Triton Skill
slowlyC/agent-gpu-skills
Write, debug, and optimize Triton and Gluon GPU kernels from local upstream tutorials, production kernels, language definitions, and compiler source.
A skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…
$ npx skills add NVIDIA/skills --skill jetson-video-setup -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-video-setup --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-video-setup .claude/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .claude/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setupType 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-video-setup -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-video-setup --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-video-setup .agents/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .agents/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setup -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-video-setup --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-video-setup .cursor/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .cursor/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setup--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-video-setup -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-video-setup --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-video-setup .gemini/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .gemini/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setupInstalls 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-video-setup -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-video-setup .github/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .github/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setup -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-video-setup --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-video-setup .opencode/skills/jetson-video-setup && 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-video-setup" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-video-setup into .opencode/skills/jetson-video-setup/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-video-setup", 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-video-setupA skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…
Jetson Video Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/setup-install.md`).
It sits in AI & LLM Engineering, covering GPU and accelerator computing and QA and bug reports. It works with NVIDIA AI Platform, CUDA and Python. 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.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0e0d506. 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.
Shell commands in SKILL.md call:
apt-getFrom 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 Video Setup loads about 2.4k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 1,231 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 noted patterns worth knowing about, such as sudo or a known installer.
he reviewed command with noninteractive `sudo -n`; if thatAutomated 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 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 1,231 words, ~2,406 tokens.
.claude/skills/jetson-video-setup/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.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.
Resolve the requested product before touching the target:
nvidia-video-codec-sdk selects the native product.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.
/etc/nv_tegra_release,
/etc/os-release, and the requested GPU ordinal. On another host, provide
guidance only and make no readiness claim.installed, not ready.ready only after its official
encode and independent decode pass all observable checks. Otherwise report
installed, blocked, or failed, name the failing command or missing
prerequisite, and give one concrete next action.When another video skill asks only for readiness, perform steps 1 and 2 and return the exact package/Samples root or Python interpreter/package path. Do not run the setup smoke test if that consumer will immediately run its own authenticated operation.
Use this interpreter precedence: an explicit user path, an exact path already
established in the current conversation, then the conventional profile path.
The conventional smoke interpreter is $HOME/.venvs/nvcodec/bin/python; the
full-samples interpreter is $HOME/.venvs/nvcodec-full/bin/python. Checking
one of these exact paths is not a filesystem scan. Never select a venv by
directory order or fall back to system Python.
For a new environment, use the applicable conventional path when it is absent, or an explicit new absolute path in a durable user-owned location. If the conventional path exists, inspect it first. Reuse it when valid; otherwise report its exact defect, leave it untouched, and ask for a different new path. Return the selected interpreter path so downstream skills can use it directly.
Use pynvc-smoke for the setup smoke proof, decode-performance work, and a
conventional interface-availability check. That availability check inspects
only the exact smoke path and reports not_ready when it is absent or invalid.
Any consumer encode operation, including a capability availability proof, and
work using advanced raw decode, segmentation, or encode-performance samples
selects the separate full-samples environment and dependencies described in
the install guide. A media-free capability inventory may use either
conventional profile: inspect smoke first, then full-samples when smoke is
absent, and report the exact interpreter that answered.
Apply that either-profile allowance only when the entire request is a
media-free inventory. When a request also seeks an operation or a capability
availability proof, use the profile that request binds; if that profile is not
ready, report not_ready and do not query the other conventional profile. The
interface-availability check above remains bound to the smoke path.
Both products use one generated 640×360 8-bit NV12 frame (345,600 bytes), H.264 encode, and an independent decode of the fresh bitstream.
| Product | Required evidence |
|---|---|
| Native | Package-owned AppEncCuda reports one encoded frame; package-owned AppDec consumes that exact nonempty bitstream, reports one decoded frame, and writes a 345,600-byte NV12 output. |
PyNv full-samples | Wheel-owned basic/encode.py reports one encoded CPU-buffer frame; wheel-owned advanced/decode.py consumes the exact bitstream, reports one frame, and writes a 345,600-byte output. |
PyNv pynvc-smoke | The same encoder proof; wheel-owned advanced/decode_perf.py reports one requested decoded frame and a total of one, with no worker error, warning, or traceback. It does not claim a raw decoded file. |
Exit zero or file creation alone is insufficient. Require the expected marker and count, a newly created nonempty bitstream, and the independent consumer. Do not require decoded bytes to equal the input because H.264 is lossy.
