Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do
Scanned 9/3/2026
Install to Claude Code
npx -y skills add NVIDIA/skills --skill tao-validate-dataset-format --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Tao Validate Dataset Format?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/nvidia-tao-validate-dataset-format)More formats (shields.io, HTML) on the badges page.
---
name: tao-validate-dataset-format
description: 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`.
license: Apache-2.0
compatibility: Requires Python 3.10+ and the nvidia-tao-sdk package (pip install nvidia-tao-daft).
metadata:
author: NVIDIA Corporation
version: "0.1.0"
allowed-tools: Read Bash
tags:
- tao-daft
- dataset
- validation
- schema
---
# 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
```bash
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
```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 validator formats before choosing a format slug, then
run validation with the target passed through `--path`:
```bash
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`.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!