Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →nvidia/tao-convert-dataset-format
Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats.
Passed 16 of 19 test cases with the skill, and 3 of 19 without it.
and 17 more in the full report
14 fixed4 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
What standard naming convention do format slug identifiers follow when specifying subcommands in `tao-daft convert`?
| Checked | Without skill | With skill |
|---|---|---|
| Describes format slugs as lowercase dot-separated versioned strings. | Missed | Passed |
The instructions your model reads when the skill loads.
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).
python -c "import nvidia_tao_daft" 2>/dev/null || {
echo "MISSING: tao-daft not installed. Run:"
echo " pip install nvidia-tao-daft"
exit 1
}
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
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.
test cases passed, vs 3 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.