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SkillAtlasSkill 详情
jetson-customize-camera
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
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项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
低风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：0 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/jetson-customize-camera" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/jetson-customize-camera" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/jetson-customize-camera" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/jetson-customize-camera" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/jetson-customize-camera" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: jetson-customize-camera
description: >-
Enable MIPI/GMSL camera sensors on a Jetson Thor or Orin custom
carrier by rendering a kernel-DT overlay from the in-tree sensor
DTSI. Do NOT use for UPHY lane allocation or ODMDATA edits.
version: 0.0.1
license: "Apache-2.0"
metadata:
data-classification: public
author: "Jetson Team"
tags:
- bsp
- phase-2
- io
- camera
- csi
domain: metaCustomize camera (CSI / MIPI / GMSL sensor bring-up)
Overview
Tegra264 (Thor) and Tegra234 (Orin) expose a single tegra-capture-vi
controller fronted by NVCSI and a fixed set of CSI ports. Camera
bring-up is:
- Sensor selection — picked from the set NVIDIA ships in-tree
.dtsireferences for on the active platform. - Carrier + module support check — verified against the Camera Development Guide, Adaptation Guide §Camera, carrier schematic, Module TRM, and carrier pinmap.
- Wiring — derived from the in-tree
tegra<soc>-camera-<sensor>*.dtsiwhen one exists (the DTSI IS the wiring source of truth); captured per-sensor from the user when the sensor is custom. - Kernel-DT overlay — cpp-expand the in-tree DTSI, extract its
fragment@Nbody, append into the composite custom overlay.dtsfor the active target (per../../references/bsp-customization-kernel-dtb.md), verify the composite withfdtoverlay./jetson-build-sourcecompiles the composite and owns the carrier conf'sOVERLAY_DTB_FILE+=registration.
Agentic, not table-driven — sensor list is built at runtime by
globbing in-tree per-sensor dtbos. No _THOR_CAMERAS dict, no
questions.json, no Python renderer in the question path.
No ODMDATA edit — cameras don't consume UPHY lanes (CSI is a separate PHY pool). The skill emits only a kernel-DT overlay; the ODMDATA line in the carrier conf is untouched by this skill.
The output is one commit:
- Camera
fragment@Nblock (plusjetson-header-nameon the composite root if not already present) appended to the composite custom overlay.dtsper../../references/bsp-customization-kernel-dtb.md→ committed to thebsp_sources/hardware repo./jetson-build-sourcecompiles the composite to.dtboand owns its Makefile + flash-conf registration.
When to invoke
- The user says "enable camera", "configure CSI", "wire a Hawk /
Owl / IMX sensor", "MIPI camera", "GMSL camera", or asks to bring
up
tegra-capture-vi/ NVCSI on a custom carrier. - Flash boots but
v4l2-ctl --list-devicesshows notegra-capture-vichannels, OR sensor enumeration on a fresh daughter-card needs to be confirmed. - A sensor was previously enabled and the user wants to add another (multi-sensor bring-up).
Prerequisites:
- Active profile with
reference_devkit:+custom_carrier:blocks. <source.root_path>/Linux_for_Tegra/.gitexists (/jetson-init-source)./jetson-derive-carrierhas run — the carrier flash-conf fork is in the overlay tracker.<source.root_path>/bsp_sources/hardware/nvidia/<chip-dir>/nv-public/overlay/exists and contains the in-tree per-sensor.dtsifiles (sourced by/jetson-init-source's Branch A archive extract).<source.root_path>/bsp_sources/kernel/kernel-noble/include/dt-bindings/contains the macro headers cpp needs (source_sync.shmay need to run if Branch B was used — see Step 5a.i below).- Source-of-truth docs registered or supplied at prompt:
Camera Development Guide (in
bsp_developer_guidemirror or separate path), Adaptation Guide §Camera, carrier schematic, SoC TRM, Module Design Guide. dtc,cpp,fdtoverlayon PATH.
Procedure
Detailed step-by-step procedure (Steps 1–7, with all tables, code
blocks, and gates) lives in
references/procedure.md. Summary:
- Step 1 — Resolve active target + open source-of-truth docs.
- Step 2 — Enumerate supported sensors by globbing in-tree per-platform camera dtbos; classify as DPHY-direct / GMSL / custom. Never invent sensors.
- Step 3 / 3a — Cross-check carrier + module support against DTSI, Camera Development Guide, Adaptation Guide §Camera, SoC TRM, Module Design Guide, schematic, and carrier pinmap. Render the wiring table FIRST, then issue the confirm-or-customize gate.
- Step 4 (custom path only) — Batched per-sensor wiring questions auto-filled from the carrier pinmap.
