Jetson Customize Mgbe

Organization

NVIDIA-AI-IOT

jetson-customize-mgbe

Enable Jetson Thor 25G/10G/1G MGBE QSFP via kernel-DT overlay. Do NOT use for UPHY lane allocation or ODMDATA edits.

Overview

Publisher	NVIDIA-AI-IOT
Repository	`jetson-bsp-skills`
Skill name	`jetson-customize-mgbe`
Stars	73
Forks	13
Bundled files	7
License	Apache-2.0
Links	Skill source Repository

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.
7 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 Mgbe AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-customize-mgbe
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-customize-mgbe

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-mgbe .claude/skills/jetson-customize-mgbe

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jetson Customize Mgbe 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 Mgbe 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 Mgbe 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 MGBE / 25G QSFP

Overview

Thor T264 exposes mgbe0..mgbe3. On a custom carrier, the 25G QSFP cage (or 10G / 1G fiber path) is wired to one of them through SerDes — with or without an external MDIO PHY in front of the cage. This skill renders the kernel-DT overlay that pairs the BPMP allocation with kernel-side status="okay" + PHY plumbing on &mgbeN.

Out of scope:

UPHY lane allocation — owned by /jetson-customize-uphy. Refuse if the chosen uphy1-config-N doesn't allocate the target MGBE.
All ODMDATA tokens (mgbeN-speed-*, sub-node mgbeN_status=*) — owned by /jetson-customize-uphy in its single atomic ODMDATA commit. This skill MUST NOT touch ODMDATA=.

Output is one commit to the composite custom overlay .dts in 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 25G", "configure QSFP", "set MGBE PHY mode", "wire MGBE to QSFP", or asks to bring up a 10G / 1G fiber path.
Cold boot succeeds but ip link show mgbe<N> reports state DOWN or NO-CARRIER on the configured controller, OR the controller never appears at all.
jetson-customize-uphy ran with uphy1-config-8 (or another config allocating MGBE) and you now need to bring up the per-controller side.

Prerequisites:

Active profile selected with reference_devkit: (Thor) + custom_carrier: blocks.
<source.root_path>/Linux_for_Tegra/.git exists (/jetson-init-source).
/jetson-derive-carrier has run — carrier flash-conf fork is in the overlay tracker.
/jetson-customize-uphy chose a UPHY config that allocates the target MGBE controller's lanes (uphy1-config-8 on Thor for MGBE0..3 25G).
Source-of-truth docs registered or supplied at prompt: Adaptation Guide, Module Design Guide, SoC TRM.
When custom_carrier: is present, both documents.custom_carrier_schematic AND documents.custom_carrier_pinmux_xls are REQUIRED. Refuse the run if either is missing — MGBE routing on a custom carrier cannot be guessed. Reference-devkit-only profiles skip this check.
dtc on PATH.

Procedure

See references/procedure.md for the full step-by-step procedure (Steps 1–8). Summary:

Resolve active target + documents. Validate active profile, custom_carrier, overlay tracker; locate the relevant Adaptation Guide chapter and pinmap.
Per-controller question loop. AskUserQuestion driven by questions.json (controller, phy_mode, attach kind, I²C bus/addr, reset GPIO, compatible_list).
Derive max-speed from phy_mode. Decompile the BPMP DTB to pick sub-node vs top-level token grammar; cite the inspection in notes.
Verify HSIO pins + auto-fix. Run pin_verifier.py for MDC/MDIO/RESET/INT; surface mismatches and route to /jetson-customize-pinmux.
(no ODMDATA edits.) MGBE ODMDATA tokens are emitted by /jetson-customize-uphy. Step 5 only records the BPMP DTB token-form inspection (sub-node vs top-level) in notes[] for audit.
Append composite-overlay fragments. Write one fragment per controller into the composite custom overlay .dts; obey the /* custom-bsp: mgbe:mgbe... */ marker contract; run the cpp/dtc/fdtoverlay pre-flight.
(Reserved.) Sibling-skill ordering / cross-cutting validation.
Run-state sidecar + summary + next-step chain. Write <profile-stem>.jetson-customize-mgbe.json and emit the one-line + table summary, then drive the downstream chain via sequential AskUserQuestion prompts per references/procedure.md Step 8. Never substitute a printed "Next step: …" line for the prompts.

