspondingnull,false, or empty fields from the JSON and explain which signal is limited.If
snapshot.shemits malformed JSON, report the raw failure and rerun after fixing the helper output; do not hand-edit a synthetic device snapshot.
Output contract for snapshot.sh
{
"sku": "orin-nano",
"generation": "orin",
"product_line": "orin-nano",
"variant": "orin-nano-8gb",
"mem_total_gb": 8,
"l4t_version": "36.4.0",
"product_model": "nvidia jetson orin nano developer kit",
"memory_kb": { "total": 8123456, "available": 4123456, "swap_total": 0, "swap_free": 0, "cached": 1234567 },
"tegrastats_sample": "RAM 4011/8138MB (lfb 8x4MB) ...",
"thermal_c": { "CPU": 52.3, "GPU": 49.0, "AO": 47.0 },
"power": { "nvpmodel_id": 0, "nvpmodel_name": "MAXN" },
"disk": [ { "mount": "/", "used_pct": 41 } ],
"gpu_source": "nvmap:iovmm-clients",
"gpu_devices": [],
"gpu_processes": [],
"nvmap": {
"readable": true,
"total_kb": 654321,
"stats_total_bytes": 669985280,
"top_clients": [ { "pid": 1234, "cmd": "vlm-server", "kb": 524288 } ]
},
"top_processes": [ { "pid": 4321, "cmd": "vllm", "pss_kb": 4000000 } ],
"candidate_services": { "gdm3": { "active": "inactive", "enabled": "disabled" } }
}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:
"nvidia-smi:compute-apps"— per-processused_memoryvalues fromnvidia-smi --query-compute-apps. Used on the unifiednvidia.kostack (Thor family today). Note: on this stacknvidia-smi's device-levelmemory.usedquery returns[N/A]on some BSPs, which is why the skill sums the per-process list rather than reading a top-level total. The summed total appears ingpu_processes[*].used_mib."nvmap:iovmm-clients"— per-process sizes from/sys/kernel/debug/nvmap/iovmm/clients. Used on thenvgpustack (Orin family today), wherenvidia-smiis a stub that returns[N/A]for every compute/memory query. Per-process entries appear innvmap.top_clients; the kernel-side total is innvmap.total_kband (when readable)nvmap.stats_total_bytes."none"— no authoritative source reachable. Typical when running unprivileged on annvgpu-stack Jetson (debugfs under/sys/kernel/debug/nvmapneedssudo); rerun withsudoto populate thenvmapfields.
The 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 / nvmap, services).
Safety
This 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:
jetson-memory-audit— focused memory snapshot + drop_caches verify loopjetson-headless-mode— disable GUI + auxiliary daemons (safe, reversible)jetson-inference-mem-tune— pick runtime + memory flags (vLLM / SGLang / llama.cpp / TensorRT Edge-LLM)jetson-llm-serve— vLLM and related GHCR images with Jetson defaultsjetson-llm-benchmark— reproducible latency / throughput benchmarksjetson-package— GHCR + Jetson AI Lab PyPI indexes vs generic ARM wheels
Cross-platform behavior
| Family | Variants the skill recognises | tegrastats | nvidia-smi | nvpmodel | NvMap debugfs |
|---|---|---|---|---|---|
| Jetson Thor | thor-t5000, thor-t4000 | yes | yes (full) | yes | yes (root) |
| Jetson AGX Orin | orin-agx-64gb, orin-agx-32gb, orin-agx-industrial | yes | yes (stub, nvgpu)* | yes | yes (root) |
| Jetson Orin NX | orin-nx-16gb, orin-nx-8gb | yes | yes (stub, nvgpu)* | yes | yes (root) |
| Jetson Orin Nano | orin-nano-8gb, orin-nano-4gb | yes | yes (stub, nvgpu)* | yes | yes (root) |
* 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 / N/A. 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 /sys/kernel/debug/nvmap/iovmm/clients, which on nvgpu-stack Jetsons is the authoritative per-process GPU-memory source.
The script handles each tool's presence gracefully and reports null / false for tools it cannot reach (typical when the agent isn't running with the privilege needed for /sys/kernel/debug). Variant detection uses the /proc/device-tree/model string first (recognising names like T5000 / T4000) and falls back to memory-size heuristics when the model string is generic.
Files
- ATTRIBUTION.mdAdded by AutoGineer914 B
- BENCHMARK.md3.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.6 KiB
- evals/evals.json5.3 KiB
- references/nvmap-clients.md3.6 KiB
- references/tegrastats-fields.md9.8 KiB
- scripts/common.shScript426 B
- scripts/detect_jetson.shScript9.5 KiB
- scripts/mem_summary.shScript6.5 KiB
- scripts/runtime_probes.shScript1.7 KiB
- scripts/snapshot.shScript14.0 KiB
- skill-card.md3.6 KiB
- skill.oms.sig6.1 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Jetson Team
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/jetson-diagnostic · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download jetson-diagnostic.zip