Skip to content
jetson-set-target logo

Set Target Platform

jetson-set-target

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.

nvidia/skills1Kinstalls3.5Kstars

SKILL.md

Full skill instructions

Set Target Platform

Overview

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.

When to invoke

  • The user asks to switch / change / activate a different target platform among profiles that already exist.
  • A downstream skill refused with "no active target" and target-platform/ already contains one or more profile YAMLs.
  • The user names a target by filename or product, and that profile YAML is already on disk.

If target-platform/ is missing, empty, or contains only active_target.yml, stop and tell the user to run jetson-init-target first.

Procedure

Survey the profiles directory

  1. Read 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).
  2. List target-platform/​*.yaml, excluding active_target.yml itself.
SituationAction
No profile YAMLs existRefuse. Tell the user to run jetson-init-target to author one.
Exactly one profile, already activeTell the user it is already active; no change needed. Stop.
Exactly one profile, not activeConfirm with the user, then jump to the "Update the active pointer" step with that profile selected.
Multiple profilesProceed to the "Show the list" step.

Show the list

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.conf

If 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 for selection

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.

Update the active pointer

Edit target-platform/​active_target.yml:

  • Set active: <chosen-filename>.yaml.
  • Preserve the header comment block verbatim — only modify the active: line.
  • If the file does not exist (it can be missing if the user hand-set up target-platform/ without the bootstrap step), create it from ../​../​references/​active_target_template.yaml and then set active:.

Confirm

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.

Prerequisites

  • 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.

Limitations

  • Edits only the active: line in active_target.yml; never writes profile YAMLs.
  • Refuses when no profile YAMLs exist — routes the user to jetson-init-target instead.
  • Does not resolve fuzzy product names beyond simple substring match; ambiguous selections are re-prompted.

Troubleshooting

  • "no profiles found" refusal — the directory has no *.yaml other than the pointer; run /​jetson-init-target first.
  • Pointer rewritten to a missing profile — should not happen because the chosen filename is verified before writing; if seen, hand-edit active_target.yml back to a valid filename or rerun with the right selection.
  • Active pointer reads NA — legitimate "no selection" state; pick any listed profile and the pointer will be set.

Gotchas

  • No authoring here. If the user names a target whose profile YAML is not on disk, refuse and route them to jetson-init-target. Do not silently fall through to authoring.
  • Don't edit active_target.yml to point at a missing profile. Verify the chosen filename exists in target-platform/ before writing.
  • Preserve active_target.yml's comment block when editing — only the active: line should change.
  • Exclude 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.
  • One-profile shortcut. If only one profile exists, do not skip user confirmation — still confirm before flipping the pointer.

References

More skills from nvidia

cudaq-guide logo
nvidia/skills

cudaq-guide

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

3.5K 2.5K
View
data-designer logo
nvidia/skills

data-designer

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

3.5K 2.4K
View
deepstream-dev logo
nvidia/skills

deepstream-dev

NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.

3.5K 2.4K
View
rag-eval logo
nvidia/skills

rag-eval

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

3.5K 2.3K
View
nemo-retriever logo
nvidia/skills

nemo-retriever

Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (`.jpg` `.png` `.tiff`), Office (`.docx` `.pptx`), text (`.html` `.txt`), audio (`.mp3` `.wav` `.m4a`), or video (`.mp4` `.mov`). Prefer this over native Read / Grep for multi-file or non-PDF corpora. Not for: editing files, web browsing, single-file plain-text lookups, fine-tuning.

3.5K 2.3K
View

Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.

3.5K 2.3K
View

Use this skill to bring any vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.

3.5K 2.3K
View

Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

3.5K 2.3K
View

Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

3.5K 2.2K
View

Use only to generate or update a governance skill card for a specified existing agent skill directory. Do not use for explaining, listing, comparing, or discussing skill capabilities.

3.5K 2.2K
View

Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.

3.5K 2.2K
View
rag-perf logo
nvidia/skills

rag-perf

Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).

3.5K 2.1K
View

Popular AI tools

Kaiber logo
Video

Kaiber

Generate, edit, and beat-sync AI video with leading models in one workspace.

Paid
View
Vimcal logo
Productivity

Vimcal

The world's fastest calendar for remote work

Free
View

Transform Your Design with AI Designer by ImgCreator.ai

Freemium
View
Akool AI logo
Content & writing

Akool AI

Revolutionizing Video Production with AI-Powered Creativity

Paid
View

Extend an image past the frame and let AI fill the new aspect ratio.

Freemium
View
StarByFace logo
Security

StarByFace

Discover your celebrity doppelgänger with StarByFace!

Free
View
C

ChainClarity explains 700+ crypto whitepapers in plain English, with layered summaries, comparisons, research tools, alerts, and a $4.99 Pro plan.

Freemium
View
Opus Clip logo
Coding & apps

Opus Clip

Opus.ai: Revolutionize Your Web Experience

Free
View