Official agent skill

Jetson Init Image

Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile.

OfficialApache-2.0Auto-check: notesAI & LLM Engineering

Install Jetson Init Image

skills CLI

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

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

GitHub CLI

$ gh skill install NVIDIA/skills jetson-init-image --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-init-image .claude/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .claude/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image

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-init-image -a codex

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

GitHub CLI

$ gh skill install NVIDIA/skills jetson-init-image --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-init-image .agents/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .agents/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image -a cursor

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

GitHub CLI

$ gh skill install NVIDIA/skills jetson-init-image --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-init-image .cursor/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .cursor/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image

--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-init-image -a gemini-cli

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

GitHub CLI

$ gh skill install NVIDIA/skills jetson-init-image --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-init-image .gemini/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .gemini/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image

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-init-image -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-init-image .github/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .github/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image -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-init-image --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-init-image .opencode/skills/jetson-init-image && 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-init-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-init-image into .opencode/skills/jetson-init-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-init-image", 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-init-image
GitHub stars: 3.5k
Used in: 1 other repo
Token cost: ~1.8k tokens
SKILL.md length: 708 words
Files: 5
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile.

Works in 3 steps: Do not extract over it unless the user… → Derive the on-disk version from… → Verify the installed GPU stack against…
Tasks that involve GPU and accelerator computing
SKILL.md covers When to invoke, Procedure, Finish and Purpose, plus 4 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jetson Init Image is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile. Use after jetson-init-target; not for source-tree setup.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).

It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform and Linux. 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-init-image”

Workflow steps

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

Do not extract over it unless the user explicitly requested
Derive the on-disk version from Linux_for_Tegra/nv_tegra_release.
Verify the installed GPU stack against the platform-derived

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
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and yaml).
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 Init Image loads about 1.8k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 708 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~52
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteRuns commands with sudoSKILL.md:121

sudo tar xpjf "$ROOTFS_TARBALL" -C "$ROOT/Linux_for_Tegra/rootfs"

NoteRuns commands with sudoSKILL.md:125
```
sudo ./apply_binaries.sh --openrm
```
NoteRuns commands with sudoSKILL.md:127
```
sudo ./apply_binaries.sh
```

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 708 words, ~1,819 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-init-image/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: jetson-init-image
description: Extract Jetson Linux + sample-rootfs tarballs and run apply_binaries.sh for the active target, then record bsp_image in the profile. Use after jetson-init-target; not for source-tree setup.
version: 0.0.1
license: Apache-2.0
metadata.data-classification: public
metadata.author: Jetson Team
metadata.tags: bsp, image, bootstrap
metadata.domain: meta

Initialize BSP Image

Output is only bsp_image: in the active profile: derived version plus root_path only when overriding <workspace>/Image.

When to invoke

The user asks to extract, prepare, or initialize the BSP image.
A downstream skill reports missing <bsp_image.root_path>/Linux_for_Tegra/.
The active profile has no bsp_image: block yet.

Procedure

Resolve target and image path

Resolve the active profile per ../../context/target-platform-contract.md. Refuse if there is no active profile or reference_devkit: is missing.

<workspace> is the parent of the active profile's target-platform/ directory. <bsp_image.root_path> defaults to <workspace>/Image.

Profile state	Action
`bsp_image.root_path` exists	Use it without prompting.
`bsp_image:` exists without `root_path`	Use `<workspace>/Image`.
No `bsp_image:` block	Ask once: Enter for default, or absolute override path.

For an override, validate that the closest existing parent is writable. Omit root_path when the default is used.

Determine GPU stack

Use the shared GPU-driver invariant from ../../context/target-platform-contract.md. Derive the expected stack from reference_devkit.module.id and the catalogue:

Chip family	Module IDs	Stack	`apply_binaries.sh` flag
T234 / Orin	`p3701`, `p3767`	nvgpu	none
T264 and later / Thor+	`p3834`	OpenRM	`--openrm`

Refuse unknown module IDs. The --openrm flag is only valid on BSP releases that ship the OpenRM stack; if the active BSP doesn't expose the flag, omit it regardless of what the target wants.

Reuse or extract

If <bsp_image.root_path>/Linux_for_Tegra/ already exists:

Do not extract over it unless the user explicitly requested re-extraction and accepted the overwrite risk.
Derive the on-disk version from Linux_for_Tegra/nv_tegra_release. Ask before replacing a different recorded bsp_image.version.
Verify the installed GPU stack against the platform-derived expectation when possible. Detection precedence (first probe that yields a definitive answer wins):
1. Linux_for_Tegra/rootfs/etc/nv_tegra_release carries an INSTALL_TYPE= token on BSP releases that expose it (newer lines). Read and compare directly.
2. Otherwise, find Linux_for_Tegra -name nvgpu.ko: present → nvgpu, absent → OpenRM.
3. If the chip family has only ever shipped one stack (e.g. T234 / Orin is always nvgpu in current BSPs), fall back to the catalogue-derived expectation without disk probing.

If the installed stack conflicts with the active target, refuse and ask the user to re-extract with the correct stack or fix the target profile. Otherwise skip extraction and update the profile.

Locate tarballs

When extraction is needed, search:

<bsp_image.root_path>/
<workspace>/
current working directory

Prompt for absolute paths for anything missing. Required filenames:

Jetson_Linux_R<ver>_aarch64.tbz2
Tegra_Linux_Sample-Root-Filesystem_R<ver>_aarch64.tbz2

Both filenames must contain the same R<ver> token. Refuse mismatches and record <ver> as bsp_image.version. Do not download tarballs.

