Tao Convert Dataset Format
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Tool integration
- Agent tooling
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides an interface to the tao-daft convert command, enabling users to transform DAFT-formatted datasets into different supported versions or formats. It emphasizes the use of the underlying CLI tool to ensure compatibility and accuracy, requiring users to discover supported format slugs and flags dynamically via the tool's help system. The skill is strictly for DAFT-to-DAFT conversions and requires the nvidia-tao-daft package.
What it does
- Executes the tao-daft convert command to transform dataset formats.
- Supports discovery of available source and target formats via CLI help.
- Handles path and output directory specifications for dataset conversion.
- Provides guidance on using per-pair flags for specific conversion requirements.
- Verifies the installation of the required nvidia-tao-daft package.
When to use it
- When you need to convert a DAFT dataset from one format to another.
- When preparing DAFT datasets for VLM training or specific task workflows.
- When you need to change the format of a TAO dataset.
- When you need to run the tao-daft convert command within an agentic workflow.
Advantages
- Ensures consistency by using the official CLI as the single source of truth.
- Supports tree-based conversion for large dataset directories.
- Provides a structured approach to discovering valid format pairs and flags.
- Reduces errors by enforcing correct flag usage for paths and outputs.
Usage examples
- “Convert my DAFT dataset at /data/input to the cosmos-reason-v1.0 format in /data/output.”
- The agent will identify the source format, verify the target, and execute the tao-daft convert command with the appropriate flags.
- “What formats can I convert my metropolis-v3.0 dataset into?”
- The agent will run tao-daft convert metropolis-v3.0 --help to list all valid target formats supported by the current installation.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/tao-convert-dataset-format/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-convert-dataset-format.zip -d .agents/skills
Documentation by NVIDIA Corporation
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/convertedPurpose
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.
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, ...).Path and output are flags —
--path PATH(source),--output OUTPUT(destination). Both required at the leaf; passing positionally fails.--pathaccepts both granularities — a single scene/dataset or a parent directory; the converter walks the tree.Per-pair flags live at the leaf — flag sets differ between targets (e.g. media-handling). Always check the leaf
--help.
Operating procedure:
tao-daft --version— confirm install, pin version in any report.tao-daft convert --help— list supported source formats.tao-daft convert <source> --help— list valid targets for that source.Infer source from layout (same directory markers as the
tao-validate-dataset-formatskill's "Format inference"). If you cannot infer or the target is unspecified, ask.tao-daft convert <source> <target> --help— pick flags for the user's intent (task subset, media copy vs reference, metadata).Execute, then interpret (see below).
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
--helpreports for the installed version — don't pass an unconfirmed pair.Source and target are positional;
--path/--outputare flags.convertonly —validateandinfohave 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 withtao-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.
Files
- ATTRIBUTION.mdAdded by AutoGineer948 B
- BENCHMARK.md7.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.1 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json853 B
- skill-card.md3.6 KiB
- skill.oms.sig4.7 KiB
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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-convert-dataset-format · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-convert-dataset-format.zip