Per-controller PCIe enable / disable / lanes / link-speed for a Jetson Thor or Orin custom carrier via ODMDATA + kernel-DT overlay.
$ npx skills add NVIDIA/skills --skill jetson-customize-pcie -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-customize-pcie --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-pcie .claude/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .claude/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pcieType 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-pcie -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-customize-pcie --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-pcie .agents/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .agents/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pcie -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-customize-pcie --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-pcie .cursor/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .cursor/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pcie--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-pcie -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-customize-pcie --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-pcie .gemini/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .gemini/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pcieInstalls 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-pcie -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-pcie .github/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .github/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pcie -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-pcie --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-pcie .opencode/skills/jetson-customize-pcie && 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-pcie" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-customize-pcie into .opencode/skills/jetson-customize-pcie/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-customize-pcie", 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-pciePer-controller PCIe enable / disable / lanes / link-speed for a Jetson Thor or Orin custom carrier via ODMDATA + kernel-DT overlay.
Jetson Customize Pcie is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Per-controller PCIe enable / disable / lanes / link-speed for a Jetson Thor or Orin custom carrier via ODMDATA + kernel-DT overlay. Do NOT use for UPHY lane allocation or endpoint-mode bring-up.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/gotchas.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.
9 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. 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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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 Pcie loads about 2.3k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 925 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 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 925 words, ~2,318 tokens.
.claude/skills/jetson-customize-pcie/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.PCIe on Tegra264 (Thor, pcie@C0..C5) and Tegra234 (Orin,
pcie@C0..C10) is split across multiple controllers that share the
UPHY lane pool with USB3 / MGBE / UFS. Each controller's runtime
behavior is determined by two surfaces, both required:
| Surface | Target | Authoritative for |
|---|---|---|
ODMDATA pcie@N_status=… (+ pcie@N_max-link-speed, pcie@N_pcie-mode, pcie@N_clk-scheme, pcie-cN-endpoint-enable) | /pcie/pcie@N in BPMP DTB | UPHY lane power, refclk gating, controller-side power rails |
Kernel-DT overlay on &pcieN | /bus@0/pcie@<addr> in kernel DTB | Kernel probe, lane width, link speed, RC/EP mode |
Skipping the kernel overlay on a disable lets the kernel probe a powered-down PHY (link timeouts in dmesg). Skipping the ODMDATA token on a disable leaves BPMP holding the PHY hot.
Agentic, not table-driven — no controller table, no
questions.json. Every controller, lane width, schematic-routed
receptacle, and authoritative DT node address is discovered at runtime
from the docs + DTB + carrier pinmap.
The output is a kernel-DT overlay commit only. Per-controller
fragment@N blocks are appended to the composite custom overlay
.dts per
../../references/bsp-customization-kernel-dtb.md
and committed to the bsp_sources/ hardware repo.
/jetson-build-source compiles the composite to .dtbo and owns its
Makefile + flash-conf registration.
This skill MUST NOT edit ODMDATA="...". All ODMDATA tokens
(pcie@N_status=…, pcie@N_max-link-speed, pcie@N_pcie-mode,
pcie@N_clk-scheme, pcie-cN-endpoint-enable, plus the
uphyX-config-N surface tokens and UPHY_CONFIG="" clear) are
emitted by /jetson-customize-uphy in a single atomic commit on the
carrier flash-conf fork. The allocation table this skill consumes
from the UPHY sidecar already tells the operator which controllers
are okay / disabled / per-lane sized; this skill only translates
that table into kernel-DT overlay fragments and verifies that the
overlay agrees with the ODMDATA already committed by customize-uphy
(consistency check in Step 8 — disagreement is reported, not silently
fixed).
pcie@N_status token.jetson-customize-uphy ran and re-allocated lanes across PCIe controllers
(e.g. switched from uphy0-config-7 to uphy0-config-6 enabling
PCIe C3); the per-controller side now needs to be brought up.jetson-customize-mgbe reports the QSFP path is wired but the kernel
doesn't probe its PCIe-side companion (rare; XFI configurations).Prerequisites:
reference_devkit: + 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 has run — its JSON sidecar at
<workspace>/target-platform/<profile-stem>.jetson-customize-uphy.json
drives the per-controller enable decision.custom_carrier: is present, both
documents.custom_carrier_schematic AND
documents.custom_carrier_pinmux_xls are REQUIRED. Refuse the run
if either is missing — routing on a custom carrier cannot be guessed.
