菜单
Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do not use for non-DAFT formats. Use when the user asks to validate a DAFT dataset, check DAFT schema, validate a TAO dataset format, or run `tao-daft validate`.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/NVIDIA-TAO/tao-skill-bank --skill tao-validate-dataset-format命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images, unique-neighbor mining against a source pool, ODVG dataset staging, and retraining — repeated for a fixed number of iterations. Also prepares the source pool the loop mines from, as a separate run: Co-DETR pseudo-labeling, folding to the target classes, KITTI→COCO→ODVG conversion, and embedding. Use for prompts like "run the DEFT OD loop", "run smart data augmentation for grounding dino", "mine and retrain my detection model", "improve OD mAP with gap analysis and mining", "prep the source pool", or "pseudo-label my unlabeled images for mining"; do not use for standalone TAO training, one-off inference, or gap analysis alone.
NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. Use when the user asks to "fine-tune NV-Tesseract", "run AD diffusion inference", "detect anomalies with diffusion", "time series anomaly detection", "finetune ad-diffusion", "use perform_anomaly_analysis_with_diffusion", "automl ad-diffusion", "hyperparameter search ad-diffusion", "hyperparameter optimization" or mentions "curriculum_medium.yaml", "final_model.pth", "nv-tesseract-ad-diffusion", "ad_diffusion", or "TSDiffuser_Generic".
NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. Use when the user asks to "forecast with NV-Tesseract", "run forecasting inference", "use perform_forecasting", "DARR mode", "context-enhanced forecasting", "lag horizon attribution", "interpretability", "fine-tune forecasting", "fine-tune forecasting with automl", "hyper-parameter optimization with forecasting", or or mentions "nv-tesseract-forecasting", "moment_head_512_6hr", or "run8_best_model_cr".
基于 SOC 职业分类
正在显示 SKILL.md
| name | tao-validate-dataset-format |
| description | Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do not use for non-DAFT formats. Use when the user asks to validate a DAFT dataset, check DAFT schema, validate a TAO dataset format, or run `tao-daft validate`. |
| license | Apache-2.0 |
| compatibility | Requires Python 3.10+ and the nvidia-tao-daft package (pip install nvidia-tao-daft). |
| metadata | {"author":"NVIDIA Corporation","version":"0.1.0"} |
| allowed-tools | Read Bash |
| tags | ["tao-daft","dataset","validation","schema"] |
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).
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).
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 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
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.
nvidia-tao-daft installed (pip install nvidia-tao-daft; the wheel
is enough, no source repo). Confirm with tao-daft --version.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.
--format:
tao-daft validate <format> [flags]. List current formats via
tao-daft validate --help; slugs look like metropolis-v3.0,
cosmos-reason-v1.0.--path PATH, not positional. It accepts a single
dataset/scene or a parent directory — the validator walks the tree.tao-daft validate metropolis-v3.0 --help, before choosing them.
Don't assume a flag from one format exists on another.So the loop is: tao-daft --version → tao-daft validate --help →
pick format (infer if unspecified, see below) →
tao-daft validate <format> --help → run → interpret.
Use directory markers, not filenames:
meta.json next to media/ and text/ ⇒ cosmos-reason-v1.0.contextual/,
typically alongside raw/ and task/ ⇒ metropolis-v3.0.The CLI ends every run with a VALIDATION RESULTS block, then
✅ VALIDATION PASSED or ❌ VALIDATION FAILED, and exits non-zero on
failure (safe to chain in scripts).
Output can be large on big trees — capture the full output to a file and read it in slices rather than scrolling inline.
tao-daft validate --help reports
for the installed version; older slugs may have been retired.validate only. Defer to the dedicated skills for
tao-daft info and tao-daft convert.tao-daft: command not found — wheel not installed in the active
env. pip install nvidia-tao-daft; verify tao-daft --version.error: argument --path is required — path passed positionally.
Move it behind --path.invalid choice: '<format>' — slug isn't wired up in this
version. Re-run tao-daft validate --help and pick from the list.--help.--strict.