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.
$ npx skills add NVIDIA/skills --skill jetson-customize-camera -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-customize-camera --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-customize-camera .claude/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .claude/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-cameraType 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-customize-camera -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-customize-camera --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-customize-camera .agents/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .agents/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-camera -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-customize-camera --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-customize-camera .cursor/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .cursor/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-camera--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-customize-camera -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-customize-camera --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-customize-camera .gemini/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .gemini/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-cameraInstalls 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-customize-camera -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-customize-camera .github/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .github/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-camera -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-customize-camera --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-customize-camera .opencode/skills/jetson-customize-camera && 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-customize-camera" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-camera into .opencode/skills/jetson-customize-camera/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-camera", 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-customize-cameraEnable MIPI/GMSL camera sensors on a Jetson Thor or Orin custom carrier by rendering a kernel-DT overlay from the in-tree sensor DTSI.
Jetson Customize Camera is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. 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.
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/camera-overlay-templates/README.md`).
It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform. 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.
4 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.
No scripts in the folder and no shell commands in SKILL.md.
From 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 Customize Camera loads about 2.4k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 1,011 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 found no risky patterns in SKILL.md.
Automated 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). 1,011 words, ~2,441 tokens.
.claude/skills/jetson-customize-camera/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.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:
.dtsi references for on the active platform.tegra<soc>-camera-<sensor>*.dtsi when one exists (the DTSI IS
the wiring source of truth); captured per-sensor from the user
when the sensor is custom.fragment@N body, append into the composite custom overlay
.dts for the active target (per
../../references/bsp-customization-kernel-dtb.md),
verify the composite with fdtoverlay.
/jetson-build-source compiles the composite and owns the
carrier conf's OVERLAY_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:
fragment@N block (plus jetson-header-name on the
composite root if not already present) appended to the composite
custom overlay .dts per
../../references/bsp-customization-kernel-dtb.md
→ committed to the bsp_sources/ hardware repo.
/jetson-build-source compiles the composite to .dtbo and
owns its Makefile + flash-conf registration.tegra-capture-vi / NVCSI on a custom carrier.v4l2-ctl --list-devices shows no
tegra-capture-vi channels, OR sensor enumeration on a fresh
daughter-card needs to be confirmed.Prerequisites:
reference_devkit: + custom_carrier: blocks.<source.root_path>/Linux_for_Tegra/.git exists
(/jetson-init-source)./jetson-derive-carrier has 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 .dtsi files (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.sh may need to
run if Branch B was used — see Step 5a.i below).bsp_developer_guide mirror or
separate path), Adaptation Guide §Camera, carrier schematic, SoC
TRM, Module Design Guide.dtc, cpp, fdtoverlay on PATH.Detailed step-by-step procedure (Steps 1–7, with all tables, code
blocks, and gates) lives in
references/procedure.md. Summary:
/* 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 set
jetson-header-name on the composite root. Verify with
dtc + fdtoverlay (pre-compile single-fragment gate;
post-compile deep-tree uniqueness gate). Commit via the
workflow's commit-message preview gate.cam_i2c_*,
extperiph<m>_clk, reset/PWDN/PWR_EN GPIOs) via
pin_verifier.py; route mismatches to /jetson-customize-pinmux.<workspace>/target-platform/<profile-stem>.jetson-customize-camera.json
and emit the headline, then drive the downstream next-step chain via
sequential AskUserQuestion prompts per references/procedure.md
Step 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.fragment@N to the composite. A second one carrying status
overrides triggers dtc deep-merge → duplicate sibling subtrees
(e.g. two tca9546@70) → runtime first-match drops the dtsi-
supplied deep tree → camera silently doesn't enumerate. Gate on
this skill's marker only (Step 5c).compatible is owned globally, not by this
skill. Don't widen from any in-tree per-sensor dtbo's
compatible (devkit-SKU-gated). Fix the composite root if needed.jetson-header-name from any in-tree per-sensor dtbo. Fixed,
carrier-agnostic; read once, paste onto the metadata root.OVERLAY_DTB_FILE. Registering both your rendered overlay AND
the in-tree tegra<soc>-p3971-camera-<sensor>-overlay.dtbo
produces a phantom subdev bind that bricks camera enumeration.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.mode<N>, sensor_modes, pixel_phase — copy verbatim from the
closest in-tree DTSI.camera_common_regulator_get (null) ERR: -EINVAL = missing
avdd-reg / iovdd-reg / dvdd-reg strings — splice the FULL
sensor body; always-on rails fall back to dummy regulator.&label refs must exist in base DTB's __symbols__.
