nemotron-add-model
Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe…
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.
$ npx -y skills add nvidia-nemo/nemotron --skill nemotron-add-step --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/nemotron-add-stepContext 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.
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.
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.
Concise. Checklist-first. Ask for missing facts before writing files.
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.
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Four phases. Always in this order.
Read these first:
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:
Create the step directory:
Create these files:
If needed, also create:
For `step.toml`, include:
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`:
Always run both commands after generation:
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
Show:
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Open and efficient models for agentic AI. Training recipes, deployment guides, and use-case examples for the Nemotron family.
Repo: nvidia-nemo/nemotron
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