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  • Qwen
    Downloadable

    qwen-image

    Qwen-Image is a text-to-image foundation model with advanced multilingual text rendering.
    Model

  • Generate synthetic PCB inspection images on Kubernetes with the SMT Defect Image Generation (DIG) DAG in CDF Orchestration. The DAG renders components in simulation, turns the renders into realistic golden images (sim2real), and uses AnomalyGen to add tex
    Skill

  • 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-cha
    Skill
  • Stepfun-ai
    Free Endpoint

    Step 5 Preview

    Preview of StepFun's Step 5: ~600B-total / 27B-active multimodal MoE (text and image in, text out) with 1M context, reasoning and tool calling, served in NVFP4.
    Model
  • Microsoft
    Downloadable

    TRELLIS

    MSFT TRELLIS is a 3D AI model that generates high-quality 3D assets from text or image inputs.
    Model
  • NVIDIA
    DownloadableFree Endpoint

    cosmos3-nano

    Generates images, videos and action predictions from text, visual inputs and spatial controls, with mandatory input screening and visual SynthID watermarking.
    Model
  • Z.ai
    Free Endpoint

    glm-5-3-flash

    Multimodal 320B-total / 18B-active MoE with hybrid KDA and sparse MLA attention, native FP8 weights, reasoning and tool calling.
    Model
  • Robotics
    Enterprise

    Synthetic Manipulation Motion Generation for Robotics

    Generate exponentially large amounts of synthetic motion trajectories for robot manipulation from just a few human demonstrations.
    Blueprint
  • Meta
    DownloadableFree Endpoint

    muse-glimmer-30b

    Muse Glimmer 30B is a multimodal reasoning model accepting text and images, with native tool-calling and separate reasoning output.
    Model
  • Intermediate60 MIN

    Run Multi-Modal Inference with TensorRT

    GPU-accelerated text-to-image generation with diffusion models
    Playbook
  • Google
    Free Endpoint

    paligemma

    Vision language model adept at comprehending text and visual inputs to produce informative responses
    Model

  • CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CL
    Skill

  • OCRNet for scene text recognition. Recognizes text content from cropped text-region images and supports CTC and attention-based decoders. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCRNet mode
    Skill

  • Run iterative improvement for NVIDIA TAO CLIP / SigLIP image-text retrieval on attribute-labelled data. Use when a request combines retrieval evaluation, weak-attribute or caption-pair mining, repeated retraining, and a stopping condition based on a retri
    Skill

  • OCDNet for scene text detection. Detects arbitrary-oriented text regions in natural images using a differentiable binarization approach. Use when training, evaluating, exporting, pruning, quantizing, retraining, or running inference for a TAO OCDNet model
    Skill

  • Run a binary industrial-inspection DEFT loop with TAO RT-DETR: measure on frozen KPI/test roles, mine gap-similar real and clean images with SigLIP, accumulate admitted COCO data, retrain from one base checkpoint, and select by KPI AP50. Use for iterative
    Skill
  • Stability AI
    Downloadable

    stable-diffusion-3.5-large

    Stable Diffusion 3.5 is a popular text-to-image generation model
    Model
  • NVIDIA
    Free Endpoint

    nemotron-3.5-rerank-8b

    8B multimodal reranking model that scores how relevant a text or document-image passage is to a query.
    Model
  • NVIDIA
    Downloadable

    nemotron-ocr-v2

    Nemotron OCR v2 is a state-of-the-art multilingual text recognition model designed for robust end-to-end optical character recognition (OCR) on complex real-world images.
    Model

  • Build a per-target knowledge-base markdown next to the active profile by walking the BSP root and source tree. Use after init-image / init-source; not for editing profile fields.
    Skill

  • Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on thi
    Skill
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