mcpField
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tao-convert-dataset-format

tao-convert-dataset-format

github.com/promptingcompanyData & databases039 installsUpdated 17w ago
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About tao-convert-dataset-format

A skill for converting DAFT datasets with `tao-daft convert`, intended for preparing visual-language model training data. It uses the installed CLI's help output to select supported source and target formats and their options.

Key features

  1. 01Convert supported DAFT formats
  2. 02Handle datasets or directory trees
  3. 03Select options for each format pair
  4. 04Inspect conversion command help

Use cases

  1. 01Convert a DAFT dataset for training
  2. 02Batch-convert datasets in a directory tree
  3. 03Prepare QA or summarization data for VLM training
  4. 04Check dataset structure after conversion

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/promptingcompany/nv-skills --skill tao-convert-dataset-format

After installing, type/tao-convert-dataset-formatin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

BENCHMARK.md
skill-card.md
skill.oms.sig
evals/
evals.json

Details

From the project README

Convert a TAO DAFT Dataset

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-daft installed (wheel only, not the source repo). Confirm with tao-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.

  1. 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, ...).
  2. Path and output are flags — --path PATH (source), --output OUTPUT (destination). Both required at the leaf; passing positionally fails.
  3. --path accepts both granularities — a single scene/dataset or a parent directory; the converter walks the tree.
  4. Per-pair flags live at the leaf — flag sets differ between targets (e.g. media-handling). Always check the leaf --help.

Operating procedure:

  1. tao-daft --version — confirm install, pin version in any report.
  2. tao-daft convert --help — list supported source formats.
  3. tao-daft convert <source> --help — list valid targets for that source.
  4. Infer source from layout (same directory markers as the tao-validate-dataset-format skill's "Format inference"). If you cannot infer or the target is unspecified, ask.
  5. tao-daft convert <source> <target> --help — pick flags for the user's intent (task subset, media copy vs reference, metadata).
  6. 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 --help reports for the installed version — don't pass an unconfirmed pair.
  • Source and target are positional; --path / --output are flags.
  • convert only — validate and info have 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 with tao-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.

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