Gotchas

Stock Thor BPMP DTB has no /mgbe/mgbe@N subtree — only mgbe<N>-speed under /uphy. The mgbe<N>_status=disabled sub-node token is silently rejected on these releases; the whole ODMDATA line is then dropped at flash time. Always decompile BPMP DTB (Step 3) before emitting; use the top-level dashed form (mgbe<N>-speed-del to remove, mgbe<N>-speed-25G to set) when the sub-node isn't there. Same wrong-form failure surface as jetson-customize-uphy.
mdio child needs both #address-cells = <1> AND #size-cells = <0> when phy_attach_kind=="phy". Missing either → kernel rejects phy@<addr> reg property at probe; MGBE never comes up.
Overlay root compatible must intersect live DT compatible. UEFI plugin-manager filters by compatible match. A mismatched overlay is silently skipped — flash succeeds, MGBE stays disabled, no error in dmesg. Always sanity-check against /proc/device-tree/compatible on a booted reference DUT.
OVERLAY_DTB_FILE ordering is jetson-build-source's problem, not this skill's. This skill never touches the carrier flash conf. The composite custom overlay is registered (by /jetson-build-source Step 5.0a) AFTER the platform *-dynamic.dtbo, which is the correct ordering. If you find yourself appending OVERLAY_DTB_FILE+= in this skill, you're duplicating ownership — stop, and let the build skill do it.
UPHY lane allocation is jetson-customize-uphy's job. If the chosen uphy1-config-N doesn't allocate lanes for the target MGBE controller, BL31 SError (fmon_update_config: detected fault 0x80) on cold boot. Always run /jetson-customize-uphy first; cite the chosen uphy1-config-N in this skill's run-summary notes[].
Don't disable a stock-okay controller via ODMDATA alone. Same rule as jetson-customize-uphy: mgbe<N>_status=disabled for a controller that's already disabled in BPMP DTB is a no-op the parser may treat as ambiguous → drops the rest of the ODMDATA line. Disable via the kernel-DT overlay (status="disabled") only.
Don't touch the upstream BSP at <bsp_image.root_path>. All edits land in the overlay tracker / bsp_sources mono-repo under the pristine + customization commit pattern.
JSON sidecar is structured state, not authoritative. Same caveat as jetson-customize-uphy: ODMDATA + overlay .dts + two git commits are the device-facing outputs; the sidecar is for tooling and idempotency only.

References

questions.json — Q-1..Q-8 prompt schema consumed by Step 2.
../../scripts/pin_verifier.py — shared HSIO pin verifier (Step 4).
../../references/platform_template.yaml — documents: block consumed by Step 1.
../../context/bsp-customization-workflow.md — overlay edit protocol (batched pristine + customization commit).
../jetson-customize-uphy/SKILL.md — sibling skill that owns UPHY lane allocation. Must run before this skill to set uphy1-config-N for MGBE-allocated configurations.
../jetson-customize-pinmux/SKILL.md — sibling skill invoked by Step 4 (with operator confirmation) to fix pin SFIO mismatches.
../jetson-derive-carrier/SKILL.md — must run first; produces the carrier flash-conf fork edited in Step 5 and the carrier base overlay this skill orders after.
../jetson-init-source/SKILL.md — produces the overlay tracker + bsp_sources repo this skill commits into.

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 Mgbe AI skill do?

Enable Jetson Thor 25G/10G/1G MGBE QSFP via kernel-DT overlay. Do NOT use for UPHY lane allocation or ODMDATA edits.

Why use Jetson Customize Mgbe on TypingMind?

Because you install it once and use it with any model. Jetson Customize Mgbe 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 Mgbe 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-mgbe. 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 Mgbe?

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 Mgbe?

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

Is the Jetson Customize Mgbe 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Customize MGBE / 25G QSFP

Overview

When to invoke

Procedure

Gotchas

References

Bundled files

Frequently asked questions

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