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Tao Validate Dataset Format

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Review and audit
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do not use for non-DAFT formats. Use when the user asks to validate a DAFT dataset, check DAFT schema, validate a TAO dataset format, or run `tao-daft validate`.
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides an interface to the tao-daft validate command, enabling users to verify the integrity of NVIDIA TAO DAFT datasets. It guides the user through discovering supported dataset formats, identifying the correct validation subcommands, and executing checks against local dataset paths. The skill ensures that validation is performed according to the specific requirements of the installed version of the nvidia-tao-daft package.

What it does

  • Checks dataset structure, schema, and cross-references for DAFT-formatted data.
  • Discovers available validation formats via the tao-daft CLI.
  • Supports validation of individual datasets or entire directory trees.
  • Provides guidance on identifying dataset formats based on directory markers.
  • Interprets validation results and failure codes from the CLI output.

When to use it

  • When you need to verify that a dataset conforms to the DAFT format requirements.
  • When you encounter schema or cross-reference errors in a TAO DAFT dataset.
  • When you need to confirm the validity of a dataset before running further TAO operations.
  • When you are unsure which validation flags are required for a specific DAFT format.

Advantages

  • Uses the official CLI as the source of truth for validation specifications.
  • Helps prevent errors by guiding the user through format-specific flag discovery.
  • Automates the process of checking complex dataset trees.
  • Provides clear feedback on validation success or failure.

Usage examples

“Validate my DAFT dataset located at /data/my_dataset.”
The agent will identify the dataset format, run the appropriate tao-daft validate command with the --path flag, and report the validation results.
“How do I check the schema for a metropolis-v3.0 dataset?”
The agent will list the available flags for the metropolis-v3.0 format and execute the validation command on the specified path.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/tao-validate-dataset-format/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-validate-dataset-format.zip -d .agents/skills

Documentation by NVIDIA Corporation

Validate 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

tao-daft validate <format> --path <dataset-or-parent-dir>

<format> is a positional subcommand (e.g. metropolis-v3.0, cosmos-reason-v1.0); --path is required. Discover supported formats and per-format flags via tao-daft validate --help and the leaf --help (see "CLI conventions" below).

Preflight

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 validator formats before choosing a format slug, then run validation with the target passed through --path:

tao-daft --version
tao-daft validate --help
tao-daft validate <format> --help
tao-daft validate <format> --path /path/to/daft-dataset

Purpose

Drive tao-daft validate against a DAFT dataset (or a tree of them). The CLI is the spec; the skill picks subcommand + flags and explains the result.

Trigger when the user mentions "TAO DAFT", "DAFT format", validating a DAFT dataset, schema/cross-reference errors, or tao-daft validate. Do not trigger for non-DAFT layouts (COCO, YOLO, Data Factory JSONL), or for tao-daft info / tao-daft convert — those have their own skills.

If the user's opening is ambiguous, run a few --help commands first to ground yourself, then come back and confirm the task.

Prerequisites

  • nvidia-tao-daft installed (pip install nvidia-tao-daft; the wheel is enough, no source repo). Confirm with tao-daft --version.

  • A DAFT dataset, or a parent directory of them, on local disk.

Instructions

CLI conventions

tao-daft is nested argparse subcommands. Names and flags drift across versions, so discover the current surface from --help rather than trusting any list in this doc.

  1. Format is a positional subcommand, not --format: tao-daft validate <format> [flags]. List current formats via tao-daft validate --help; slugs look like metropolis-v3.0, cosmos-reason-v1.0.

  2. Target is --path PATH, not positional. It accepts a single dataset/scene or a parent directory — the validator walks the tree.

  3. Flags are per-format; run the leaf help, e.g. tao-daft validate metropolis-v3.0 --help, before choosing them. Don't assume a flag from one format exists on another.

So the loop is: tao-daft --version → tao-daft validate --help → pick format (infer if unspecified, see below) → tao-daft validate <format> --help → run → interpret.

Format inference

Use directory markers, not filenames:

  • meta.json next to media/ and text/ ⇒ cosmos-reason-v1.0.

  • A directory (or nested directories) containing contextual/, typically alongside raw/ and task/ ⇒ metropolis-v3.0.

  • Neither marker present ⇒ ask the user; do not guess.

Reading errors

The CLI ends every run with a VALIDATION RESULTS block, then ✅ VALIDATION PASSED or ❌ VALIDATION FAILED, and exits non-zero on failure (safe to chain in scripts).

Output can be large on big trees — capture the full output to a file and read it in slices rather than scrolling inline.

Limitations

  • Validates DAFT only. Non-DAFT layouts (COCO, YOLO, Data Factory JSONL, etc.) belong in the upstream converter skills.

  • Supported formats are whatever tao-daft validate --help reports for the installed version; older slugs may have been retired.

  • Covers validate only. Defer to the dedicated skills for tao-daft info and tao-daft convert.

  • Don't reimplement validation in Python; the CLI is the spec.

Troubleshooting

  • tao-daft: command not found — wheel not installed in the active env. pip install nvidia-tao-daft; verify tao-daft --version.

  • error: argument --path is required — path passed positionally. Move it behind --path.

  • invalid choice: '<format>' — slug isn't wired up in this version. Re-run tao-daft validate --help and pick from the list.

  • Auto-detection (raw type / contextual set) is wrong — override via the format's scope-restriction flag; discover the name from the leaf --help.

  • CI wants warnings to fail — add --strict.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs Python

The author says: Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft).

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Corporation
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/tao-validate-dataset-format · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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