菜单
One-to-three-sentence description of what this data transformation does. Use when the user asks to "REPLACE-WITH-INTENT", or mentions REPLACE-WITH-DOMAIN-TERMS. Include literal trigger phrases the user is likely to say.
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 replace-with-skill-name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。
正在显示 SKILL.md
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
| name | REPLACE-WITH-SKILL-NAME |
| description | One-to-three-sentence description of what this data transformation does. Use when the user asks to "REPLACE-WITH-INTENT", or mentions REPLACE-WITH-DOMAIN-TERMS. Include literal trigger phrases the user is likely to say. |
| license | Apache-2.0 |
| compatibility | REPLACE — typical examples — `Requires docker + nvidia-container-toolkit.` (containerized) or `Requires Python 3.8+ and Pillow.` (local script) |
| metadata | {"author":"REPLACE-WITH-AUTHOR-NAME","version":"0.1.0"} |
| allowed-tools | Read Bash |
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).
Two-line summary of the transformation. Inputs → outputs.
| Dependency | Purpose | Install |
|---|---|---|
| docker | Run the container | https://docs.docker.com/engine/install/ |
(or for local scripts)
| Dependency | Purpose | Install |
|---|---|---|
| Python 3.8+ | Runtime | System / conda |
| Pillow | Image I/O | pip install pillow |
docker run --gpus all --rm \
-v /path/to/input:/input \
-v /path/to/output:/output \
nvcr.io/nvidian/iva/<image>:<tag> \
<command> --input /input --output /output
python scripts/<script>.py --input-dir <path> --output <path>
for label in bridge tombstone shift; do
python scripts/<script>.py --input-dir data/${label}/ --output ${label}.csv --label ${label}
done
See tao-skill-bank:tao-run-on-docker for docker conventions (when containerized).
| Field | Type | Description |
|---|---|---|
<input-dir> | folder | What's in it, expected format |
<input-parquet> | file | Schema, columns |
| Field | Type | Description |
|---|---|---|
<output-csv> | file | Schema produced |
| Argument | Required | Default | Description |
|---|---|---|---|
--input-dir | Yes | — | Input directory |
--output | No | output.csv | Output path |
--label | No | auto-detect | Force single label for all rows |
Step-by-step description of the transformation:
Optional pipeline diagram:
Input A ─┐
Input B ─┼─→ [processing] ─→ Output
Input C ─┘
| Symptom | Cause | Fix |
|---|---|---|
KeyError: ... | Wrong column name in input parquet | Use --column-name to override |