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Official agent skill

Tao Train Single Step

by NVIDIA in NVIDIA/skills

Standard single-step train/eval/export workflow for any TAO model.

OfficialApache-2.0Auto-check: notes

Install Tao Train Single Step

skills CLI
$ npx skills add NVIDIA/skills --skill tao-train-single-step -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills tao-train-single-step --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/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tao-train-single-step .claude/skills/tao-train-single-step && 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
tao-train-single-step
GitHub stars
3.5k
Token cost
~1.2k tokens
SKILL.md length
518 words
Files
7 (incl. references)
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Standard single-step train/eval/export workflow for any TAO model.

  • Works in 3 steps: train — executed through AutoML when the… → eval — executed if eval_dataset_uri is… → export — optional, on user request after…
  • Training a TAO model on a dataset without iterative data augmentation
  • SKILL.md covers Steps, Prerequisites and Launch Intake
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tao Train Single Step is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md for action specifics and through tao-launch-workflow for platform/credentials/dataset intake.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yaml` and `evals/evals.json`). Compatibility notes: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.

The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Training a TAO model on a dataset without iterative data augmentation
  • Phrases include single train run
  • Train then evaluate then export
  • Plain TAO training

Example prompts

  • “single train run”
  • “train then evaluate then export”
  • “plain TAO training”
  • “/tao-train-single-step”

Requirements

  • Python 3
  • Docker
  • Compatibility (from SKILL.md): Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.
  • Pre-approved tools (allowed-tools): Read, Bash, Write

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. train — executed through AutoML when the selected model has
  2. eval — executed if eval_dataset_uri is resolved
  3. export — optional, on user request after training

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Bash
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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.

  • Compatibility

    Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.

    From compatibility in the SKILL.md frontmatter.

Context cost

Tao Train Single Step loads about 1.2k tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 518 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Bash, Write

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/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 518 words, ~1,191 tokens.

Download SKILL.mdSave it as .claude/skills/tao-train-single-step/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
tao-train-single-step
description
Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset without iterative data augmentation, AutoML, or DEFT loops. Trigger phrases include "single train run", "train then evaluate then export", "plain TAO training", "normal training", "no AutoML", "skip the loop". Routes through the per-model SKILL.md for action specifics and through `tao-launch-workflow` for platform/credentials/dataset intake.
allowed-tools
Read, Bash, Write
compatibility
Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.
license
Apache-2.0
metadata.author
NVIDIA Corporation
metadata.version
0.1.0
tags
training, single-step, generic

Normal Train

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Standard supervised fine-tuning: train a model on a labeled dataset, optionally evaluate, then optionally export. The most common TAO workflow for adapting a pretrained model to a new dataset.

Steps

  1. train — executed through AutoML when the selected model has automl_enabled: true and automl_policy is on; set automl_policy=off for a plain single training run
  2. eval — executed if eval_dataset_uri is resolved
  3. export — optional, on user request after training

Prerequisites

The selected model skill's resolved container_image is the default training runtime. Do not replace it with a host venv, uv environment, generic training image, or hand-written trainer unless the user explicitly requests that execution mode. SDK/controller Python environments are control-plane-only; the model action remains container-backed.

Required
  • model: A compatible TAO model (e.g., clip, nvdinov2, grounding_dino)
  • train_dataset_uri: URI of the training dataset (e.g., s3://bucket/train/)
  • platform: Discover the execution platforms from the installed platform skills (tao-run-on-docker / -slurm / -kubernetes / -brev, plus any external one); on a runtime that surfaces only the core router skills, read skills/platform/tao-run-on-*/SKILL.md frontmatter.
  • container image confirmation: resolve the default image from the selected model/action config, show it to the user, and require confirmation or image=<override> before creating runner files or submitting training.
Optional
  • eval_dataset_uri: Some model skills mark this as required — check the resolved model skill before treating it as optional.
  • base_checkpoint: If not provided, defaults to the NGC pretrained checkpoint listed in the model skill, or trains from scratch if no NGC checkpoint exists.
  • automl_policy: on by default; set off to bypass model-level AutoML for this run while leaving model metadata unchanged. Use only on / off in new launch settings.
  • image override: Use image=<override> to pin a specific TAO toolkit build after reviewing the resolved default.
Show full SKILL.md (214 more words)Show less

Launch Intake

After the user confirms they want this standard train/eval/export workflow, ask which supported platform they intend to run on. Discover the execution platforms from the installed platform skills (tao-run-on-docker / -slurm / -kubernetes / -brev, plus any external one); on a runtime that surfaces only the core router skills, read skills/platform/tao-run-on-*/SKILL.md frontmatter.

