Skill UI

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.

An adapter for generating short, browser-playable video assets using OpenRouter's asynchronous video generation API. This skill is designed for meta-skills to integrate dynamic multimedia content into web pages. It handles the full job lifecycle—submitting the request, polling for completion, and downloading the final MP4 asset—and adheres strictly to the required manifest reporting format.

This skill analyzes various legal AI outputs, such as memos, contracts, and reports, to assess their structural integrity and legal robustness. Inspired by Minimum Description Length (MDL), it audits for unnecessary complexity, overly verbose caveats, hidden legal uncertainties, or inefficient workflow design, ensuring the resulting output is as concise and simple as legally safe.