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Skills 列表
network
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-viewerdata-analytics
Create data pipeline and analytics architecture diagrams using PlantUML syntax with database/analytics stencil icons. Best for ETL pipelines, data lakes, real-time streaming, data warehousing, and BI dashboard design.
markdown-viewerinfographic
Create template-based infographics with space-separated key-value syntax (NOT YAML). Best for KPI dashboards, timelines, roadmaps, SWOT analysis, funnels, comparisons, and org charts with quick visual impact.
markdown-viewergraphviz
Create directed/undirected graphs using DOT language with automatic layout. Best for dependency trees, call graphs, package hierarchies, and module relationships requiring fine-grained edge routing.
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
seedance-prompt-en
Write effective prompts for Jimeng Seedance 2.0 multimodal AI video generation. Use when users want to create video prompts using text, images, videos, and audio inputs with the @ reference system. Covers camera movements, effects replication, video extension, editing, music beat-matching, e-commerce ads, short dramas, and educational content.
dexhunter
tao-list-capabilities
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".
nvidiatao-generate-video-reasoning-annotations
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-of-thought", "video captioning pipeline", "video distillation".
nvidiatilegym-cutile-python
Expert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks.
nvidiatao-port-huggingface-model
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check, HuggingFace inspection and validation, codebase exploration, tao-core configuration and native trainer implementation, ONNX export plus TensorRT deploy integration, packaging and L0 testing, container-based end-to-end validation, and (conditional) accuracy/latency tuning. Supports classification, object detection, semantic / instance / panoptic segmentation, zero-shot detection, and depth estimation.
nvidiatao-finetune-clip
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX
nvidiatao-run-automl
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm
nvidiatao-convert-dataset-format
Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data.
nvidiatao-train-image-classification
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.)
nvidiatao-run-on-kubernetes
Kubernetes execution platform — submits TAO container jobs as single-pod k8s Jobs with NVIDIA GPU scheduling.
nvidiatao-generate-image-grounding
"Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them
nvidiatao-analyze-changenet-rca
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with
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
nvidiatao-run-inference-service
Start, query, and stop a network-specific TAO inference microservice ({network_arch}-inference-microservice) by delegating container execution to the appropriate platform skill. Handles container image resolution, job-payload JSON construction, and the service registry. Use when the user wants to run inference on a TAO model checkpoint using a microservice container, deploy a TAO inference endpoint, or stop a running inference container.
nvidiatao-analyze-gaps-visual-changenet
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the data-services container (`tao_toolkit.data_services` from `versions.yaml`) directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.
nvidia
swiftui-expert-skill
Use when writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state management and `@Observable` data flow, view composition and invalidation/performance, lists and `ForEach` identity, environment usage, localization, animations, Liquid Glass adoption, migrating soft-deprecated APIs, or Instruments `.trace` capture/analysis for hangs, hitches, CPU hotspots, or
avdlee
google-agents-cli-adk-code
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google