When developing AI on Jetson, a coding agent can help read device information, organize diagnostic results, prepare packages, and run tests. However, the right approach depends on the Jetson model, JetPack/L4T version, available memory, and runtime environment. Jetson Agent Skills organizes NVIDIA's Jetson workflows into agent-readable skills and helper scripts, allowing recommendations to reflect the current device rather than relying only on generic commands.

This tutorial brings together Jetson AI Lab's Jetson Agent Skills guide and AI-assisted development on NVIDIA Jetson tutorial. We first explain what the skills do, then connect to Jetson using Cursor Remote-SSH, install Device Skills, and get started with diagnostic and planning prompts.

What Are Jetson Agent Skills?

Agent Skills provide workflows and tool instructions for coding agents. Jetson Agent Skills can help inspect the device model, system version, memory, power configuration, and service status, then support package selection, inference deployment, or performance testing based on the device's conditions.

They do not replace the coding agent, NVIDIA documentation, or developer judgment. Always confirm the target device and check command results. This article focuses on Device Skills for a Jetson that is already running.

The Jetson device family shown on the Jetson AI Lab website

Jetson device family illustration from Jetson AI Lab.

Understand Device Skills vs. BSP Skills First

  • Jetson Device Skills: For a running Jetson. These support application development, system diagnostics, memory checks, package selection, and model inference. This is the collection we install here.
  • Jetson BSP Skills: For host-workstation platform tasks involving board support packages, custom carrier boards, pinmux, PCIe, camera configuration, building, and flashing. You do not need to install them first for ordinary application development.

If you simply want to run a model or inspect the system on an Orin or Thor, start with Device Skills. When modifying low-level hardware settings or building a custom platform, consult the Jetson BSP Skills project and the relevant hardware documentation.

A Recommended Workflow for Agent-assisted Development

  1. Inspect: Read the Jetson model, JetPack/L4T version, available memory, power mode, and relevant services to establish its current state.
  2. Plan: Compare packages, models, or configuration options against the device and task requirements. List the proposed commands and their possible effects.
  3. Review, then execute: Understand operations such as package installation, disabling services, changing power settings, and rebooting before deciding whether to run them.
  4. Verify: Use actual outputs, memory measurements, or performance tests to confirm that changes work. Do not assume success simply because the agent says it is done.

Jetson AI Lab's remote-development demonstration uses Codex with a Jetson Orin Nano, covering connection, skill installation, and device inspection. This article instead provides a Cursor example. Configuration interfaces and skill installation paths can differ between coding agents; check the version you are using.

Jetson AI Lab's Codex remote-development tutorial

Cover image from Jetson AI Lab's Codex remote-development tutorial. The following steps demonstrate Device Skills with Cursor. Source: AI-assisted development on NVIDIA Jetson.

Before You Start: Prepare Jetson and Your Development Environment

  • An NVIDIA Jetson device, such as an Orin-series device or Jetson Thor, that has completed initial setup, is running, and is connected to a network.
  • A Windows, macOS, or Linux development computer that can connect to Jetson over SSH.
  • Cursor installed, with Remote-SSH available. The commands in this article run in the remote terminal connected to Jetson.
  • Access from Jetson to GitHub to download the project. If you choose individual skill installation through the NVIDIA Agent Skills catalog, you also need Node.js and npm to provide npx.

Step 1: Connect to Jetson with Remote-SSH

  1. Open Cursor on your development computer. Press Ctrl + Shift + P, or Cmd + Shift + P on macOS, to open the command palette.
  2. Enter Remote-SSH: Connect to Host... and select that command.
  3. Enter Jetson's SSH address, for example ssh jetson@192.168.1.42. Replace the username and IP address with your own.
  4. On the first connection, select Linux, verify the host key as prompted, and sign in. If a host key is unfamiliar or has changed, confirm the device's identity before continuing.
  5. After connecting, confirm that Cursor shows the SSH host and open the integrated terminal. Before running the remaining commands, make sure that the terminal is on Jetson rather than your development computer.

A laptop connecting to Jetson over SSH

Conceptual illustration of Cursor Remote-SSH connecting to Jetson; not an actual software screenshot.

Step 2: Choose an Installation Method

Jetson AI Lab provides two entry points: select individual skills from the NVIDIA Agent Skills catalog, or install the full collection from the NVIDIA Jetson Device Skills project.

Install the skills in the environment where the agent will use them. For this Remote-SSH example, that means Jetson's remote terminal.

Option A: Install an Individual Skill from the NVIDIA Agent Skills Catalog

To start with read-only device diagnostics, run this command in the Jetson terminal to install jetson-diagnostic for Cursor:

npx skills@latest add nvidia/skills --skill jetson-diagnostic --agent cursor --yes

If you are unsure which skill to use, browse the NVIDIA catalog first. The CLI provides interactive options for the installation target:

npx skills@latest add nvidia/skills --list

Installing a single skill is a useful way to get started. For memory auditing, headless mode, package selection, or inference workflows, check the NVIDIA Agent Skills catalog for current skill names.

Option B: Install the Full Jetson Device Skills Collection

Run the following commands in Jetson's remote terminal:

git clone https://github.com/NVIDIA-AI-IOT/jetson-device-skills.git cd jetson-device-skills ./install.sh --targets cursor

The installation script places the skills in a directory Cursor can read. To use the script's default installation targets across supported agents, run ./install.sh without arguments. To install for selected agents, consult the latest instructions in the official Jetson Device Skills project.

After installation, restart your Cursor Agent session and confirm that the skills are available in a location the agent can read.

Installing Jetson Device Skills in an agent-readable directory

Conceptual illustration of installing skill files in an agent-readable location on Jetson.

Step 3: Run Read-only Diagnostics Before Planning Changes

Open AI Chat in Cursor's sidebar. A common shortcut is Ctrl + L, or Cmd + L on macOS. First ask the agent to inspect the device without changing the system:

Read this Jetson's model, JetPack/L4T version, available memory, GPU utilization, power mode, and relevant services. List the inspection commands you use and their results, and identify your data sources. For this round, perform read-only checks only. Do not install packages or change system settings.

An AI agent organizing Jetson diagnostic information

Conceptual illustration of an agent summarizing device diagnostics; not an actual operation screenshot.

After confirming the device information, ask for a plan that lists commands, trade-offs, and expected effects before execution:

Based on the device model, JetPack/L4T version, memory, and service status you just inspected, propose feasible options for switching to headless mode or deploying an LLM/VLM. First explain your reasoning, the required commands, possible effects, and how to revert the changes. Do not disable services, install packages, change settings, or reboot until I confirm.

Device support and actual performance vary with the model, JetPack/L4T version, model size, quantization method, and runtime environment. For inference options such as vLLM or llama.cpp, check compatibility first, then measure memory usage and performance on the target Jetson. Avoid copying another model's configuration without checking it.

Key Takeaways

  • Start with Jetson Device Skills for ordinary application development. BSP Skills cover host-side tasks such as custom platforms and flashing.
  • Install an individual skill through the NVIDIA Agent Skills catalog, or install the complete Device Skills collection from the official project.
  • When using Remote-SSH, make sure the terminal and skill installation location correspond to the intended Jetson and agent.
  • Follow the sequence inspect, plan, review and execute, then verify. Understand the effects before making system changes.

Jetson Agent Skills brings platform knowledge into a coding agent's workflow, helping developers experiment based on the actual device state. Always verify results using real Jetson outputs and tests, and let the developer decide whether to adopt a change.

English translation of CAVEDU's original Traditional Chinese article, published November 9, 2026. The link opens the CAVEDU website outside this platform. Original technical commands, reference links, and image credits are retained.