New and Recently Updated Skills
Verified Agent Skills sorted by source update date. This is where newly published SKILL.md files should surface first.
Skill Card Generator
Use only to generate or update a governance skill card for a specified existing agent skill directory. Do not use for explaining, listing, comparing, or discussing skill capabilities.
Tao Analyze Changenet Rca
Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with
Tao 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.
Tao Analyze Gaps Vlm Bcq
Extract false-positive and false-negative gaps from VLM binary-classification-question (BCQ, yes/no) predictions.
Tao Convert Dataset Format
Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data.
Tao Finetune Clip
CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX
Tao Finetune Cosmos Embed
Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".
Tao Finetune Cosmos Reason
Cosmos3-Nano video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video
Tao Finetune Huggingface Model
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.
Tao Generate Image Grounding
Two-step image grounding pipeline: extracts referring expressions from (image, caption) pairs and grounds them
Tao Generate Referring Expressions
Four-step image referring-expression pipeline: turns images plus KITTI bounding-box labels into region
Tao 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".
Tao Launch Workflow
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.
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".
Tao Mine Aoi Images
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.
Tao 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.
Tao Route Visual Changenet Samples
Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module
Tao Run Automl
Run AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm
Tao Run Automl Deft Pipeline
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. Use when the user asks to "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
Tao Run Deft Aoi
Run the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models: baseline evaluate, RCA, Cosmos AnomalyGen / AMP synthetic defects, k-NN mining, retraining, and deployment gating until FAR / recall KPI targets are met. Use for prompts like "run the DEFT loop", "fine-tune until FAR below 0.1% at recall=100%", or "improve my AOI ChangeNet model with RCA and synthetic defects"; do not use for standalone TAO training, one-off inference, generic anomaly generation, or RCA-only analysis.
Tao 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.
Tao Run On Brev
Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on
Tao Run On Docker
Docker conventions for running NVIDIA GPU container workloads — NGC authentication, --gpus flag, mount patterns,
Tao Run On Kubernetes
Kubernetes execution platform — submits TAO container jobs as single-pod k8s Jobs with NVIDIA GPU scheduling.
Tao Run On Local Docker
Local or remote Docker execution for TAO SDK job containers using a Docker daemon with NVIDIA GPU runtime. Use
Tao Run On Slurm
Remote SLURM GPU cluster execution over SSH with sbatch/srun, Pyxis/Enroot containers, and Lustre-backed
Tao Run Platform
TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Brev, SLURM,
Tao Setup Nvidia Gpu Host
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. Use when the user asks to "set up an NVIDIA GPU host", "check TAO Docker GPU runtime", or prepare a Kubernetes GPU worker for TAO.
Tao Train Action Recognition
Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for
Tao Train Bevfusion
BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view
Tao Train Centerpose
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF
Tao Train Deformable Detr
Deformable DETR for 2D object detection. Uses deformable attention for efficient multi-scale feature processing,
Tao Train Depth Anything V2
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts
Tao Train Dino
DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with
Tao Train Fast Foundation Stereo
Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of
Tao Train Foundation Stereo
Stereo depth estimation using FoundationStereo. Predicts disparity maps from stereo image pairs for 3D
Tao Train Grounding Dino
Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for
Tao Train Image Classification
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.)
Tao Train Mask Auto Encoder
Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. Masks random patches and reconstructs
Tao Train Mask Auto Label
MAL (Mask Auto-Label) for weakly-supervised segmentation. Produces segmentation masks from minimal annotations
Tao Train Mask Grounding Dino
Mask Grounding DINO for grounded instance segmentation. Extends Grounding DINO with a mask-prediction head for
Tao Train Mask2former
Mask2Former for universal image segmentation (panoptic, instance, and semantic). Transformer-based with
Tao Train Metric Learning Recognition
Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for
Tao Train Nvdinov2
NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation
Tao Train Nvpanoptix3d
NVPanoptix3D for panoptic 3D scene reconstruction from posed RGB images. Produces 3D panoptic segmentation
Tao Train Ocdnet
OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a
Tao Train Ocrnet
OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC
Tao Train Oneformer
OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a