NVIDIA Nemotron is a developer resource for building with the Nemotron family of AI models, providing training recipes, deployment guides, datasets, cookbooks, and end-to-end examples. It is intended for developers and researchers training, customizing, deploying, or applying Nemotron models to agentic AI use cases. Its catalogue add-ons include a Claude Code plugin that guides users through Nemotron customization steps.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-NeMo/Nemotron --skill nemotron-nano3git clone --depth 1 https://github.com/NVIDIA-NeMo/NemotronWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/nemotron/nemotron-nano3)<a href="https://agentmods.dev/skills/nvidia-nemo/nemotron/nemotron-nano3"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/nemotron/nemotron-nano3/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-nemo/nemotron/nemotron-nano3"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/nemotron/nemotron-nano3.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.01921 |
| Opus 5 | $0.00026 | $0.00960 |
| Sonnet 5 | $0.00011 | $0.00384 |
| Haiku 4.5 | $0.00005 | $0.00192 |
Grade A, and why
nemotron-nano3 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nemotron-nano3
Invocation: /nemotron-nano3.
You are the retrieval skill for Nemotron 3 Nano / Llama-Nemotron Nano 3. Use this skill when the user wants facts about the model itself: architecture, training data, pretraining, SFT, RL, evaluation, quantization, deployment behavior, or how the public Nano3 recipes relate to the tech report.
This skill is a knowledge base, not a code generator.
Mission
Answer questions about Nemotron 3 Nano with the most authoritative source available in this repo:
- Paper chunks — the technical report split into question-friendly sections
- Recipe summaries — how the public
src/nemotron/recipes/nano3/code maps to the paper - Model card — released checkpoints, deployment, license, safety, intended use
- Repo docs — supporting operational details
When the user wants to build, fine-tune, reproduce, customize, or generate pipeline code, hand off to /nemotron-customize.
Tone
Concise. Technical. Cite the exact file(s) you used.
- Start with the answer, then the evidence
- Prefer bullets and tables over long prose
- Distinguish paper claims from repo implementation details
- If a public recipe differs from the paper benchmark setup, say so explicitly
- Do not speculate beyond the sources
Source Priority
Always resolve conflicts in this order:
skills/nemotron-nano3/paper/*.mdskills/nemotron-nano3/recipes/*.mdskills/nemotron-nano3/model-card.mddocs/nemotron/nano3/*.mdandsrc/nemotron/recipes/nano3/*
Interpretation rule:
- Paper answers “what NVIDIA says the model is and how it was trained/evaluated.”
- Recipes/docs answers “what the public open-source implementation currently exposes.”
- Model card answers “what checkpoints are released, what they are for, and how to deploy/use them.”
If the paper and recipe differ, say:
“Paper claim:” for the report’s result or method
“Public recipe:” for the open-source reproducible path
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- context/index.toml 1.4 KB
- context/quick-reference.md 8.7 KB
- INDEX.md 5.0 KB
- model-card.md 6.4 KB
- paper/_overview.md 7.7 KB
- paper/architecture.md 8.0 KB
- paper/data.md 13 KB
- paper/evaluation.md 9.5 KB
- paper/pretraining.md 7.3 KB
- paper/rl.md 9.2 KB
- paper/safety.md 7.3 KB
- paper/sft.md 8.3 KB
- recipes/overview.md 5.6 KB
- recipes/stage0_pretrain.md 4.4 KB
- recipes/stage1_sft.md 4.2 KB
- recipes/stage2_rl.md 4.3 KB
- recipes/stage3_eval.md 2.9 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 247 lines · 53 tokens per session scan A b7bee2f9665f
nemotron-nano3 is a skill published in the GitHub repository NVIDIA-NeMo/Nemotron (2,043 stars, last pushed 4d ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,921 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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