CPU-buffer and device-memory sample modes establish only the exact readiness
operation. They do not establish external buffer sharing, zero copy, or a
synchronization primitive for another process or pipeline stage. For a request
to confirm an in-process or cross-stage buffer contract, invoke
jetson-video-pipeline; require an authenticated operation of the actual
downstream stage that proves the sharing handle, format and layout, ownership
and lifetime, signal and wait behavior, and safe buffer reuse. Do not answer
that request from setup evidence alone.
For a request that also asks about support, encoder configuration, throughput, or an application workflow, invoke only the matching public skill:
jetson-video-capabilityjetson-video-recipejetson-video-benchmarkjetson-video-pipelinePass the selected product and exact local paths as data. Do not read or import a sibling skill's private files. If a required sibling is unavailable, preserve completed setup results and name the missing skill.
apt-get -s simulation, and reject removals, downgrades, or unexpected
packages. Apply the reviewed command with noninteractive sudo -n; if that
authorization is unavailable, stop rather than using a password prompt,
su, or another escalation path.dpkg --audit after an APT mutation and stop if it is nonempty or fails.nvidia-video-codec-sdk; a native-only request must not create a
venv.For PSNR, SSIM, DRM playback, capture, inference, or display work, state that setup does not own that workflow and route only an explicitly requested video codec portion.
© 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 8 other files (references) in skills/jetson-video-setup of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
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.
Jetson Video Setup 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 Video Setup this skillNVIDIA/skills | 3.5k | 1 repos | ~2.4k | Automated safety check: Notes | Apache-2.0 | |
| Triton SkillslowlyC/agent-gpu-skills | 169 | — | ~1.3k | Automated safety check: Pass | MIT | |
| Hyperpod Version Checkerawslabs/agent-plugins | 912 | 1 repos | ~910 | Automated safety check: Pass | Apache-2.0 | |
| Megatron-LM on SLURMNVIDIA/Megatron-LM | 18k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Debug Failing GPUfacebookexperimental/triton | 201 | — | ~709 | Automated safety check: Pass | MIT | |
| Optimize For GPUK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT |
slowlyC/agent-gpu-skills
Write, debug, and optimize Triton and Gluon GPU kernels from local upstream tutorials, production kernels, language definitions, and compiler source.
awslabs/agent-plugins
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia)…
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.
facebookexperimental/triton
Recover from GPU-busy / GPU-unavailable failures. An agent skill from facebookexperimental/triton.
K-Dense-AI/scientific-agent-skills
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster.
PaddlePaddle/Paddle
A skill your agent uses when working with Paddle 3.0 compiler full pipeline: SOT (Symbolic Opcode Translator) for bytecode-level dy2st graph capture, PIR (Paddle IR) for SSA-based intermediate…
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
A skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…. Jetson Video Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.
Jetson Video Setup fits situations like: verifying readiness of the native NVIDIA Video Codec SDK; pyNvVideoCodec on Jetson; including the one-frame encode/decode smoke test with official samples; when interpreting what those readiness results.
Run `npx skills add NVIDIA/skills --skill jetson-video-setup -a claude-code`. Or copy the skill folder (skills/jetson-video-setup in NVIDIA/skills) into .claude/skills/jetson-video-setup in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-video-setup -a codex`. Or copy the skill folder (skills/jetson-video-setup in NVIDIA/skills) into .agents/skills/jetson-video-setup 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-video-setup -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-video-setup, .gemini/skills/jetson-video-setup, .github/skills/jetson-video-setup and .opencode/skills/jetson-video-setup in your project.
Going by SKILL.md and its folder, Jetson Video Setup needs the command-line tools its instructions call (apt-get). Our summary lists: Python 3.
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 notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Jetson Video Setup 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 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Jetson Video Setup: Triton Skill (slowlyC/agent-gpu-skills, 169 stars), Hyperpod Version Checker (awslabs/agent-plugins, 912 stars), Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars) and Debug Failing GPU (facebookexperimental/triton, 201 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 3,534 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.