Official agent skill

Tao Convert Dataset Format

by NVIDIA in NVIDIA/skills

Run tao-daft convert to convert NVIDIA TAO DAFT datasets between supported formats.

OfficialApache-2.0Auto-check: notes

Install Tao Convert Dataset Format

skills CLI
$ npx skills add NVIDIA/skills --skill tao-convert-dataset-format -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills tao-convert-dataset-format --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-convert-dataset-format .claude/skills/tao-convert-dataset-format && 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-convert-dataset-format
GitHub stars
3.5k
Token cost
~1.3k tokens
SKILL.md length
559 words
Files
6
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run tao-daft convert to convert NVIDIA TAO DAFT datasets between supported formats.

  • Works in 4 steps: Source and target are both positional… → Path and output are flags — --path PATH… → path accepts both granularities — a… → …
  • The user asks to convert a DAFT dataset
  • SKILL.md covers Quick start, Preflight, Quick Start and Purpose, plus 4 more sections
  • Calls python

What it does

Tao Convert Dataset Format is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run tao-daft convert to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run tao-daft convert.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `BENCHMARK.md`, `config/skillspector-baseline.yaml` and `evals/evals.json`). Compatibility notes: Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).

It works with NVIDIA AI Platform. 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

  • The user asks to convert a DAFT dataset
  • Change DAFT format
  • Change a TAO dataset format
  • Run tao-daft convert

Example prompts

  • “/tao-convert-dataset-format”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).
  • Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

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

  1. Source and target are both positional subcommands, not
  2. Path and output are flags — --path PATH (source),
  3. path accepts both granularities — a single scene/dataset
  4. Per-pair flags live at the leaf — flag sets differ between

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. 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

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    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 Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).

    From compatibility in the SKILL.md frontmatter.

Context cost

Tao Convert Dataset Format loads about 1.3k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 559 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~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

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 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 559 words, ~1,303 tokens.

Download SKILL.mdSave it as .claude/skills/tao-convert-dataset-format/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
tao-convert-dataset-format
description
Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run `tao-daft convert`.
allowed-tools
Read, Bash
compatibility
Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).
license
Apache-2.0
metadata.author
NVIDIA Corporation
metadata.version
0.1.0
tags
tao-daft, dataset, conversion, vlm, cosmos-reason

Convert a TAO DAFT Dataset

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).

Quick start

bash
tao-daft convert <source-format> <target-format> --path <input> --output <output>

Source and target are positional subcommands; --path and --output are flags. Discover the supported formats and per-pair flags from the leaf --help (see "CLI conventions" below).

Preflight

bash
python -c "import nvidia_tao_daft" 2>/dev/null || {
  echo "MISSING: tao-daft not installed. Run:"
  echo "  pip install nvidia-tao-daft"
  exit 1
}

Quick Start

Discover the installed CLI surface before choosing format slugs, then run the leaf conversion command with explicit --path and --output flags:

bash
tao-daft --version
tao-daft convert --help
tao-daft convert <source-format> --help
tao-daft convert <source-format> <target-format> --path /path/to/daft --output /path/to/converted

Purpose

Drives tao-daft convert to transform a DAFT dataset (or a tree of them) between supported formats. The CLI does the real work; the skill picks the right source/target pair and flags, then explains the result.

Trigger on: converting a DAFT dataset, packaging DAFT QA / summarization / temporal tasks for VLM training, producing a meta.json-style training set, or the command tao-daft convert. Do not trigger for non-DAFT → DAFT conversion (COCO, YOLO, Data Factory JSONL) — redirect to the upstream nvidia-tao-daft repo's converter skills.

If the user opens ambiguously, run a few --help calls first.

Prerequisites

  • nvidia-tao-daft installed (wheel only, not the source repo). Confirm with tao-daft --version.
  • A DAFT dataset, or a parent directory containing many, on local disk.

Instructions

CLI conventions

tao-daft is nested argparse subcommands. The conventions below are stable across versions even when format names or flags change, so always discover the current surface from --help rather than relying on names this doc happens to mention.