Before creating a plain train runner, inspect the selected model's metadata with scripts/list_tao_models.py --scope automl --format json or read skills/models/<network>/references/skill_info.yaml. If automl_enabled is true and the helper reports a valid train schema for that model, route the train stage through skills/applications/tao-run-automl by default. Only stay on the plain train path when automl_policy=off, the user explicitly asks for no HPO/AutoML, or AutoML is enabled but not runnable because the model's train schema is not packaged yet.

Also ask whether long-running monitoring should stay enabled and how many minutes between status updates. Defaults: enabled, 5 minutes.

After the model/action are known, run scripts/resolve_tao_image.py --model <network> --action train --format text and ask whether to use the resolved image or an image=<override>. Do not create the tao-train-single-step runner until the image is confirmed.

After platform selection, read the chosen platform skill's ## Credentials section and references/skill_info.yaml (required_credentials / credential_groups) and ask only for credentials relevant to that platform, plus any selected-model credentials. Do not ask for unrelated platform credentials.

© NVIDIA, 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

SKILL.md and 6 other files (references) in skills/tao-train-single-step of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • config/skillspector-baseline.yaml
  • evals/evals.json
  • references/skill_info.yaml
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Tao Train Single Step next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tao Train Single Step compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tao Train Single Step this skillNVIDIA/skills3.5k—~1.2kAutomated safety check: NotesApache-2.0
Eval Harnessaffaan-m/ECC274k—~2.2kAutomated safety check: PassMIT
Evalalirezarezvani/claude-skills28k1 repos~618Automated safety check: PassMIT
Eval Harnessaffaan-m/ECC274k1 repos~1.7kAutomated safety check: PassMIT
Eval-Driven Development Harnessaffaan-m/ECC274k—~1.5kAutomated safety check: PassMIT
Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs13k3 repos~2.7kAutomated safety check: PassMIT

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Questions about Tao Train Single Step

What does Tao Train Single Step do?

Standard single-step train/eval/export workflow for any TAO model. Tao Train Single Step is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Standard single-step train/eval/export workflow for any TAO model.

When should I use Tao Train Single Step?

Tao Train Single Step fits situations like: training a TAO model on a dataset without iterative data augmentation; phrases include single train run; train then evaluate then export; plain TAO training.

How do I install Tao Train Single Step in Claude Code?

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

How do I install Tao Train Single Step in Codex?

Run `npx skills add NVIDIA/skills --skill tao-train-single-step -a codex`. Or copy the skill folder (skills/tao-train-single-step in NVIDIA/skills) into .agents/skills/tao-train-single-step in your project. Codex loads it when a task matches its description.

Can I use Tao Train Single Step 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/skills --skill tao-train-single-step -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tao-train-single-step, .gemini/skills/tao-train-single-step, .github/skills/tao-train-single-step and .opencode/skills/tao-train-single-step in your project.

What does Tao Train Single Step need to run?

SKILL.md names no scripts, command-line tools or credentials: Tao Train Single Step is instructions for the agent only. Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: Read, Bash, Write. Compatibility (from SKILL.md): Requires docker + nvidia-container-toolkit. Workflows declare additional requirements..

Does Tao Train Single Step access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tao Train Single Step safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Tao Train Single Step use?

Tao Train Single Step is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tao Train Single Step use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 141 tokens, read only when the agent opens those files.

What are the alternatives to Tao Train Single Step?

Skills that share tags, products or a category with Tao Train Single Step: Eval Harness (affaan-m/ECC, 274k stars), Eval (alirezarezvani/claude-skills, 28k stars), Eval Harness (affaan-m/ECC, 274k stars) and Eval-Driven Development Harness (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tao Train Single Step?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

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