Agent skill

Nemotron Add Pattern

by NVIDIA-NeMo in NVIDIA-NeMo/Nemotron

Add a cross-cutting decision pattern under src/nemotron/steps/patterns/.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Nemotron Add Pattern

skills CLI
$ npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-add-pattern -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA-NeMo/Nemotron nemotron-add-pattern --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA-NeMo/Nemotron.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemotron-add-pattern .claude/skills/nemotron-add-pattern && rm -rf skills-src

Use ~/.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/

Facts

Skill name
nemotron-add-pattern
GitHub stars
2.1k
Token cost
~1.4k tokens
SKILL.md length
734 words
Files
1
Skills in repo
10
Repo updated
First seen
Licence
Apache-2.0

At a glance

Add a cross-cutting decision pattern under src/nemotron/steps/patterns/.

  • Works in 4 steps: Orient → Generate → Validate → …
  • A recurring ML decision (tokenizer lock
  • SKILL.md covers Tone, Workflow, Boundaries and When Stuck, plus 1 more section
  • Calls uv

What it does

Nemotron Add Pattern is an agent skill from NVIDIA-NeMo/Nemotron. Add a cross-cutting decision pattern under src/nemotron/steps/patterns/. Use when a recurring ML decision (tokenizer lock, eval bookends, LoRA-on-small-data, etc.) must be encoded so other skills can fire it during planning.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Fine-tuning. The repository describes itself as: Developer Asset Hub for NVIDIA Nemotron — A one-stop resource for training recipes, usage cookbooks, datasets, and full end-to-end reference examples to build with Nemotron models. The licence is Apache-2.0.

When your agent uses it

  • A recurring ML decision (tokenizer lock
  • LoRA-on-small-data
  • Etc.) must be encoded so other skills can fire it during planning

Example prompts

  • “/nemotron-add-pattern”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Orient
  2. Generate
  3. Validate
  4. Summarize

What it can do on your machine

Read from SKILL.md and the folder at commit ca8c409. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nemotron Add Pattern loads about 1.4k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 734 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from NVIDIA-NeMo/Nemotron at commit ca8c409, republished under its Apache-2.0 licence (© NVIDIA-NeMo). 734 words, ~1,422 tokens.

Download SKILL.mdSave it as .claude/skills/nemotron-add-pattern/SKILL.md (or your agent's skills folder).
name
nemotron-add-pattern
description
Add a cross-cutting decision pattern under src/nemotron/steps/patterns/. Use when a recurring ML decision (tokenizer lock, eval bookends, LoRA-on-small-data, etc.) must be encoded so other skills can fire it during planning.

nemotron-add-pattern

Invocation: /nemotron-add-pattern.

You help contributors add a new cross-cutting pattern to src/nemotron/steps/patterns/ without getting the frontmatter, scope, catalog regeneration, or tests wrong.

Tone

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

  • Status updates: ≤2 lines
  • Prefer bullets over long prose
  • Say exactly which pattern file you will create and which commands you will run
  • Do not guess step ids or confidence level
  • Keep the recommendation actionable, not academic
  • Always regenerate PATTERNS.md and run tests

Workflow

Four phases. Always in this order.

1. Orient

Read these first:

  • src/nemotron/steps/patterns/sft-small-dataset-prefer-lora.md
  • src/nemotron/steps/PATTERNS.md
  • src/nemotron/steps/index.py
  • tests/steps/test_patterns.py

Then ask the contributor:

  1. What is the pattern about? (one sentence)
  2. When should it apply? (natural-language triggers)
  3. Which steps does it touch? (step ids, or [] for global)
  4. What is the confidence level? (high, medium, or experimental)
  5. Does it introduce a new concept or just encode existing tribal knowledge?

Use these repo conventions:

  • Pattern files live at src/nemotron/steps/patterns/{id}.md.
  • The filename stem must match the frontmatter id.
  • Required frontmatter fields are id, title, tags, triggers, steps, and confidence.
  • steps: [] is valid for a global pattern.
  • Valid confidence values are high, medium, and experimental.
  • The body uses these sections: ## When to apply, ## What to do, ## Exceptions, ## References.
  • Step-strategy cross-links in step.toml are a separate task. Do not edit them here.
2. Generate

Create:

  • src/nemotron/steps/patterns/{id}.md

The pattern file must contain:

  • YAML frontmatter with id, title, tags, triggers, steps, confidence
  • ## When to apply
  • ## What to do
  • ## Exceptions
  • ## References

Generation rules:

  1. Keep the pattern id kebab-case and make it match the filename exactly.
  2. Turn vague triggers into 2–4 concrete, observable conditions.
  3. Scope the pattern honestly: use explicit step ids if it only applies to a subset of steps; use [] only when it is truly global.
  4. Put the recommendation itself in What to do; keep background explanation shorter than the action guidance.
  5. If the pattern introduces a new concept, define it in the first paragraph of When to apply.
  6. Do not modify existing patterns.
  7. Regenerate the catalog with:
    • uv run python src/nemotron/steps/index.py
  8. Run validations with:
    • uv run pytest tests/steps -q
3. Validate

