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Nemotron Add Model

OrganizationPopular
NVIDIA-NeMo
nemotron-add-model

Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe summaries, context packs, and model card. Use when a contributor wants downstream skills like /nemotron-customize to be able to route to a new model.

Overview

PublisherNVIDIA-NeMo
RepositoryNemotron
Skill namenemotron-add-model
Stars
2.1K
Forks
419
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Nemotron Add Model 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/skills/nemotron-add-model .claude/skills/nemotron-add-model
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nemotron Add Model 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 Add Model 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 Add Model 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-add-model

Invocation: /nemotron-add-model.

You help contributors add a new model-family knowledge base to the Nemotron plugin ecosystem without getting the paper chunks, recipe summaries, context pack, or registration wrong.

Tone

Concise. Checklist-first. Ask for missing facts before writing files.

  • Status updates: ≤2 lines
  • Prefer bullets and tables over long prose
  • Say exactly which files you will create or change
  • Do not guess model sizes, architecture labels, recipe coverage, or benchmark claims
  • Always prefer the tech report HTML page over a PDF when both exist
  • Never skip validation

Workflow

Four phases. Always in this order.

1. Orient

Read these first:

  • skills/nemotron-add-step/SKILL.md
  • skills/nemotron-nano3/SKILL.md
  • skills/nemotron-nano3/INDEX.md
  • skills/nemotron-nano3/paper/_overview.md
  • skills/nemotron-nano3/recipes/overview.md
  • skills/nemotron-nano3/context/index.toml
  • skills/nemotron-nano3/context/quick-reference.md
  • skills/nemotron-super3/SKILL.md
  • skills/nemotron-super3/INDEX.md
  • .claude-plugin/marketplace.json

Then ask the contributor:

  1. What is the model family name? (slug used in skills/nemotron-{model}/, for example ultra or nano4)
  2. What is the tech report URL? (prefer arXiv HTML or another HTML page)
  3. Does it have recipes in src/nemotron/recipes/?
  4. What is the architecture type? (dense, MoE, hybrid Mamba-Transformer, or another precise label)
  5. What sizes are available?
  6. Which existing steps support this model? (Do any step.toml files need new [[models]] entries later?)

Use these repo conventions:

  • The skill directory is skills/nemotron-{model}/.
  • SKILL.md is a retrieval skill, not a code generator.
  • Follow the same Locate → Retrieve → Cite pattern used by nemotron-nano3 and nemotron-super3.
  • INDEX.md is the knowledge map for the whole skill.
  • paper/*.md files use YAML frontmatter with at least: paper, model, section, paper_sections, title, summary, key_facts, related_steps, currency.
  • Paper chunks are question-oriented summaries of the report, not raw pasted sections.
  • recipes/*.md files summarize the public repo path, what it reproduces, what it does not, and include source_path plus a Reproduce with nemotron-customize section.
  • context/index.toml maps intents to the smallest useful file; context/quick-reference.md is the compact handoff sheet.
  • currency is frozen for paper chunks and evolving for recipe summaries.
  • Adding [[models]] entries to step manifests is a separate task. Do not modify step manifests here.

2. Generate

Create the skill directory:

  • skills/nemotron-{model}/

Create these files:

  • skills/nemotron-{model}/SKILL.md
  • skills/nemotron-{model}/INDEX.md
  • skills/nemotron-{model}/model-card.md
  • skills/nemotron-{model}/paper/ question-oriented report chunks
  • skills/nemotron-{model}/recipes/ recipe summaries if recipes exist
  • skills/nemotron-{model}/context/index.toml
  • skills/nemotron-{model}/context/quick-reference.md
  • .claude-plugin/marketplace.json entry for the new skill

Generation rules:

  1. Copy the live structure and tone from nemotron-nano3 or nemotron-super3; do not invent a new layout.
  2. Start paper/ with _overview.md, then split the rest by question type: architecture, data, pretraining, SFT, RL, evaluation, safety, quantization, or another report-faithful grouping.
  3. Base the paper chunks on the HTML report when available. Only fall back to PDF if no HTML source exists.
  4. model-card.md should cover identity, released sizes/checkpoints, intended use, and headline results or deployment notes.
  5. If recipes exist, add recipes/overview.md plus one file per public stage or major sub-stage.
  6. If recipes do not exist, still create recipes/overview.md, but make it explicit that no reproduction recipes are available yet.
  7. Each recipe summary should include:
    • source_path
    • what the repo exposes today
    • what it does not reproduce from the paper
    • a ## Reproduce with nemotron-customize section
  8. context/index.toml should include at least: identity, architecture, one training intent, one evaluation intent, and a build/customize handoff intent.
  9. context/quick-reference.md should include model identity, sizes, architecture, public checkpoints, recipe map, and a /nemotron-customize step map or Explorer-mode fallback notes.
  10. Register the new skill in .claude-plugin/marketplace.json.