Extract and apply binaries

Use absolute tarball paths; they may live outside <bsp_image.root_path>.

bash

ROOT="<bsp_image.root_path>"
BSP_TARBALL="<absolute path to Jetson_Linux_R<ver>_aarch64.tbz2>"
ROOTFS_TARBALL="<absolute path to Tegra_Linux_Sample-Root-Filesystem_R<ver>_aarch64.tbz2>"

mkdir -p "$ROOT"
tar xjf "$BSP_TARBALL" -C "$ROOT"
sudo tar xpjf "$ROOTFS_TARBALL" -C "$ROOT/Linux_for_Tegra/rootfs"

cd "$ROOT/Linux_for_Tegra"
if [ "$GPU_STACK" = "openrm" ]; then
  sudo ./apply_binaries.sh --openrm
else
  sudo ./apply_binaries.sh
fi

Set GPU_STACK from the "Determine GPU stack" step above. Abort on the first failing command and surface the failed command.

Show full SKILL.md (279 more words)Show less

Update the active profile

Persist the resolved BSP image metadata in the active target profile so later skills can find the BSP without re-prompting. Preserve existing blocks, comments, and quoted SKU values; use a round-tripping YAML writer such as ruamel.yaml.

yaml

bsp_image:
  root_path: <absolute override path>  # omit for <workspace>/Image
  version: "<derived version>"

Rules:

Same version and same root path: no rewrite.
Different version: ask before updating.
Different recorded root_path: refuse automatic rewrite.
Always quote version.

Finish

Report the image path, extracted vs reused state, GPU stack, derived version, and profile update status. Then suggest /jetson-init-source.

Purpose

Materialize Linux_for_Tegra/ on disk by extracting the right Jetson Linux + sample-rootfs tarballs and running apply_binaries.sh with the GPU-stack flag derived from the active target (nvgpu for T234, OpenRM for T264+). Then commit the derived BSP version into the profile's bsp_image: block.

Prerequisites

Active target profile resolved per ../../context/target-platform-contract.md.
Jetson Linux BSP tarball and matching sample-rootfs tarball staged on disk (e.g. by /jetson-download-bsp or hand-placed).
Write access to the workspace Image/ root (or the override bsp_image.root_path).
sudo available for apply_binaries.sh.

Limitations

Writes only the bsp_image: block; source tree, documents, and carrier profile are owned by sibling skills.
Refuses to overwrite an existing Linux_for_Tegra/ without explicit user direction.
Does not download tarballs; rely on /jetson-download-bsp or hand-stage the inputs.

Troubleshooting

apply_binaries.sh exits non-zero — re-read its console output; most failures are missing sudo, missing rootfs tarball, or wrong GPU stack flag for the SoC generation.
nv_tegra_release absent after extract — extraction stopped early; verify tarball integrity and rerun.
Recorded version disagrees with the tarball filename — the tarball was renamed; trust the value parsed from Linux_for_Tegra/nv_tegra_release over filenames.
Different recorded root_path already in profile — refuse and ask the user to confirm before overwriting.

References

Files

SKILL.md and 4 other files in skills/jetson-init-image of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
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.

majiayu000/claude-skill-registryskills/ai-llm/jetson-init-image

Compare with similar skills

Jetson Init Image next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Jetson Init Image compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Jetson Init Image this skillNVIDIA/skills	3.5k	1 repos	~1.8k	Automated safety check: Notes	Apache-2.0	today
Graphsignalgraphsignal/graphsignal	257	—	~6.2k	Automated safety check: Pass	Apache-2.0	10 days ago
LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS	911	—	~2.8k	Automated safety check: Pass	None	3 days ago
TensorRT-LLM InferenceOrchestra-Research/AI-Research-SKILLs	13k	5 repos	~1.3k	Automated safety check: Pass	MIT	3 mo ago
Cv DeployLMIXR/CV_Deployment_skill	146	—	~547	Automated safety check: Pass	None	9 days ago
Megatron-Core LLM TrainingOrchestra-Research/AI-Research-SKILLs	13k	3 repos	~2.4k	Automated safety check: Pass	MIT	3 mo ago

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Works with

Questions about Jetson Init Image

What does Jetson Init Image do?

Extract Jetson Linux + sample-rootfs tarballs and run applybinaries.sh for the active target, then record bspimage in the profile. Jetson Init Image is an agent skill from NVIDIA/skills, published by the product's own GitHub organization.sh for the active target, then record bspimage in the profile.

When should I use Jetson Init Image?

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

How do I install Jetson Init Image in Claude Code?

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

How do I install Jetson Init Image in Codex?

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

Can I use Jetson Init Image 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-init-image -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-init-image, .gemini/skills/jetson-init-image, .github/skills/jetson-init-image and .opencode/skills/jetson-init-image in your project.

What does Jetson Init Image need to run?

SKILL.md names no scripts, command-line tools or credentials: Jetson Init Image is instructions for the agent only.

Does Jetson Init Image 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 Init Image safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Jetson Init Image use?

Jetson Init Image 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 Init Image use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Jetson Init Image?

Skills that share tags, products or a category with Jetson Init Image: Graphsignal (graphsignal/graphsignal, 257 stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 911 stars), TensorRT-LLM Inference (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Cv Deploy (LMIXR/CV_Deployment_skill, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Init Image?

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 Init Image

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Initialize BSP Image

When to invoke

Procedure

Resolve target and image path

Determine GPU stack

Reuse or extract

Locate tarballs

Extract and apply binaries

Update the active profile

Finish

Purpose

Prerequisites

Limitations

Troubleshooting

References

Files

Used in 1 other repository

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