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-build-sourcegit 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-build-source)<a href="https://agentmods.dev/skills/nvidia/skills/jetson-build-source"><img src="https://agentmods.dev/badge/skills/nvidia/skills/jetson-build-source.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
- medium Privilege Escalation · line 398 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00043 | $0.05118 |
| Opus 5 | $0.00022 | $0.02559 |
| Sonnet 5 | $0.00009 | $0.01024 |
| Haiku 4.5 | $0.00004 | $0.00512 |
Grade B, and why
jetson-build-source 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 3d 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.
| `host package missing: <pkg>` | `flex`/`bison`/`libssl-dev` refuse; others warn. | `sudo apt install <pkg>` per [`references/upstream-recipe.md`](references/upstream-recipe.md). | How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build BSP Source
Purpose
Rebuild the kernel-side artifacts (DTBs, OOT modules, in-tree
modules, kernel Image) implied by changes under
<source.root_path>/bsp_sources/, and write a manifest that
/jetson-promote-image reads to stage those outputs into the BSP
image. The skill never writes into <bsp_image.root_path> itself.
Prerequisites
- Active target-platform profile with
bsp_image:andsource.toolchain:resolved (run/jetson-init-imageand/jetson-init-sourcefirst). <source.root_path>/bsp_sources/populated with the kernel-side checkout layout/jetson-init-sourcematerializes.<bsp_image.root_path>/Linux_for_Tegra/source/kernel_src_build_env.shpresent (extracted frompublic_sources.tbz2).- Host packages:
flex,bison,libssl-dev(hard);git,build-essential,bc,zstd(warn-only). - Cross-toolchain at
${source.toolchain}gccresolvable on disk.
Overview
This skill is the Build stage of the workflow — see
../../context/bsp-customization-workflow.md
for where it sits in the Setup → Customize → Build → Deploy pipeline
and what triggers it. The skill takes source-side customization
commits, rebuilds the implied artifacts, and records which were
rebuilt in a manifest. Outputs stay in-tree under
<source.root_path>/bsp_sources/;
jetson-promote-image reads the
manifest at Deploy to copy each rebuilt artifact into the matching
path under <bsp_image.root_path>/Linux_for_Tegra/.
Overlay-only edits (nvpmodel.conf, nvfancontrol.conf, BPMP DTB)
skip Build — customize-* stages them directly to the overlay
tracker; BPMP DTB uses the dtc decompile → edit → recompile loop in
../../references/bsp-customization-bpmp-dtb.md.
Custom-overlay slot ownership. Kernel-DT customizations from
every customize-* skill collect into a single composite
tegra<soc>-<carrier-id-sku>+<module-id>-xxxx-custom.dts per
active target — see
../../references/bsp-customization-kernel-dtb.md
for the filename / location / append protocol. This skill is the
sole owner of the composite's per-dir Makefile registration
(dtbo-y += <name>.dtbo) and the carrier flash conf's
OVERLAY_DTB_FILE+= line (the "Register composite custom overlay" step).
What ships with it
9 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.
- 3d ago First seen · 412 lines · 43 tokens per session scan B 3322a4e01002
jetson-build-source is a skill published in the GitHub repository NVIDIA/skills (3,223 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 5,118 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
terraform-module-library
Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
hyperpod-version-checker
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), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches…
technical-troubleshooting
Provide setup, troubleshooting, and maintenance guidance. Use when the user reports a device that won't power on, connectivity issues, setup questions, overheating, or maintenance concerns.
gpu-optimizer
GPU optimization for consumer NVIDIA GPUs (8-24GB VRAM) covering mixed precision, gradient checkpointing, XGBoost GPU, CuPy/cuDF migration, and torch.compile. Triggers on: "optimize GPU training", "speed up CUDA", "reduce OOM", "migrate NumPy to CuPy", "manage GPU memory", "benchmark PyTorch".