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Found 51 Skills
Data Transfer Objects using Spatie Laravel Data. Use when handling data transfer, API requests/responses, or when user mentions DTOs, data objects, Spatie Data, formatters, transformers, or structured data handling.
Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.
NVDINOv2 for self-supervised visual representation learning. Trains vision transformers via self-distillation (teacher-student) without labels and produces general-purpose visual features. Use when training, distilling, exporting, or running inference for a TAO NVDINOv2 backbone. Trigger phrases include "train NVDINOv2", "self-supervised ViT pretraining", "DINOv2 backbone", "visual representation learning".
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
Built-in OpenAPI support for .NET 10 applications. Covers document generation, transformers, TypedResults metadata, security schemes, XML comments, build-time generation, and multiple document support. No Swashbuckle needed. Load this skill when setting up API documentation, customizing OpenAPI output, adding security schemes to docs, or when the user mentions "OpenAPI", "AddOpenApi", "MapOpenApi", "document transformer", "operation transformer", "schema transformer", "OpenAPI 3.1", "API documentation", "Swashbuckle replacement", "Produces", "WithSummary", "WithDescription", "ProblemDetails", "Kiota", or "client generation".
Self-hosted ML coding practice platform with 68 problems covering Transformers, diffusion, RLHF, and more — instant browser feedback, no GPU required.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. Provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The industry standard for Large Language Models (LLMs) and foundation models in science.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Used for command-shape or live NV-Reason-CXR chest X-ray reasoning smoke tests. Not for diagnosis or clinical reporting.