Check all of these before finishing:

  • Frontmatter has all required fields
  • The pattern id matches the filename
  • steps contains only valid step ids
  • confidence is one of high, medium, experimental
  • src/nemotron/steps/PATTERNS.md is updated
  • uv run pytest tests/steps -q passes

If validation fails:

  1. Fix the real frontmatter, scope, or catalog issue
  2. Re-run the failing command
  3. Do not present the result until tests pass
4. Summarize

Show:

  • What pattern was created
  • The file added
  • The final scope (steps list or global)
  • The confidence level
  • That PATTERNS.md was regenerated
  • That uv run pytest tests/steps -q passed

Show full SKILL.md (288 more words)Show less

Boundaries

Do
  • Reuse the live pattern format from small-dataset-lora.md
  • Keep triggers concrete
  • Keep advice prescriptive and easy to apply
  • Regenerate PATTERNS.md
  • Run tests every time
Don’t
  • Don’t modify existing patterns
  • Don’t modify step.toml strategy blocks here
  • Don’t invent step ids
  • Don’t label a new idea high confidence without evidence
  • Don’t stop before tests pass

When Stuck

  • If the step scope is unclear, look at src/nemotron/steps/STEPS.md and ask which ids the contributor actually wants.
  • If the trigger is vague, rewrite it as an observable condition before writing the file.
  • If the concept is still experimental, mark it experimental rather than overstating certainty.
  • If the pattern overlaps an existing one, explain the overlap and ask whether this should be a separate pattern.
  • After two failed validation loops, stop and report the exact failing command and error.

Calibration Examples

Example 1 — simple high-confidence pattern

User:

Add a pattern saying we should use a consistent random seed across stages.

Assistant:

I’ll confirm whether this is global or limited to a few steps, then I’ll create src/nemotron/steps/patterns/consistent-random-seed.md with the required frontmatter and the four body sections.

I’ll mark it high confidence only if the guidance is already established team practice, regenerate PATTERNS.md with uv run python src/nemotron/steps/index.py, and run uv run pytest tests/steps -q before I finish.

Example 2 — experimental step-scoped pattern

User:

Add a pattern for code-generation SFT that uses a higher learning rate.

Assistant:

I’ll confirm the exact step ids and keep the scope narrow, for example sft/automodel and sft/megatron_bridge if that is what you intend.

Because this is a newer idea rather than settled guidance, I’ll label it experimental, encode the trigger conditions in frontmatter, create the new pattern markdown file, regenerate PATTERNS.md, and run uv run pytest tests/steps -q.

© NVIDIA-NeMo, 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

Files

Just SKILL.md in skills/nemotron-add-pattern of NVIDIA-NeMo/Nemotron.

Open the folder on GitHubat commit ca8c409

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Questions about Nemotron Add Pattern

What does Nemotron Add Pattern do?

Add a cross-cutting decision pattern under src/nemotron/steps/patterns/. Nemotron Add Pattern is an agent skill from NVIDIA-NeMo/Nemotron. Add a cross-cutting decision pattern under src/nemotron/steps/patterns/.

When should I use Nemotron Add Pattern?

Nemotron Add Pattern fits situations like: A recurring ML decision (tokenizer lock; loRA-on-small-data; etc.) must be encoded so other skills can fire it during planning.

How do I install Nemotron Add Pattern in Claude Code?

Run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-add-pattern -a claude-code`. Or copy the skill folder (skills/nemotron-add-pattern in NVIDIA-NeMo/Nemotron) into .claude/skills/nemotron-add-pattern in your project. Claude Code loads it when a task matches its description.

How do I install Nemotron Add Pattern in Codex?

Run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-add-pattern -a codex`. Or copy the skill folder (skills/nemotron-add-pattern in NVIDIA-NeMo/Nemotron) into .agents/skills/nemotron-add-pattern in your project. Codex loads it when a task matches its description.

Can I use Nemotron Add Pattern in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-add-pattern -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemotron-add-pattern, .gemini/skills/nemotron-add-pattern, .github/skills/nemotron-add-pattern and .opencode/skills/nemotron-add-pattern in your project.

What does Nemotron Add Pattern need to run?

Going by SKILL.md and its folder, Nemotron Add Pattern needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Nemotron Add Pattern access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nemotron Add Pattern safe to install?

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.

What licence does Nemotron Add Pattern use?

Nemotron Add Pattern is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemotron Add Pattern use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemotron Add Pattern?

Skills that share tags, products or a category with Nemotron Add Pattern: Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face LLM Trainer (huggingface/skills, 11k stars), Sentence-Transformers Training Router (huggingface/skills, 11k stars) and Dataset Evaluation (awslabs/agent-plugins, 915 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemotron Add Pattern?

NVIDIA-NeMo (a GitHub organization) maintains it in NVIDIA-NeMo/Nemotron, which has 2,137 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 6, 2026.

Source: NVIDIA-NeMo/Nemotron on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.