- Step 5 — Append exactly ONE
/* custom-bsp: camera:<sensor> */fragment to the composite custom overlay.dts(see../../references/bsp-customization-kernel-dtb.md). Clone path cpp-expands the in-tree DTSI; custom path splices Step-4 answers + mode tables in-place. Idempotently setjetson-header-nameon the composite root. Verify withdtc+fdtoverlay(pre-compile single-fragment gate; post-compile deep-tree uniqueness gate). Commit via the workflow's commit-message preview gate. - Step 6 — Verify ancillary CAM pin SFIOs (
cam_i2c_*,extperiph<m>_clk, reset/PWDN/PWR_EN GPIOs) viapin_verifier.py; route mismatches to/jetson-customize-pinmux. - Step 7 — Atomic-write run-state JSON sidecar at
<workspace>/target-platform/<profile-stem>.jetson-customize-camera.jsonand emit the headline, then drive the downstream next-step chain via sequentialAskUserQuestionprompts perreferences/procedure.mdStep 7. The chain is a documented workflow gate, not a clarifying question — auto-mode does NOT exempt it. Never substitute a printed "Next step: …" line for the prompts.
Gotchas
- Dual-fragment trap. Contribute exactly ONE camera-tagged
fragment@Nto the composite. A second one carrying status overrides triggers dtc deep-merge → duplicate sibling subtrees (e.g. twotca9546@70) → runtime first-match drops the dtsi- supplied deep tree → camera silently doesn't enumerate. Gate on this skill's marker only (Step 5c). - Composite root
compatibleis owned globally, not by this skill. Don't widen from any in-tree per-sensor dtbo'scompatible(devkit-SKU-gated). Fix the composite root if needed. jetson-header-namefrom any in-tree per-sensor dtbo. Fixed, carrier-agnostic; read once, paste onto the metadata root.- DO NOT also append the in-tree per-sensor dtbo to
OVERLAY_DTB_FILE. Registering both your rendered overlay AND the in-treetegra<soc>-p3971-camera-<sensor>-overlay.dtboproduces a phantom subdev bind that bricks camera enumeration. - Stub overlay is a known footgun. Committing
tegra-capture-vi { status="okay"; num-channels=<N>; }with no ports / sensor / nvcsi body bricks the camera (all channel init failed). Splice the FULL sensor body via cpp + dtc. - Sensor mode tables must be spliced, never hand-authored.
mode<N>,sensor_modes,pixel_phase— copy verbatim from the closest in-tree DTSI. camera_common_regulator_get (null) ERR: -EINVAL= missingavdd-reg/iovdd-reg/dvdd-regstrings — splice the FULL sensor body; always-on rails fall back to dummy regulator.- External
&labelrefs must exist in base DTB's__symbols__. Usetarget-path = "/tegra-capture-vi"when the label is absent;fdtoverlayexits non-zero withFDT_ERR_NOTFOUNDotherwise. cppfailure ondt-bindings/gpio/gpio.h: No such file= L4T source tree isn't staged. Re-run/jetson-init-source(Branch B'ssource_sync.shfetches the headers). Never fabricate the macro expansion.- No ODMDATA edit, no flash-conf edit. Camera doesn't consume
UPHY lanes. The carrier conf's
ODMDATA="..."is untouched.OVERLAY_DTB_FILE+=is owned by/jetson-build-sourceStep 5.0a — this skill never touches the carrier flash conf. - Don't touch the upstream BSP at
<bsp_image.root_path>. All edits land in<source.root_path>/Linux_for_Tegra/(overlay tracker) and<source.root_path>/bsp_sources/(overlay.dts) under the pristine + customization commit pattern.
References
references/procedure.md— full step-by-step Steps 1–7 procedure (extracted from this SKILL.md).references/csi-dt-bindings.md— CSI / nvcsi / vi DT binding reference notes.references/overlay-template.md— guidance on the metadata-root + clone-body overlay shape.references/camera-overlay-templates/— starter.dts.tmpltemplates:dphy-direct.dts.tmpl,gmsl-serdes.dts.tmpl.../../scripts/pin_verifier.py— shared HSIO pin verifier (Step 6).../../references/platform_template.yaml—documents:block consumed by Step 1.../../context/bsp-customization-workflow.md— overlay edit protocol.../jetson-customize-pinmux/SKILL.md— sibling skill auto-invoked by Step 6 to fix HSIO pin SFIO mismatches (CAM I²C, MCLK, reset GPIOs).../jetson-derive-carrier/SKILL.md— must run first; produces the carrier base overlay (the*-dynamic.dtbo) this skill's composite stacks after.../jetson-init-source/SKILL.md— produces the overlay tracker +bsp_sourcesrepo (with thehardware/nvidia/<chip-dir>/per-sensor DTSI tree) this skill reads and commits into.
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