Switch the active Jetson target-platform pointer to an existing profile YAML.
$ npx skills add NVIDIA/skills --skill jetson-set-target -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-set-target --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/jetson-set-target .claude/skills/jetson-set-target && rm -rf skills-srcUse ~/.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/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .claude/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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 install https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-targetType 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.
$ npx skills add NVIDIA/skills --skill jetson-set-target -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-set-target --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/jetson-set-target .agents/skills/jetson-set-target && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .agents/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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.
$ npx skills add NVIDIA/skills --skill jetson-set-target -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-set-target --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/jetson-set-target .cursor/skills/jetson-set-target && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .cursor/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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 https://github.com/NVIDIA/skills.git --path skills/jetson-set-target--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill jetson-set-target -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-set-target --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/jetson-set-target .gemini/skills/jetson-set-target && rm -rf skills-srcUse ~/.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/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .gemini/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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.
$ gh skill install NVIDIA/skills jetson-set-targetInstalls 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).
$ npx skills add NVIDIA/skills --skill jetson-set-target -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/jetson-set-target .github/skills/jetson-set-target && rm -rf skills-srcUse ~/.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/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .github/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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.
$ npx skills add NVIDIA/skills --skill jetson-set-target -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills jetson-set-target --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/jetson-set-target .opencode/skills/jetson-set-target && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "jetson-set-target" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-set-target into .opencode/skills/jetson-set-target/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-set-target", 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.
jetson-set-targetSwitch the active Jetson target-platform pointer to an existing profile YAML.
Jetson Set Target is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Switch the active Jetson target-platform pointer to an existing profile YAML. Use before customize/build/flash to change target; not for authoring profiles — use jetson-init-target instead.
Its SKILL.md is about 1.7k 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. 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.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Jetson Set Target loads about 1.7k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 764 words of instructions outside code blocks.
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.
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); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 764 words, ~1,727 tokens.
.claude/skills/jetson-set-target/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.This skill is the switcher side of the target-platform contract in
../../context/target-platform-contract.md. It only
edits target-platform/active_target.yml; it never authors a new
profile YAML. To author a new profile, use the sibling skill
jetson-init-target.
Single output: target-platform/active_target.yml updated to point at
the user's chosen existing profile.
target-platform/ already contains one or more profile YAMLs.If target-platform/ is missing, empty, or contains only
active_target.yml, stop and tell the user to run jetson-init-target
first.
target-platform/active_target.yml (treat a missing file as
active: NA for the purposes of this skill — the file will be
created in the "Update the active pointer" step if needed, preserving the template header).target-platform/*.yaml, excluding active_target.yml
itself.| Situation | Action |
|---|---|
| No profile YAMLs exist | Refuse. Tell the user to run jetson-init-target to author one. |
| Exactly one profile, already active | Tell the user it is already active; no change needed. Stop. |
| Exactly one profile, not active | Confirm with the user, then jump to the "Update the active pointer" step with that profile selected. |
| Multiple profiles | Proceed to the "Show the list" step. |
Print the profiles in alphabetical order, numbered. Mark the current
active selection with (active) so the user knows what they're
switching from. For each profile, parse its YAML and show a one-line
summary so the user can identify it without opening files: the
reference_devkit.name (or custom_carrier.name if present) plus the
flash_config.
Sample shape:
Profiles in target-platform/:
1) acme-vision-x1.yaml — Acme Vision X1 (custom carrier on AGX Thor T5000) — acme-vision-x1.conf
2) jetson-agx-orin-32gb.yaml — Jetson AGX Orin 32GB — jetson-agx-orin-devkit.conf
3) jetson-agx-thor-devkit.yaml — Jetson AGX Thor T5000 — jetson-agx-thor-devkit.conf (active)
4) jetson-orin-nano-8gb.yaml — Jetson Orin Nano 8GB — jetson-orin-nano-devkit.confIf a profile YAML fails to parse or is missing the expected fields,
show the bare filename with a (unparseable — fix or remove) marker
and skip to the next; do not refuse the whole skill over one bad file.
Prompt:
Pick a number, the bare filename, the product name (substring match), or
cancel.
Resolve to a single profile filename. If the user picks the currently active profile, tell them so and stop without rewriting the pointer. If ambiguous, list candidates and ask again.
