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-TAO/tao-skill-bank --skill tao-validate-dataset-formatgit clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bankWrote 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-tao/tao-skill-bank/tao-validate-dataset-format)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-validate-dataset-format"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-validate-dataset-format/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-tao/tao-skill-bank/tao-validate-dataset-format"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-validate-dataset-format.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 | $0.00077 | $0.01362 |
| Opus 5 | $0.00039 | $0.00681 |
| Sonnet 5 | $0.00015 | $0.00272 |
| Haiku 4.5 | $0.00008 | $0.00136 |
Grade A, and why
tao-validate-dataset-format 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 13d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validate a TAO DAFT Dataset
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill 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-daftinstalled (pip install nvidia-tao-daft; the wheel is enough, no source repo). Confirm withtao-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.
What ships with it
4 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.
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.
- 13d ago First seen · 136 lines · 77 tokens per session scan A 62b5266a67fa
tao-validate-dataset-format is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 77 tokens to every session and 1,362 once invoked, about $0.0004 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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