Nemotron Customizer Airgap logo

Nemotron Customizer Airgap

OrganizationPopular
NVIDIA-NeMo
nemotron-customizer-airgap

Prepare, validate, build, and use Nemotron Customizer airgap image bundles for offline clusters. Use when planning airgapped deployments, editing deploy/nemotron-customizer/airgap/airgap.yaml, selecting workflow targets, grouping step execution images, baking repo overlays or wheel additions, resuming airgap runner builds, or submitting `nemotron steps run` jobs inside an airgapped environment.

Overview

PublisherNVIDIA-NeMo
RepositoryNemotron
Skill namenemotron-customizer-airgap
Stars
2.1K
Forks
419
Bundled files
8
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.

  • 8 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-NeMo on GitHub. Read the source before you install it.

Installation

Install the Nemotron Customizer Airgap 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.
  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-NeMo/Nemotron.git /tmp/Nemotron
mkdir -p .claude/skills
cp -r /tmp/Nemotron/deploy/nemotron-customizer/airgap .claude/skills/nemotron-customizer-airgap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nemotron Customizer Airgap 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 Nemotron Customizer Airgap 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 Nemotron Customizer Airgap 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.

Nemotron Customizer Airgap

Use this skill to help an agent produce a connected-machine airgap bundle and then submit Nemotron Customizer steps from the airgapped side. Keep it grounded in the checked-in runner and manifests; do not invent a parallel packaging flow.

Read First

  • deploy/nemotron-customizer/airgap/README.md for the operator flow.
  • deploy/nemotron-customizer/airgap/airgap.yaml for the current image map.
  • deploy/nemotron-customizer/airgap/runner.py when changing behavior.
  • tests/deploy/test_airgap_runner.py before editing runner logic.
  • deploy/nemotron-customizer/airgap/configs/ for runtime overlay configs.

For selected steps, inspect the catalog through the CLI:

bash
uv run nemotron steps show <step_id> --json

Workflow

  1. Establish the side of the workflow:

    • Connected machine: validate, build, save image tarballs.
    • Airgapped side: load images, set env profiles, run selected steps.
  2. Gather the minimum inputs:

    • Target steps and config names, for example sft/megatron_bridge:tiny.
    • Target architecture or Docker platform, for example linux/amd64.
    • Available base images and whether the connected machine can pull them.
    • Airgapped env profile name, mounts, model/data/checkpoint locations.
    • Whether destructive or expensive actions such as --execute, Docker build, Docker volume cleanup, or state-file removal are explicitly allowed.
  3. Plan with the runner first:

bash
uv run python deploy/nemotron-customizer/airgap/runner.py \
  --config deploy/nemotron-customizer/airgap/airgap.yaml

Use --target <step_id>:<config> for one-off selections without editing YAML. The runner expands dependencies from dependencies, validates selected step files/configs, groups execution images, and prints selected execution images.

  1. Edit airgap.yaml only where the runner expects configuration:

    • workflow.stages or CLI --target for selected customer steps.
    • dependencies for explicit upstream Nemotron Customizer step outputs.
    • step_execution_images for step-to-image mapping.
    • execution_images for base image, tag, tar, platform, and import probes.
    • launcher_image for the launcher container.
  2. Execute only when the user asks for a real build:

bash
uv run python deploy/nemotron-customizer/airgap/runner.py \
  --config deploy/nemotron-customizer/airgap/airgap.yaml \
  --execute

If a build fails midway, keep airgap-build-state.yaml and rerun the same command. Remove or move that state only when intentionally changing the plan.

  1. On the airgapped side, use images from out/airgap-manifest.yaml under step_execution_images. Submit with the plural CLI:
bash
uv run nemotron steps run <step_id> \
  -c <config-or-airgap-overlay> \
  -b <airgap-profile> \
  run.env.container_image=<image-from-manifest>

For sft/megatron_bridge, prefer the airgap overlay configs under deploy/nemotron-customizer/airgap/configs/; they clear runtime git auto-mounts because the runner bakes those repos into the execution image.

Guardrails

  • Keep models, datasets, checkpoints, secrets, and customer files out of images. Put them on persistent storage and reference them through config overrides and run.env.mounts.
  • Treat ${auto_mount:git+...} as a connected-machine build input. The runner bakes pinned repo overlays into execution images so airgapped jobs do not clone from GitHub.
  • Do not add missing packages blindly. Let discover-execution-deps and import probes determine small additions; keep heavyweight framework deps in the base image choice.
  • Preserve offline defaults unless the user has an internal mirror: HF_HUB_OFFLINE=1, TRANSFORMERS_OFFLINE=1, HF_DATASETS_OFFLINE=1, and WANDB_MODE=offline.
  • Use nemotron steps ...; do not reintroduce nemotron step ....

Validation

After edits to runner logic, YAML structure, or airgap docs, run:

bash
uv run pytest tests/deploy/test_airgap_runner.py -q

For CLI-facing examples, also smoke the command shape:

bash
uv run nemotron steps --help
uv run nemotron steps show data_prep/sft_packing --json

Do not run Docker build/save stages during validation unless the user explicitly asked for a real connected-machine bundle build.

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 Nemotron Customizer Airgap AI skill do?

Prepare, validate, build, and use Nemotron Customizer airgap image bundles for offline clusters. Use when planning airgapped deployments, editing deploy/nemotron-customizer/airgap/airgap.yaml, selecting workflow targets, grouping step execution images, baking repo overlays or wheel additions, resuming airgap runner builds, or submitting `nemotron steps run` jobs inside an airgapped environment.

Why use Nemotron Customizer Airgap on TypingMind?

Because you install it once and use it with any model. Nemotron Customizer Airgap 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 Nemotron Customizer Airgap in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-NeMo/Nemotron/tree/main/deploy/nemotron-customizer/airgap. 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 Nemotron Customizer Airgap?

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 Nemotron Customizer Airgap?

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

Is the Nemotron Customizer Airgap AI skill free?

Yes. It is published on GitHub by NVIDIA-NeMo 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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