Atlas / Skills / nvidia-nemo / Nemotron Customize

Nemotron CustomizeSAFE

skills/nvidia-nemo/nemotron-customize

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

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
0.1.1
Hosts
1 documented
License
Apache-2.0
Stars
2,139
01

Overview

From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.

Per-step extracts of upstream library documentation. Load these only after the bundled catalog/run/artifact references have selected a step and an action needs real library API detail.

Lookup

index.toml maps (step_id, intent) → pack file. The Act phase reads this once and dispatches packs to per-stage sub-agents.

Provenance

These packs are not the step catalog. For routing and normal execution, read:

  • ../CATALOG.md
  • ../ARTIFACTS.md
  • ../COMMANDS.md
  • ../PATTERNS.md
  • ../HARDWARE.md

Each *.txt file is a snapshot of upstream docs + selected source files from one of:

These packs are curated summaries for agent grounding. They are intentionally short and should point agents back to bundled references first, then to the repo step manifest, config, runner, and active profile TOML for live verification.

Read from source at commit 441e9a359902OBSERVED · 2026-10-09
02

Install

Commands as the repository documents them. They are shown, not run.

uv run nemotron steps list --json
uv run nemotron steps list --json --category <category>
uv run nemotron steps show <step_id>
uv run nemotron steps run <step_id> -c <config-or-path> --dry-run
uv run nemotron steps run <step_id> -c <config-or-path> --dry-run --batch <profile>
uv run nemotron steps run <step_id> -c <config-or-path> --batch <profile>
03

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
codexmentioned
04

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: nemotron-customize
description: "Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding tasks."
version: 0.1.1
license: Apache-2.0
metadata:
  version: 0.1.1
  author: NVIDIA Nemotron Team <[email protected]>
  tags:
    - nemotron
    - customization
    - training
    - pipelines
---

# nemotron-customize

IMPORTANT: Read this file before answering any `nemotron-customize`,
Nemotron customization, Curator curation, translation, SFT, PEFT, RL,
conversion, optimization, checkpoint or existing/hosted-endpoint evaluation, or
multi-step pipeline request. This applies whether the user names one step or
asks you to compose several steps into a pipeline.

Evaluation requests count even when no training is involved: "evaluate",
"benchmark", "smoke test", or "score" an existing/hosted endpoint, an API/model
ID, or a deployed model all route to `eval/model_eval`. Read this skill for
those too.

## Purpose

Turn a model-customization request into a repo-native Nemotron step pipeline.
Plan the DAG, validate artifact wiring, and create only the YAML/config files
needed to run existing steps.

Use this skill only for inspecting, configuring, validating, running, or
submitting existing Nemotron steps or multi-step training/customization
pipelines. For frontend, dashboard, visualization, generic ML advice,
billing/access, or unrelated coding tasks, stop with a short scope note and do
not inspect the step catalog or edit files in that turn.

## Prerequisites

- A checkout of the Nemotron repo with `src/nemotron/steps/` present; run from
  the repo root.
- `uv` available to invoke `uv run nemotron steps ...`.
- For remote execution: an env profile TOML (`NEMOTRON_ENV_FILE` or
  `env*.toml`) with a section matching the selected step.
- For hosted services (translation, hosted eval): the auth environment variable
  expected by the step (for example `NVIDIA_API_KEY`), exported in the
  environment — never inlined or committed.
- User-provided concrete values (model/checkpoint, data paths, output dir,
  hardware/GPU count) before any command is presented as runnable.

## Limitations

- Does not invent new catalog steps. When no existing step, runner, recipe, CLI,
  or config can satisfy the request, it names the gap (Explorer mode) instead of
  fabricating a step.
- Produces YAML/config for existing steps; new Python/shell is out of scope
  except in Explorer mode after the gap is approved.
- Not for deployment-only/serving, frontend, dashboards, generic ML advice, or
  non-Nemotron tasks.
- Does not guess concrete values (paths, model IDs, GPU counts, profiles); it
  asks or returns `Blocked` when they are missing.

## Core Rule

Use bundled references first. The `references/` folder is the first decision
surface for routing, artifacts, patterns, hardware heuristics, and command
shape. Use `src/nemotron/steps/...` only as a live verification/fallback source
when you need exact current config fields, manifests, runner imports, or details
missing from bundled references.

If sources disagree:

1. Checked live repo files win for exact execution.
2. Bundled references win for initial routing and planning.
3. Upstream docs/context packs are used only for exceptional code generation
   or library API details.

## Before You Begin

- Read this `SKILL.md` workflow and the relevant bundled reference before
  opening repo source files.
- Route from `references/CATALOG.md` and `references/ARTIFACTS.md` before any
  broad repo exploration. Once a route is determined, verify only the selected
  live step/config/env files needed for the answer.
- Do not emit commands with fake paths, placeholder model IDs, guessed task IDs,
  guessed batch profiles, or default auth variable names presented as facts.
  Ask for missing concrete values or return a `Blocked` handoff.
- Use `references/COMMANDS.md` as the authoritative checklist before
  finalizing configs or execution commands.
- For pipeline requests, plan before editing. Do not create or modify files
  until the DAG, artifact edges, required inputs, and validation checks are
  stated and approved.
- For one-shot command requests, prefer a complete parameterized command in one
  response over exploratory prose, but only after required inputs are known.
  If the user already provides the needed values and asks for only a command,
  answer with the command first and keep explanation minimal.
- Output discipline (keeps responses tight): emit one command block per step,
  include only flags the step actually defines, and add no speculative or
  invented flags. Keep narrative to a few lines — the command plus the required
  safety/profile callouts, not a tutorial. Do not restate reference content the
  user did not ask for.
- Do not spawn subagents for one-shot command lookup. Use the bundled command
  reference directly; verify only the selected step if needed.

## Safety

Keep Bash scoped to repo-safe commands such as `uv run nemotron steps ...`,
targeted tests, `git status/diff`, and config validation. Never run environment
dumps (`env`, `printenv`, broad `export`) or commands that expose secret values.
For remote submissions, destructive changes, or expensive launches, confirm
before execution.

When inspecting env/config files, avoid printing whole files that may contain
secrets. Use targeted reads, report only section names and env-var names, and
redact values for fields
05

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-09 · audit v0.4.1 · source sha 441e9a359902full audit observations/trust-audit/skill/nvidia-nemo__nemotron-customize.json · Report an issue / request a re-scan
06

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-09441e9a359902SAFEB89first audit
07

Questions

What does the Nemotron Customize skill do?

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

Is Nemotron Customize safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Nemotron Customize access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Nemotron Customize work with?

Its documentation mentions codex. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (441e9a359902), read on 2026-10-09. The repository is watched, and a new audit runs when it changes — this is the first audit.

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