Jetson Customize Camera logo

Jetson Customize Camera

Organization
NVIDIA-AI-IOT
jetson-customize-camera

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.

Overview

PublisherNVIDIA-AI-IOT
Repositoryjetson-bsp-skills
Skill namejetson-customize-camera
Stars
73
Forks
13
Bundled files
9
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-AI-IOT on GitHub. Read the source before you install it.

Installation

Install the Jetson Customize Camera AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-customize-camera
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills.git /tmp/jetson-bsp-skills
mkdir -p .claude/skills
cp -r /tmp/jetson-bsp-skills/skills/jetson-customize-camera .claude/skills/jetson-customize-camera
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jetson Customize Camera in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jetson Customize Camera on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jetson Customize Camera is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Customize 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:

  1. Sensor selection — picked from the set NVIDIA ships in-tree .dtsi references for on the active platform.
  2. Carrier + module support check — verified against the Camera Development Guide, Adaptation Guide §Camera, carrier schematic, Module TRM, and carrier pinmap.
  3. Wiring — derived from the in-tree 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.
  4. Kernel-DT overlay — cpp-expand the in-tree DTSI, extract its 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:

  • Camera 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.

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-devices shows no tegra-capture-vi channels, 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/.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).
  • Source-of-truth docs registered or supplied at prompt: Camera Development Guide (in bsp_developer_guide mirror or separate path), Adaptation Guide §Camera, carrier schematic, SoC TRM, Module Design Guide.
  • dtc, cpp, fdtoverlay on PATH.

Procedure

Detailed step-by-step procedure (Steps 1–7, with all tables, code blocks, and gates) lives in references/procedure.md. Summary:

  1. Step 1 — Resolve active target + open source-of-truth docs.
  2. Step 2 — Enumerate supported sensors by globbing in-tree per-platform camera dtbos; classify as DPHY-direct / GMSL / custom. Never invent sensors.
  3. 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.
  4. Step 4 (custom path only) — Batched per-sensor wiring questions auto-filled from the carrier pinmap.
  5. 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 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.
  6. Step 6 — Verify ancillary CAM pin SFIOs (cam_i2c_*, extperiph<m>_clk, reset/PWDN/PWR_EN GPIOs) via pin_verifier.py; route mismatches to /jetson-customize-pinmux.
  7. Step 7 — Atomic-write run-state JSON sidecar at <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.

Gotchas

  • Dual-fragment trap. Contribute exactly ONE camera-tagged 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).
  • Composite root 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.
  • DO NOT also append the in-tree per-sensor dtbo to 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.
  • 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 = missing avdd-reg / iovdd-reg / dvdd-reg strings — splice the FULL sensor body; always-on rails fall back to dummy regulator.
  • External &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.
  • 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-source Step 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Jetson Customize Camera AI skill do?

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.

Why use Jetson Customize Camera on TypingMind?

Because you install it once and use it with any model. Jetson Customize Camera is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jetson Customize Camera in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-customize-camera. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Jetson Customize Camera?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jetson Customize Camera?

As many as you like. As long as a model supports skills, you can use Jetson Customize Camera with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jetson Customize Camera AI skill free?

Yes. It is published on GitHub by NVIDIA-AI-IOT under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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