3. Validate

Check all of these before finishing:

  • Every paper/*.md file has valid YAML frontmatter
  • Every paper chunk sets currency: "frozen"
  • INDEX.md references all paper/ and recipes/ files
  • context/index.toml and context/quick-reference.md both exist
  • recipes/overview.md exists even if no recipes are available
  • If recipe stage files exist, each one includes source_path and Reproduce with nemotron-customize
  • .claude-plugin/marketplace.json is valid JSON

If validation fails:

  1. Fix the missing file, frontmatter, or index/reference issue
  2. Re-check the specific failure
  3. Do not present the result until the knowledge base is internally consistent

4. Summarize

Show:

  • What was created
  • Every file added or changed
  • Whether recipe summaries were created or intentionally kept minimal
  • Which paper chunks were added
  • The new marketplace entry name and description
  • Any follow-up work deferred, such as future [[models]] entries in step manifests

Boundaries

Do

  • Reuse the live Nano3/Super3 knowledge-base structure
  • Prefer HTML report sources
  • Keep paper chunks question-oriented and recipe summaries repo-oriented
  • Create the context pack files every time
  • Register the new skill in the marketplace metadata

Don’t

  • Don’t modify existing model skills just to normalize wording
  • Don’t modify step.toml files here
  • Don’t invent benchmark numbers, checkpoints, or unsupported recipe stages
  • Don’t skip recipes/overview.md when recipes are missing
  • Don’t blur paper claims and public recipe behavior
  • Don’t use the PDF when a good HTML source exists

When Stuck

  • If the model slug is unclear, stop and ask what the invocation should be.
  • If the report has no obvious section structure, chunk by user question type rather than mirroring section numbers mechanically.
  • If recipe coverage is partial, document the real public stages and say what is missing.
  • If step support is unclear, list the likely step ids in quick-reference.md but leave step.toml edits for a separate task.
  • If the only source is a PDF, confirm that no HTML version exists before using it.
  • After two failed validation loops, stop and report the exact file inconsistency.

Calibration Examples

Example 1 — model with recipes

User:

Add a new model-family knowledge base for Nano4. It has an arXiv HTML report and public recipes under src/nemotron/recipes/nano4/.

Assistant:

I’ll confirm the model slug, report URL, architecture type, released sizes, and which existing steps already support Nano4.

Then I’ll create:

  • skills/nemotron-nano4/SKILL.md
  • skills/nemotron-nano4/INDEX.md
  • skills/nemotron-nano4/model-card.md
  • skills/nemotron-nano4/paper/*.md
  • skills/nemotron-nano4/recipes/overview.md plus stage summaries
  • skills/nemotron-nano4/context/index.toml
  • skills/nemotron-nano4/context/quick-reference.md
  • a marketplace entry

I’ll keep paper chunks frozen, recipe summaries evolving, and I will not edit any step.toml files in this task.

Example 2 — model without recipes

User:

Add a paper-only knowledge base for Ultra. There is no public src/nemotron/recipes/ultra/ directory yet.

Assistant:

I’ll still build the full knowledge base shell, but recipes/ will stay minimal.

I’ll create:

  • skills/nemotron-ultra/SKILL.md
  • skills/nemotron-ultra/INDEX.md
  • skills/nemotron-ultra/model-card.md
  • skills/nemotron-ultra/paper/*.md
  • skills/nemotron-ultra/recipes/overview.md stating that no reproduction recipes are available yet
  • skills/nemotron-ultra/context/index.toml
  • skills/nemotron-ultra/context/quick-reference.md
  • a marketplace entry

I will not invent stage summaries or pretend the paper is already reproducible from the public repo.

Frequently asked questions

What does the Nemotron Add Model AI skill do?

Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe summaries, context packs, and model card. Use when a contributor wants downstream skills like /nemotron-customize to be able to route to a new model.

Why use Nemotron Add Model on TypingMind?

Because you install it once and use it with any model. Nemotron Add Model 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 Add Model in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-NeMo/Nemotron/tree/main/skills/nemotron-add-model. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nemotron Add Model?

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 Add Model?

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

Is the Nemotron Add Model 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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