Edit target-platform/active_target.yml:
active: <chosen-filename>.yaml.active: line.target-platform/ without the bootstrap step), create it from
../../references/active_target_template.yaml
and then set active:.Print a summary: the previous active value, the new active value, the
chosen profile's reference_devkit.name (and custom_carrier.name if
present), and a reminder that downstream skills (jetson-customize-clocks,
jetson-customize-nvpmodel, jetson-customize-fan,
jetson-optimize-memory) will now resolve to this target.
If a downstream skill triggered this run, tell the user to re-issue their original request; do not silently re-trigger it.
target-platform/ exists and contains at least one *.yaml profile
(excluding active_target.yml).target-platform/active_target.yml is present, or the template at
../../references/active_target_template.yaml is available so this
skill can recreate the pointer.active: line in active_target.yml; never writes
profile YAMLs.jetson-init-target instead.*.yaml other than the pointer; run /jetson-init-target first.active_target.yml back to a valid filename or rerun with the right
selection.NA — legitimate "no selection" state; pick
any listed profile and the pointer will be set.jetson-init-target. Do
not silently fall through to authoring.active_target.yml to point at a missing profile.
Verify the chosen filename exists in target-platform/ before
writing.active_target.yml's comment block when editing —
only the active: line should change.active_target.yml from the profile list. It lives in
the same directory but is the pointer, not a profile.active: NA is the legitimate "no selection" state. Treat it
the same as a fresh pointer — don't refuse, just show the list and
let the user pick.../../context/target-platform-contract.md — the contract this skill writes to.../../references/active_target_template.yaml — schema starter for the active-target pointer (used to recreate it if the file is missing).../jetson-init-target/SKILL.md — sibling skill: author a new profile YAML.© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 4 other files in skills/jetson-set-target of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Jetson Set Target 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Jetson Set Target this skillNVIDIA/skills | 3.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Fla Triton To Gluonfla-org/flash-linear-attention | 5.8k | — | ~4.2k | Automated safety check: Pass | MIT | |
| DGX Spark Memory and Thermal Opswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| DGX Spark Training Gotchaswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Megatron-LM on SLURMNVIDIA/Megatron-LM | 18k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| OpenVLA-OFT Fine-TuningOrchestra-Research/AI-Research-SKILLs | 13k | 1 repos | ~3.7k | Automated safety check: Pass | MIT |
fla-org/flash-linear-attention
Workflow for porting an existing Triton kernel in fla/ops/ to Gluon (triton.experimental.gluon) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA…
wshobson/agents
Plans memory headroom, works through out-of-memory failures and watches temperature and power during long ML training jobs on NVIDIA DGX Spark.
wshobson/agents
Preflight checks and diagnosis for ten known failure modes of ML training on NVIDIA DGX Spark's GB10, spanning launch errors, memory, thermals, bandwidth and precision.
NVIDIA/Megatron-LM
Shows how to launch distributed Megatron-LM training on a SLURM cluster: sbatch skeleton, torch.distributed.run setup, CUDA_DEVICE_MAX_CONNECTIONS rules and failure diagnosis.
Orchestra-Research/AI-Research-SKILLs
Fine-tunes and evaluates OpenVLA-OFT and OFT+ robot policies with LoRA and continuous action heads on LIBERO simulation and ALOHA real-robot setups.
Orchestra-Research/AI-Research-SKILLs
Sets up and runs NVIDIA Cosmos Policy evaluations on the LIBERO and RoboCasa simulators, including headless GPU rendering and inference latency profiling.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
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.
Works with
Categories
Switch the active Jetson target-platform pointer to an existing profile YAML. Jetson Set Target is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Switch the active Jetson target-platform pointer to an existing profile YAML.
Jetson Set Target fits situations like: tasks that involve GPU and accelerator computing.
Run `npx skills add NVIDIA/skills --skill jetson-set-target -a claude-code`. Or copy the skill folder (skills/jetson-set-target in NVIDIA/skills) into .claude/skills/jetson-set-target in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-set-target -a codex`. Or copy the skill folder (skills/jetson-set-target in NVIDIA/skills) into .agents/skills/jetson-set-target in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill jetson-set-target -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-set-target, .gemini/skills/jetson-set-target, .github/skills/jetson-set-target and .opencode/skills/jetson-set-target in your project.
SKILL.md names no scripts, command-line tools or credentials: Jetson Set Target is instructions for the agent only.
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.
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. Review the folder before installing.
Jetson Set Target 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.
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Jetson Set Target: 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.
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.