Use target-path = "/tegra-capture-vi" when the label is absent;
fdtoverlay exits non-zero with FDT_ERR_NOTFOUND otherwise.cpp failure on dt-bindings/gpio/gpio.h: No such file =
L4T source tree isn't staged. Re-run /jetson-init-source (Branch
B's source_sync.sh fetches the headers). Never fabricate the
macro expansion.ODMDATA="..." is untouched.
OVERLAY_DTB_FILE+= is owned by /jetson-build-source Step
5.0a — this skill never touches the carrier flash conf.<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/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.tmpl templates: 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_sources repo (with the
hardware/nvidia/<chip-dir>/ per-sensor DTSI tree) this skill
reads and commits into.© 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 10 other files (references) in skills/jetson-customize-camera of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
Jetson Customize Camera 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 Customize Camera this skillNVIDIA/skills | 3.5k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Fla Triton To Gluonfla-org/flash-linear-attention | 5.8k | — | ~4.2k | Automated safety check: Pass | MIT | |
| DGX Spark Memory and Thermal Opswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| DGX Spark Training Gotchaswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Megatron-LM on SLURMNVIDIA/Megatron-LM | 18k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| OpenVLA-OFT Fine-TuningOrchestra-Research/AI-Research-SKILLs | 13k | 1 repos | ~3.7k | Automated safety check: Pass | MIT |
fla-org/flash-linear-attention
Workflow for porting an existing Triton kernel in fla/ops/ to Gluon (triton.experimental.gluon) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA…
wshobson/agents
Plans memory headroom, works through out-of-memory failures and watches temperature and power during long ML training jobs on NVIDIA DGX Spark.
wshobson/agents
Preflight checks and diagnosis for ten known failure modes of ML training on NVIDIA DGX Spark's GB10, spanning launch errors, memory, thermals, bandwidth and precision.
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.
Orchestra-Research/AI-Research-SKILLs
Fine-tunes and evaluates OpenVLA-OFT and OFT+ robot policies with LoRA and continuous action heads on LIBERO simulation and ALOHA real-robot setups.
Orchestra-Research/AI-Research-SKILLs
Sets up and runs NVIDIA Cosmos Policy evaluations on the LIBERO and RoboCasa simulators, including headless GPU rendering and inference latency profiling.
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
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. Jetson Customize Camera is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. 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.
Jetson Customize Camera fits situations like: UPHY lane allocation; tasks that involve GPU and accelerator computing.
Run `npx skills add NVIDIA/skills --skill jetson-customize-camera -a claude-code`. Or copy the skill folder (skills/jetson-customize-camera in NVIDIA/skills) into .claude/skills/jetson-customize-camera in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-customize-camera -a codex`. Or copy the skill folder (skills/jetson-customize-camera in NVIDIA/skills) into .agents/skills/jetson-customize-camera 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-customize-camera -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-customize-camera, .gemini/skills/jetson-customize-camera, .github/skills/jetson-customize-camera and .opencode/skills/jetson-customize-camera in your project.
SKILL.md names no scripts, command-line tools or credentials: Jetson Customize Camera is instructions for the agent only. 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 no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Jetson Customize Camera 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 9.8k 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 12k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Jetson Customize Camera: Fla Triton To Gluon (fla-org/flash-linear-attention, 5.8k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars), DGX Spark Training Gotchas (wshobson/agents, 40k stars) and Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k 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.
