Skill UI

Creates comprehensive technical design documents by mapping requirements to technical components with clear interfaces. Conducts architecture discovery and research, synthesizes design decisions, and includes visual diagrams for complex architectures. Supports new features, extensions, and simple additions with appropriate discovery processes.

Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Includes layout design, color schemes, multi-column formats, figure integration, and AI-powered visual element generation for conference presentations and academic posters.

Perform differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Python framework for single- and multi-objective optimization with NSGA-II/III, MOEA/D, and SPEA2. Supports constraints, ZDT/DTLZ benchmarks, Pareto front analysis, and visualization for engineering design.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

Vaex is a high-performance Python library designed for processing and analyzing large tabular datasets that exceed available RAM. It supports out-of-core DataFrame operations, lazy evaluation, fast aggregations, and efficient visualization of big data. Ideal for working with massive CSV, HDF5, Arrow, or Parquet files, performing statistics on billions of rows, and building machine learning pipelines without memory constraints.

Leverages the Anima SDK to convert visual designs from Figma into production-ready code across multiple modern frameworks (React, Vue, HTML). It supports clean TypeScript integration and Tailwind CSS styling, making it ideal for developers building automated design-to-code pipelines or rapidly prototyping complex UI components directly from design mockups.

This comprehensive tutor develops advanced visual literacy and design thinking skills. It covers core disciplines including art history, color theory, typography, and UX/UI principles. Users learn through structured critique, mastering foundational concepts like Gestalt principles, C.R.A.P. principles, and formal analysis. Ideal for improving creative portfolios, analyzing art, and refining fundamental design skills across any medium.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

This skill acts as an expert Design Systems Lead, analyzing complex technical assets (HTML, CSS, screenshots) from existing projects. It synthesizes a comprehensive 'Semantic Design System' into a structured DESIGN.md file. This output serves as the authoritative 'source of truth' for AI prompting, ensuring that any new UI generated by tools like Stitch adheres perfectly to the established visual language, color palette, and component standards.

Turn HTML into high-fidelity prototypes, slides, animations, and data visualizations. Runs a studio-role workflow with fact-checking, brand assets, and expert critique; every new design first yields three directional drafts for the user to choose.

This elite skill acts as an art director and implementation strategist for modern web development. It mandates an 'Image-First' workflow: first generating high-fidelity, premium design images for specific sections (e.g., hero, product features), deeply analyzing these visual references, and only then translating them into clean, structured, and highly implementable frontend code. It is designed to avoid generic AI clichés, ensuring results that are aesthetically superior, structurally sound, and ready for professional deployment.