Official agent skill

Jetson Diagnostic

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Jetson Diagnostic

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/jetson-diagnostic .claude/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .claude/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/jetson-diagnostic .agents/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .agents/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/jetson-diagnostic .cursor/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .cursor/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/jetson-diagnostic

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/jetson-diagnostic .gemini/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .gemini/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/jetson-diagnostic .github/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .github/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-diagnostic -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-diagnostic --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/jetson-diagnostic .opencode/skills/jetson-diagnostic && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "jetson-diagnostic" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-diagnostic into .opencode/skills/jetson-diagnostic/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-diagnostic", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: jetson-diagnostic
GitHub stars: 3.5k
Used in: 1 other repo
Token cost: ~2.7k tokens
SKILL.md length: 1,136 words
Files: 12 (incl. scripts, references)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

Works in 4 steps: Run scripts/snapshot.sh for the… → For a quick human-readable memory line,… → To explain a single tegrastats line the… → …
Tasks that involve GPU and accelerator computing
SKILL.md covers Purpose, When to use, Prerequisites and Available Scripts, plus 7 more sections
Runs Shell scripts from its folder; calls bash

What it does

Jetson Diagnostic is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/nvmap-clients.md`).

It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Tasks that involve GPU and accelerator computing

Example prompts

“/jetson-diagnostic”

Requirements

A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Run scripts/snapshot.sh for 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.

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 files in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
- bash
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Jetson Diagnostic loads about 2.7k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 33 tokens; SKILL.md has 1,136 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~33
When it runs · the whole SKILL.md, loaded when a task matches: ~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~6.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 1,136 words, ~2,703 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-diagnostic/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.

name: jetson-diagnostic
description: Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.
version: 0.0.1
license: Apache-2.0
metadata.author: Jetson Team
metadata.tags: jetson, diagnostic, telemetry
metadata.languages: bash
metadata.data-classification: public

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, or jetson-package and 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.sh for 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_model or sku, variant, l4t_version, and mem_total_gb.
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.
For "which process is using memory" questions, run snapshot.sh and name the leading process as pid <number>, cmd, and its pss_kb / MiB value. If NvMap GPU memory is the relevant signal, also quote gpu_source and the top nvmap.top_clients or gpu_processes entry.

If your agent runtime does not automatically execute helper scripts relative to this skill directory, resolve script paths with the AgentSkills {baseDir} placeholder:

bash

{baseDir}/scripts/snapshot.sh
{baseDir}/scripts/mem_summary.sh

Do 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/nvmap are 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 incomplete nvmap fields.
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 corresponding null, false, or empty fields from the JSON and explain which signal is limited.
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.

Show full SKILL.md (441 more words)Show less

Output contract for `snapshot.sh`

json

{
  "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-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/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 in gpu_processes[*].used_mib.
"nvmap:iovmm-clients" — per-process sizes from /sys/kernel/debug/nvmap/iovmm/clients. Used on the nvgpu stack (Orin family today), where nvidia-smi is a stub that returns [N/A] for every compute/memory 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.
"none" — no authoritative source reachable. Typical when running unprivileged on an nvgpu-stack Jetson (debugfs under /sys/kernel/debug/nvmap needs sudo); rerun with sudo to populate the nvmap fields.

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 loop
jetson-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 defaults
jetson-llm-benchmark — reproducible latency / throughput benchmarks
jetson-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

SKILL.md and 11 other files (scripts, references) in skills/jetson-diagnostic of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
references/nvmap-clients.md
references/tegrastats-fields.md
scripts/common.sh
scripts/detect_jetson.sh
scripts/mem_summary.sh
scripts/runtime_probes.sh
scripts/snapshot.sh
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 67a13c0

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

NVIDIA-AI-IOT/jetson-device-skillsskills/jetson-diagnostic

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TAO Detection KPI Analysis
NVIDIA/skills
Official
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
3.5k GitHub stars~2.7k tokensUpdated yesterday
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NVIDIA AI Platform

Questions about Jetson Diagnostic

What does Jetson Diagnostic do?

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes. Jetson Diagnostic is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

When should I use Jetson Diagnostic?

Jetson Diagnostic fits situations like: tasks that involve GPU and accelerator computing.

How do I install Jetson Diagnostic in Claude Code?

Run `npx skills add NVIDIA/skills --skill jetson-diagnostic -a claude-code`. Or copy the skill folder (skills/jetson-diagnostic in NVIDIA/skills) into .claude/skills/jetson-diagnostic in your project. Claude Code loads it when a task matches its description.

How do I install Jetson Diagnostic in Codex?

Run `npx skills add NVIDIA/skills --skill jetson-diagnostic -a codex`. Or copy the skill folder (skills/jetson-diagnostic in NVIDIA/skills) into .agents/skills/jetson-diagnostic in your project. Codex loads it when a task matches its description.

Can I use Jetson Diagnostic in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill jetson-diagnostic -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jetson-diagnostic, .gemini/skills/jetson-diagnostic, .github/skills/jetson-diagnostic and .opencode/skills/jetson-diagnostic in your project.

What does Jetson Diagnostic need to run?

Going by SKILL.md and its folder, Jetson Diagnostic needs a shell for the scripts in its folder and the command-line tools its instructions call (bash). Our summary lists: A Bash shell.

Does Jetson Diagnostic access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Jetson Diagnostic safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Jetson Diagnostic use?

Jetson Diagnostic is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Jetson Diagnostic use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.

What are the alternatives to Jetson Diagnostic?

Skills that share tags, products or a category with Jetson Diagnostic: Fla Triton To Gluon (fla-org/flash-linear-attention, 5.8k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars), DGX Spark Training Gotchas (wshobson/agents, 40k stars) and Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Diagnostic?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Jetson Diagnostic

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Jetson Diagnostic

Purpose

When to use

Prerequisites

Available Scripts

Instructions

Reporting guidance

Limitations

Error handling

Output contract for snapshot.sh

Safety

Cross-platform behavior

Files

Used in 1 other repository

Compare with similar skills

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Questions about Jetson Diagnostic

Output contract for `snapshot.sh`