菜单
Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. Use when the user asks "what can TAO Skill Bank do", "list TAO models", "which TAO workflows are available", or "what supports AutoML".
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-list-capabilities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".
正在显示 SKILL.md
基于 SOC 职业分类
| name | tao-list-capabilities |
| description | Answer what the TAO Skill Bank plugin can do by generating the response from packaged application, data, model, AutoML, and platform manifests. Use when the user asks "what can TAO Skill Bank do", "list TAO models", "which TAO workflows are available", or "what supports AutoML". |
| license | Apache-2.0 |
| compatibility | Requires the packaged TAO skill bank helper scripts. |
| metadata | {"author":"NVIDIA Corporation","version":"0.1.0"} |
| allowed-tools | Read Bash |
| tags | ["tao","capabilities","discovery"] |
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).
Use this skill when the user asks what tao-skill-bank can do, asks for plugin
capabilities, asks which application or data workflows are available, asks which
models are supported, or asks what models are capable with AutoML.
Run scripts/list_tao_capabilities.py for general capability questions, or
scripts/list_tao_models.py for model/action and AutoML support questions.
For a general capabilities answer, run the packaged helper:
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_capabilities.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text
Use the helper output as the source of truth for the answer instead of manually enumerating capabilities from this skill or plugin metadata. Include:
applications/ and what it can do.data/ and what it can do.skills/platform/tao-run-on-*/SKILL.md frontmatter.models/: train,
evaluate, inference, export, and TensorRT engine generation when those actions
are present in the packaged schema manifest.When the user asks which TAO models are available or which actions a model can run, use the packaged model-list script instead of manually scanning model folders:
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --scope all --format text
The model list comes from skills/models/schemas.manifest.json.
When the user asks what models are capable with AutoML, use the same model-list script in AutoML mode, or the compatibility wrapper:
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_models.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --scope automl --format text
${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_automl_support.py \
--skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text
Use --action distill, --action prune, or --action quantize when the user
asks about a specific compression action.
AutoML support for an action requires
skills/models/<model_skill>/schemas/<action>.schema.json to be packaged with the plugin
and parse successfully as JSON. If that dataclass schema is missing or invalid,
do not describe the model/action as AutoML-supported.