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-convert-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-convert-dataset-format)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-convert-dataset-format"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-convert-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-convert-dataset-format"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-convert-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 | $0.00071 | $0.01387 |
| Opus 5 | $0.00036 | $0.00694 |
| Sonnet 5 | $0.00014 | $0.00277 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
tao-convert-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 12d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convert 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 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
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:
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-daftinstalled (wheel only, not the source repo). Confirm withtao-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.
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.
- 12d ago First seen · 140 lines · 71 tokens per session scan A 26fef280f514
tao-convert-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 71 tokens to every session and 1,387 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.
Other skills, from other repositories
trulens-dataset-curation
Create and curate evaluation datasets with ground truth for TruLens.
evalscope
LLM evaluation & inference performance testing via the evalscope CLI. Translates natural language requests into evalscope commands for: (1) Model accuracy evaluation — runs registered benchmarks against local checkpoints or API endpoints (OpenAI-compatible, Anthropic, LiteLLM); (2) Performance stress testing — TTFT…
digital-health-clinical-asr-build
Stage 2 of the Clinical ASR Flywheel. Use when curating clinical terms, tagging IPA, and synthesizing a NeMo manifest. NOT for scoring (use /digital-health-clinical-asr-eval).
clawhub
Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool.
vision
Nested swiss-knife reference for image understanding — a decision tree that routes between three paths depending on what the agent has access to: (1) the built-in vision tool if the LLM provider supports image input, (2) the sibling minimax-cli reference if a usable MiniMax preset/key slot is available, or (3) a local…
vss-build-vision-ai
Add agent-ready vision capabilities — dense captioning, detection, search, alerting, summarization — to an agent or application through a customizable, self-contained vision stack built on the NVIDIA VSS Blueprint. Use this skill when a developer or agent wants to give their app vision: pick capabilities via guided…