DevOps 与云
部署、CI/CD、云平台和基础设施
Skills 列表
tao-train-mask2former
Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with
nvidiatao-train-mask-auto-encoder
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs
nvidiatao-run-on-brev
Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on
nvidiatao-train-nvdinov2
NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation
nvidiatao-train-pointpillars
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a
nvidiatao-train-depth-anything-v2
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts
nvidiatao-train-centerpose
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF
nvidiatao-train-bevfusion
BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view
nvidiatao-train-ocdnet
OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a
nvidiatao-train-oneformer
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a
nvidiatao-train-segformer
SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature
nvidiatao-train-foundation-stereo
Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D
nvidiatao-train-deformable-detr
Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing,
nvidiatao-train-fast-foundation-stereo
Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of
nvidia
cloud
使用 PlantUML 语法创建云提供商架构图，支持官方 AWS、Azure、GCP 和阿里云服务图标。适用于多云服务拓扑和迁移蓝图。
markdown-viewernetwork
使用 PlantUML 语法和 mxgraph 设备图标（Cisco、Citrix 等）创建网络拓扑图。适用于局域网/广域网布局、数据中心互联以及物理/逻辑网络设计。
markdown-vieweruml
使用 PlantUML 语法创建 UML 图。最适合软件建模——类图、时序图、活动图、状态机图、组件图、用例图和部署图，采用简洁的文本符号和自动布局。
markdown-viewerarchitecture
使用HTML/CSS模板创建分层系统架构图，支持颜色编码的层级和网格布局。适用于技术栈、微服务拓扑和多层应用设计。
markdown-viewertao-run-automl
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm
nvidiatao-train-image-classification
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.)
nvidiatao-train-ocrnet
OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC
nvidiatao-train-optical-inspection
Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing
nvidia
google-agents-cli-scaffold
当用户想要“创建 Agent 项目”、“启动新的 ADK 项目”、“帮我构建一个新 Agent”、“为项目添加 CI/CD”、“添加部署配置”、“增强现有项目”或“升级项目”时，应当使用此 Skill。 本 Skill 属于 Google ADK（Agent Development Kit）Skill 套件的一部分。 涵盖 `agents-cli scaffold create`、`scaffold enhance` 和 `scaffold upgrade` 命令、模板选项、部署目标以及原型优先（prototype-first）工作流。 请勿用于编写 Agent 代码（请使用 google-agents-cli-adk-code）或部署运维操作（请使用 google-agents-cli-deploy）。
googlegoogle-agents-cli-observability
当用户想要“设置追踪”、“监控我的ADK代理”、“配置日志记录”、“添加可观测性”、“调试生产流量”，或需要关于监控已部署的ADK（Agent Development Kit）代理的指导时，应使用此技能。涵盖Cloud Trace、提示-响应日志记录、BigQuery Agent Analytics、第三方集成（AgentOps、Phoenix、MLflow等）以及故障排除。属于Google ADK（Agent Development Kit）技能套件的一部分。不要用于部署设置（使用google-agents-cli-deploy）或API代码模式（使用google-agents-cli-adk-code）。
google