Skill UI

Guides symbolic mathematics work in Python, covering algebraic simplification, calculus, equation solving, linear algebra, physics, number theory, geometry, and code generation to deliver exact symbolic results instead of numeric approximations.

Rigorously dissects code and architecture through advanced mathematics—graph theory, category theory, stochastic systems, information theory, and formal invariants—to uncover hidden structure and optimize performance.

A comprehensive technical module covering foundational deep learning principles and cutting-edge computer vision architectures, drawing from Yann LeCun's research. Topics include CNN fundamentals, backpropagation theory, and advanced self-supervised learning (SSL) models such as SimCLR, MAE, and the modern Joint Embedding Predictive Architecture (JEPA). Provides detailed theoretical explanations and complete PyTorch code for implementation.

Provides guidance on organizing and writing focused XUnit unit tests, covering project setup, fact/theory patterns, assertions, mocking, and data-driven testing for reliable .NET test suites.

Stress-tests a research paper against the strongest possible rejection argument. It executes a two-stage process: first, generating a single, devastating rejection memo (the 'attack'), and second, having a fresh reviewer defend the paper point-by-point to surface unresolved critical issues. This tool is invaluable for pre-submission quality assurance, especially for theory papers.

A comprehensive skill for handling PowerPoint (.pptx) files. This includes text extraction, deep content analysis via raw XML access (comments, notes, layouts, themes), and programmatic creation of new presentations from scratch. It guides users through design principles, color theory, and structured document generation workflows.