Skip to content
Machine Learning
Skill

/nemotron-add-step

Add a new step under src/nemotron/steps/<category>/<step_id>/ — manifest (step.toml), runner glue, configs, and per-step README.md. Use when extending the catalog so /nemotron-customize can route to it.

BOOST
From plugin
nemotron
2.1k10 skills
Install
$ npx -y skills add nvidia-nemo/nemotron --skill nemotron-add-step --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/nemotron-add-step

Context preview

The summary Claude sees to decide when to auto-load this skill.

Add a new step under src/nemotron/steps/<category>/<step_id>/ — manifest (step.toml), runner glue, configs, and per-step README.md. Use when extending the catalog so /nemotron-customize can route to it.

SKILL.md

nemotron-add-step.SKILL.md
name: nemotron-add-step
description: Add a new step under src/nemotron/steps/<category>/<step_id>/ — manifest (step.toml), runner glue, configs, and per-step README.md. Use when extending the catalog so /nemotron-customize can route to it.

nemotron-add-step

Invocation: `/nemotron-add-step`.

You help contributors add a new step to the `nemotron-customize` step library without getting the manifest schema, starter configs, or validation flow 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 artifact types, stack choice, or reference paths
  • Never skip validation

Example — simple step:

user: Add a new eval backend for a NeMo Evaluator workflow.
assistant: I need six inputs before I write files:
1. One-sentence purpose
2. Category
3. Stack library
4. Consumed artifact types
5. Produced artifact types
6. The library example or script to reference

Then I'll create the step directory, write step.toml + starter configs, add step.py only if the library lacks a good standalone example, regenerate STEPS.md, and run `uv run pytest tests/steps -q`.

Example — new artifact type:

user: Add an sdg step that produces preference-pair JSONL.
assistant: `preference_jsonl` is not in `src/nemotron/steps/types.toml`.
I'll first confirm no existing type fits. If it really is new, I'll add a top-level type entry with `description` and the smallest correct `is_a` or `convert_to` relationship, then wire the new step to that type and run the step validations.

---

Workflow

Four phases. Always in this order.

1. Orient

Read these first:

  • `src/nemotron/steps/types.toml`
  • `src/nemotron/steps/sft/megatron_bridge/step.toml`
  • `src/nemotron/steps/sft/megatron_bridge/step.py`
  • `src/nemotron/steps/sft/guide.md`
  • `src/nemotron/steps/index.py`

Then ask the contributor: 1. What does this step do? (one sentence) 2. Which category? (`curate`, `sdg`, `translate`, `prep`, `pretrain`, `sft`, `peft`, `rl`, `optimize`, `eval`, `convert`, `benchmark`) 3. Which NVIDIA stack library? (Megatron-Bridge, AutoModel, NeMo-RL, NeMo Curator, Data Designer, NeMo Evaluator, Speaker, other) 4. What does it consume? (artifact types from `src/nemotron/steps/types.toml`) 5. What does it produce? (artifact types) 6. Does it introduce a new artifact type? 7. Is there an existing library example/script we should reference?

Use these repo conventions:

  • Step ids and directory names are snake_case, matching existing paths like `sft/megatron_bridge` and `eval/model_eval`.
  • `step.toml` uses `[step].id`, `name`, `category`, `description`, and `tags`.
  • `[[strategies]]` uses `when` / `then` / optional `skill`.
  • `[[errors]]` uses `name` / `recovery` / optional `skill`.
  • `types.toml` currently uses top-level artifact tables like `[checkpoint_hf]`, not a nested `[types.*]` layout.
  • `step.py` is optional. Only add it if the library does not already provide a good standalone reference.

2. Generate

Create the step directory:

  • `src/nemotron/steps/{category}/{step_name}/`

Create these files:

  • `src/nemotron/steps/{category}/{step_name}/step.toml`
  • `src/nemotron/steps/{category}/{step_name}/config/default.yaml`
  • `src/nemotron/steps/{category}/{step_name}/config/tiny.yaml`
  • `src/nemotron/steps/{category}/{step_name}/step.py` only if needed

If needed, also create:

  • `src/nemotron/steps/{category}/guide.md` if the category now has multiple steps and no guide exists yet
  • a new entry in `src/nemotron/steps/types.toml` if the step introduces a new artifact type

For `step.toml`, include:

  • `[step]` identity (`id`, `name`, `category`, `description`, `tags`)
  • `[[consumes]]`
  • `[[produces]]`
  • `[[models]]` when model choice matters
  • `[[parameters]]` for top pipeline-shaping knobs only
  • `[[strategies]]` with at least 2–3 useful recommendations
  • `[[errors]]` with common failure modes
  • `[reference]` pointing to real repo-relative library code/docs

Generation rules: 1. Follow the live schema from existing step manifests, not an invented variant. 2. Keep parameters short. Include only the knobs that affect planning, wiring, hardware choice, or output format. 3. Every local reference path in `[reference]` must resolve in this workspace; external library references should be stable upstream URLs. 4. If you add `step.py`, keep it thin and runnable. Include a PEP 723 `# /// script` header with `[tool.runspec]`. 5. Keep `step.py` at 30 lines or less unless a slightly longer wrapper is unavoidable. 6. `config/default.yaml` is the production starter config. 7. `config/tiny.yaml` is the quick smoke config. 8. If a new artifact type is required, add the smallest correct relation in `types.toml`:

  • `is_a` for implicit compatibility
  • `convert_to` only when an explicit converter step is required

3. Validate

Always run both commands after generation:

  • `uv run python src/nemotron/steps/index.py`
  • `uv run pytest tests/steps -q`

If either command fails: 1. Fix the actual schema, path, or type issue 2. Re-run the failing command 3. Do not present the result until both pass

4. Summarize

Show:

  • What was created
  • Every file added or changed
  • Whether `step.py` was created or intentionally omitted
  • Any new artifact types added to `types.toml`
  • The new step entry as rendered in `src/nemotron/steps/STEPS.md`

---

Boundaries

Do

  • Reuse the existing manifest pattern from `sft/megatron_bridge`
  • Reuse the existing guide pattern when a new `guide.md` is needed
  • Add `default.yaml` and `tiny.yaml` starter configs
  • Add or extend `types.toml` only when the step truly needs it
  • Run the two validation commands every time

Don't

  • Don't modify existing steps just to refactor or rename them
  • Don't modify anything inside `skills/nemotron-customize/` (`SKILL.md`, `act/*.md`, `examples/*.md`, or `context/*`) unless the user explicitly
Read more
Ships withnemotron

Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.

Get the whole plugin
Stats
2,137
Stars
433
Forks
Active
Maintenance
Jupyter Notebook
Language
Apache-2.0
License
1d ago
Last commit
1y ago
Created
18h ago
Added

Repo: nvidia-nemo/nemotron

Other skills on nemotron.