DevOps 与云
部署、CI/CD、云平台和基础设施
Skills 列表
dotnet-trace-collect
Guide developers through capturing diagnostic artifacts to diagnose production .NET performance issues. Use when the user needs help choosing diagnostic tools, collecting performance data, or understanding tool trade-offs across different environments (Windows/Linux, .NET Framework/modern .NET, container/non-container).
dotnet
printing-press-import
Bring a published CLI from the public library into the internal library so it's identical to a freshly-generated copy — module path reverted, manuscripts placed alongside, ready for /printing-press-polish or /printing-press-emboss. Use when the public library has a CLI you don't have locally, or to recover from a broken/lost internal copy. Trigger phrases: "import the CLI", "bring it into my library", "fetch from public library", "I don't have it locally yet".
mvanhornprinting-press-score
Score a generated CLI against the Steinberger bar, compare two CLIs side-by-side
mvanhorn
cookie-sync
Sync cookies from local Chrome to a Browserbase persistent context so the browse CLI can access authenticated sites. Use when the user wants to browse as themselves, sync cookies, or log into sites via Browserbase.
browserbase
tao-train-grounding-dino
Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for
nvidiatao-train-metric-learning-recognition
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for
nvidiatao-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
Create cloud provider architecture diagrams using PlantUML syntax with official AWS, Azure, GCP, and Alibaba Cloud service icons. Best for multi-service cloud topologies and migration blueprints.
markdown-viewernetwork
Create network topology diagrams using PlantUML syntax with mxgraph device icons (Cisco, Citrix, etc.). Best for LAN/WAN layouts, datacenter interconnects, and physical/logical network design.
markdown-vieweruml
Create UML diagrams using PlantUML syntax. Best for software modeling — Class, Sequence, Activity, State Machine, Component, Use Case, and Deployment diagrams with concise text-based notation and auto-layout.
markdown-viewerarchitecture
Create layered system architecture diagrams using HTML/CSS templates with color-coded tiers and grid layouts. Best for technology stacks, microservices topology, and multi-tier application design.
markdown-viewer