NVIDIA/skills is a catalogue of portable instruction sets that teach coding agents how to use NVIDIA software for robotics, simulation, CUDA, retrieval-augmented generation, and related workflows. Developers install these skills in agents such as Claude Code or Codex, while the catalogue mirrors skills maintained in separate NVIDIA product repositories.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/skills --skill jetson-video-setupgit clone --depth 1 https://github.com/NVIDIA/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/skills/jetson-video-setup)<a href="https://agentmods.dev/skills/nvidia/skills/jetson-video-setup"><img src="https://agentmods.dev/badge/skills/nvidia/skills/jetson-video-setup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/skills/jetson-video-setup"><img src="https://agentmods.dev/badge/skills/nvidia/skills/jetson-video-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.02082 |
| Opus 5.5 | $0.00029 | $0.00833 |
| Sonnet 5.5 | $0.00014 | $0.00416 |
| Haiku 4.5 | $0.00007 | $0.00208 |
Grade B, and why
jetson-video-setup scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
packages. Apply the reviewed command with noninteractive `sudo -n`; if that How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jetson Video Setup
Purpose
Inspect, install, and verify the native NVIDIA Video Codec SDK and PyNvVideoCodec on a live Jetson. Setup owns product installation and readiness. Use the sibling video skills for codec support, encoder configuration, performance measurement, and application pipelines.
Use the standard package manager, Python environment tools, and installed NVIDIA samples directly, then return a concise readiness result.
Read before acting
- Read setup-workflow.md for inspection and readiness checks.
- Read setup-install.md completely once before an APT, venv, or pip change.
- Read setup-output-contract.md for the concise human-readable result to return.
- Read video-content.md only when a request also supplies or asks to retrieve media.
Select the product
Resolve the requested product before touching the target:
- “Video Codec SDK”, “VC SDK”, “native SDK”, or
nvidia-video-codec-sdkselects the native product. - “PyNvVideoCodec”, “PyNv”, “PySDK”, “Python SDK”, or an explicitly Python interface selects PyNvVideoCodec.
- Select both only when the user asks for both.
A genuinely bare “video SDK” setup or readiness request is ambiguous. Ask whether the user wants native Video Codec SDK, PyNvVideoCodec, or both, then stop. The word “report” does not resolve that ambiguity.
Keep native and Python work independent. A failure on one surface must not erase a successful result from the other.
Workflow
- Confirm commands would run on a Jetson. Inspect
/etc/nv_tegra_release,/etc/os-release, and the requested GPU ordinal. On another host, provide guidance only and make no readiness claim. - Inspect only the selected product with the direct commands in setup-workflow.md. For native, identify the installed package, package-owned Samples tree, CUDA toolkit, and build tools. For Python, use the exact interpreter supplied by the user or created during this request and inspect its installed distribution and loaded module. Never scan the filesystem for virtual environments.
- If inspection is all the user requested, report what is installed and stop.
Package or import presence is
installed, notready. - If the selected product is missing and the user asked to install or repair it, follow that install reference. Show the exact package or pip commands before mutation. Install and verify system prerequisites before creating a final Python environment path. Change only the selected product and its missing prerequisites.
- After installation, repeat the direct inspection. Then run the installed release's official one-frame encode followed by independent decode as described in setup-workflow.md.
- Report each selected product separately. Use
readyonly after its official encode and independent decode pass all observable checks. Otherwise reportinstalled,blocked, orfailed, name the failing command or missing prerequisite, and give one concrete next action.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +29 tokens per session f7041496821c
- 2mo ago First seen · 182 lines · 43 tokens per session scan B db947fd250b4
jetson-video-setup is a skill published in the GitHub repository NVIDIA/skills (3,873 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 2,082 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
jetson-video-setup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| jetson-video-setupNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | B | 72 | Claude Code, Codex |
| desktop-and-local-ipc-securitymakifbaysal/tasktrooper | Skill | 120 | 16d ago | Apache-2.0 | A | 78 | Claude Code, Codex |
| spark-environment-setupwshobson/agents | Skill | 42k | yesterday | MIT | A | 76 | Claude Code |
| spark-training-gotchaswshobson/agents | Skill | 42k | yesterday | MIT | A | 63 | Claude Code |
| technical-troubleshootingstrands-agents/samples | Skill | 925 | yesterday | Apache-2.0 | A | 38 | Claude Code |
| screen-mockup-htmlmakifbaysal/tasktrooper | Skill | 120 | 16d ago | Apache-2.0 | A | 63 | Claude Code, Codex |
| responsive-layoutmakifbaysal/tasktrooper | Skill | 120 | 16d ago | Apache-2.0 | A | 35 | Claude Code, Codex |