  1. Source and target are both positional subcommands, not --from/--to: tao-daft convert <source> <target> [flags]. Format slugs are versioned, lowercase, dot-separated (metropolis-v3.0, cosmos-reason-v1.0, ...).
  2. Path and output are flags — --path PATH (source), --output OUTPUT (destination). Both required at the leaf; passing positionally fails.
  3. --path accepts both granularities — a single scene/dataset or a parent directory; the converter walks the tree.
  4. Per-pair flags live at the leaf — flag sets differ between targets (e.g. media-handling). Always check the leaf --help.

Operating procedure:

  1. tao-daft --version — confirm install, pin version in any report.
  2. tao-daft convert --help — list supported source formats.
  3. tao-daft convert <source> --help — list valid targets for that source.
  4. Infer source from layout (same directory markers as the tao-validate-dataset-format skill's "Format inference"). If you cannot infer or the target is unspecified, ask.
  5. tao-daft convert <source> <target> --help — pick flags for the user's intent (task subset, media copy vs reference, metadata).
  6. Execute, then interpret (see below).
Show full SKILL.md (162 more words)Show less
Reading output

Per-scene progress prints to stdout; non-zero exit on failure. The converted dataset is written under --output — spot-check it with the tao-validate-dataset-format skill before training. For large trees, capture the full output and partial-read if huge.

Limitations

  • DAFT-supported source formats only. For non-DAFT layouts use the upstream repo's converter skills.
  • Supported pairs are whatever --help reports for the installed version — don't pass an unconfirmed pair.
  • Source and target are positional; --path / --output are flags.
  • convert only — validate and info have their own skills.
  • Do not reimplement conversion in Python; the CLI is the spec.

Troubleshooting

  • tao-daft: command not found — wheel not installed; pip install nvidia-tao-daft, verify with tao-daft --version.
  • error: argument --path/--output is required — passed positionally; move behind the flag.
  • invalid choice: '<format>' — slug not wired up in this version. Re-run the relevant --help.
  • Output rejected by tao-daft validate — re-check per-pair flags (media handling, task subset) via leaf --help; a misset flag often produces a structurally valid but semantically wrong target.

© 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 5 other files in skills/tao-convert-dataset-format of NVIDIA/skills.

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

Open the folder on GitHubat commit 67a13c0

Compare with similar skills

Tao Convert Dataset Format 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.

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Nemoclaw Maintainer Normalize Title TagsNVIDIA/NemoClaw23k—~693Automated safety check: PassApache-2.0

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Questions about Tao Convert Dataset Format

What does Tao Convert Dataset Format do?

Run tao-daft convert to convert NVIDIA TAO DAFT datasets between supported formats. Tao Convert Dataset Format is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run tao-daft convert to convert NVIDIA TAO DAFT datasets between supported formats.

When should I use Tao Convert Dataset Format?

Tao Convert Dataset Format fits situations like: the user asks to convert a DAFT dataset; change DAFT format; change a TAO dataset format; run tao-daft convert.

How do I install Tao Convert Dataset Format in Claude Code?

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

How do I install Tao Convert Dataset Format in Codex?

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

Can I use Tao Convert Dataset Format 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-convert-dataset-format -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-convert-dataset-format, .gemini/skills/tao-convert-dataset-format, .github/skills/tao-convert-dataset-format and .opencode/skills/tao-convert-dataset-format in your project.

What does Tao Convert Dataset Format need to run?

Going by SKILL.md and its folder, Tao Convert Dataset Format needs the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft)..

Does Tao Convert Dataset Format 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 Convert Dataset Format 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 Convert Dataset Format use?

Tao Convert Dataset Format 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 Convert Dataset Format use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tao Convert Dataset Format?

Skills that share tags, products or a category with Tao Convert Dataset Format: Skill Inspector (NVIDIA/SkillSpector, 20k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Embeddings via 9Router (decolua/9router, 30k stars) and NEAR AI Cloud Private Inference (internet-court/internet-court-skill, 6.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tao Convert Dataset Format?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 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.