Skill UI

This sophisticated tool generates publication-quality AI illustrations and diagrams for academic papers. It implements a multi-stage, iterative workflow, using advanced LLMs (like Claude and Gemini) to parse user requests, optimize complex layouts, verify academic styling (e.g., CVPR/NeurIPS standards), and render high-fidelity figures. Ideal for researchers needing professional, complex visuals for their manuscripts.

This skill provides real-time monocular depth estimation using advanced models like Depth Anything v2. It transforms live camera feeds by generating colorized depth maps, enabling depth overlays and detailed scene understanding. Crucially, it supports a 'privacy mode' that anonymizes visual identities by using depth-only transformation, preserving only the spatial layout and movement patterns for secure monitoring purposes. Optimized for both CoreML on macOS and PyTorch on other platforms.

A comprehensive guide for authors on how to package and present supplementary evidence for INTERSPEECH submissions. It clarifies the difference between core reviewed content and optional accompaniment (audio demos, extended analyses), emphasizing that the main paper must stand alone. Learn best practices for audio curation, layout, and navigating the 'three shelves' structure to maximize impact during the review process.

Extract text, tables, and structured fields from documents using Azure AI Document Intelligence .NET SDK with prebuilt and custom models for invoices, receipts, IDs, and layouts.

Automated visual tuning: a vision or video model rates rendered variants in a loop. Render labeled variants into one artifact, ask the model to rate them and suggest better values, render suggestions, pick the best, repeat until good. Ideal for aesthetics, timing, layout, color grading, and other subjective visual parameters without numeric metrics.