Skill UI

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

A powerful skill for generating brand-new images or performing professional edits on existing photos using the OpenAI GPT Image API. Users can provide detailed prompts, specify desired aspect ratios (e.g., 1024x1536), select background types, and control output quality. Ideal for creative projects, logo design, concept art, and advanced photo manipulation.

A comprehensive, human-guided onboarding skill designed for users new to AI coding agents and Power BI agentic development. It provides structured setup help, deep conceptual explanations, and conversational guidance, mimicking a mentor's session. Modes include concise setup assistance and deep conceptual learning. Use cases: Setting up a new agent environment, understanding core AI concepts (models, prompts, tools), or auditing existing agent configurations.

A powerful skill utilizing the Google Gemini API to generate high-quality images from text prompts or edit existing photos. It supports various aspect ratios, resolutions (up to 4K), and offers model selection for optimal results, making it ideal for creative design, conceptual visualization, and professional visual content creation.

A comprehensive project management skill designed for authors preparing papers for the EACL conference. It maps out the entire single-cycle submission process, covering backward planning from the ARR deadline, rebuttal strategies, commitment decisions, and final camera-ready stages. Essential for navigating highly structured academic publication timelines and minimizing risk.

This skill allows agents to retrieve and inject relevant operational memories from the OpenViking experience library into the current prompt context. It is designed for complex, multi-step tasks involving tool usage, code operations, or decision-making workflows. By searching and reading past successful executions, the agent improves context awareness and decision accuracy, moving beyond simple Q&A.