Claude Skills by NVlabs

Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.

Used for smoke or dataset finetuning of NV-Segment-CT VISTA3D on CT NIfTI labels. Not for clinical validation.

Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.

Use as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.

Top-level workflow skill for USD performance diagnosis and optimization. Use for slow loading, high memory, low FPS, or 'optimize my scene' requests; delegates auth/runtime setup to Phase 0 owners.

Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with

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.

Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions.

Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data.

CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX

Cosmos3-Nano video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video

Shared launch intake for any TAO workflow or action. Use when the user wants to run TAO AutoML, train, evaluate, infer, export, generate TensorRT engines, or launch DEFT/workflow jobs on an execution platform.

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".

Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighbour source images for downstream augmentation. Use as the immediate next step after `tao-route-visual-changenet-samples` when expanding a real-image augmentation queue from the mining subset.

Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module

Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm

Kubernetes execution platform — submits TAO container jobs as single-pod k8s Jobs with NVIDIA GPU scheduling.

Local or remote Docker execution for TAO SDK job containers using a Docker daemon with NVIDIA GPU runtime. Use

Host setup for TAO GPU backends. Checks and, after user approval, installs NVIDIA driver branch 580, CUDA Toolkit 13.0, and NVIDIA Container Toolkit 1.19.0 for Docker/local-Docker and Kubernetes GPU worker hosts. The `--check-only` path works on any Linux distribution; `--install` automates debian-family (Ubuntu/Debian/Pop!_OS/Mint/Zorin/Raspbian), rhel-family (Fedora/RHEL/Rocky/AlmaLinux), and suse-family (openSUSE/SLES) hosts, and prints actionable manual-install steps for everything else. ...

Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for

BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view

CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF

Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing,

Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts

DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with

Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of

Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D

Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for

PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.)

Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs

MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations

Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for

Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with

Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for

NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation

NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation

OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a

OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC

OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a

Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing

PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a

Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences

Person re-identification (ReID). Learns discriminative embeddings to match the same person across different

RT-DETR (Real-Time DEtection TRansformer) for 2D object detection. Designed for real-time inference with

SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature

Standard single-step train/eval/export workflow for any TAO model. Use when training a TAO model on a dataset

Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable

Visual ChangeNet for binary image classification and segmentation in AOI defect detection. Use when training,

Run `tao-daft validate` to check NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors. Do