Reference-devkit-only profiles skip this check.dtc on PATH.Full step-by-step walkthrough lives in
references/procedure.md. High-level flow:
<carrier-pinmap>, <ref-dtb>, <uphy-state>). Refuse if
<uphy-state> is missing.<ref-dtb> and grepping the schematic for PEX<N>_* net labels.AskUserQuestion (multiSelect) — which controllers to customize.pin_verifier.py
for PE<N>_CLKREQ_L, PE<N>_RST_L, optional PE<N>_WAKE_L.enable from <uphy-state>,
lanes / speed from Adaptation Guide, mode hard-pinned to
"rc") → mandatory confirm-or-customize gate.fragment@N blocks (marker
/* custom-bsp: pcie:pcie@<addr> */) to the composite custom
overlay .dts in bsp_sources/. Pre-flight dtc + fdtoverlay.
Commit via the workflow's preview gate.
Do not edit ODMDATA — /jetson-customize-uphy already emitted
pcie@N_status=…, pcie@N_max-link-speed, pcie@N_pcie-mode,
pcie@N_clk-scheme, and pcie-cN-endpoint-enable in its single
atomic ODMDATA commit. This skill only translates the per-controller
plan into kernel-DT overlay fragments.git reset --hard autonomously.<workspace>/target-platform/<profile-stem>.jetson-customize-pcie.jsonAskUserQuestion prompts per references/procedure.md Step 9.
Never substitute a printed "Next step: …" line for the prompts.mode_override="ep" in Step 5c.enable is derived, not asked. UPHY-allocated controllers are
mandatorily okay; non-allocated are mandatorily disabled.Linux_for_Tegra/ +
bsp_sources/ only./jetson-build-source is authoritative./jetson-build-source Step 5.0a.<uphy-state> missing → run /jetson-customize-uphy first.dmesg | grep pcie;
re-verify ODMDATA pcie@<N>_status=okay and the overlay fragment
agree (Step 8 table in
references/procedure.md).<uphy-state> allocates the expected lane count; the kernel
fragment's num-lanes must match.compatible mismatch → fix the composite root, not the
fragment. UEFI plugin-manager silently skips on mismatch.git reset --hard. See gotchas.references/gotchas.md (RC pinning, node-
address sourcing, stock-disabled controllers, intra-file handoff
with jetson-customize-uphy).references/procedure.md — full nine-
step procedure (topology diff, plan derivation, overlay append,
ODMDATA cross-check, sidecar).references/gotchas.md — failure modes../../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 + commit message preview gate.../../references/bsp-customization-kernel-dtb.md
— composite overlay filename / skeleton / append protocol.../jetson-customize-uphy/SKILL.md
— sibling skill that owns UPHY lane allocation; its sidecar drives
the per-controller enable decision.../jetson-customize-pinmux/SKILL.md
— sibling skill invoked by Step 4 (with operator confirmation) to
fix HSIO pin SFIO mismatches.../jetson-customize-mgbe/SKILL.md
— sibling for MGBE controllers; shares the two-surface (ODMDATA../jetson-derive-carrier/SKILL.md
— must run first; produces the carrier flash-conf fork edited in
Step 6.../jetson-init-source/SKILL.md
— produces the overlay tracker + bsp_sources repo this skill
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 6 other files (references) in skills/jetson-customize-pcie of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
Jetson Customize Pcie 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 Pcie this skillNVIDIA/skills | 3.5k | — | ~2.3k | 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
Per-controller PCIe enable / disable / lanes / link-speed for a Jetson Thor or Orin custom carrier via ODMDATA + kernel-DT overlay. Jetson Customize Pcie is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Per-controller PCIe enable / disable / lanes / link-speed for a Jetson Thor or Orin custom carrier via ODMDATA + kernel-DT overlay.
Jetson Customize Pcie fits situations like: UPHY lane allocation; endpoint-mode bring-up.
Run `npx skills add NVIDIA/skills --skill jetson-customize-pcie -a claude-code`. Or copy the skill folder (skills/jetson-customize-pcie in NVIDIA/skills) into .claude/skills/jetson-customize-pcie in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-customize-pcie -a codex`. Or copy the skill folder (skills/jetson-customize-pcie in NVIDIA/skills) into .agents/skills/jetson-customize-pcie 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-pcie -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-pcie, .gemini/skills/jetson-customize-pcie, .github/skills/jetson-customize-pcie and .opencode/skills/jetson-customize-pcie in your project.
Going by SKILL.md and its folder, Jetson Customize Pcie needs the command-line tools its instructions call (git).
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. 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 Pcie 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 2.3k tokens (SKILL.md is roughly 9.3k 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 4.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Jetson Customize Pcie: 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 3,539 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.
