Skill UI

Zarr is a robust Python library designed for efficiently storing and managing large N-dimensional arrays. It provides features like chunking, compression, and parallel I/O, making it perfectly suited for cloud-native scientific computing pipelines. It ensures seamless integration with major scientific stacks including NumPy, Dask, and Xarray, while supporting remote storage backends like S3 and GCS.

This skill facilitates reproducible retrieval of scientific, regulatory, or financial facts by querying documented public APIs. It requires defining a strict retrieval contract, handling complex filtering, managing pagination, and ensuring full provenance and auditable results, minimizing reliance on general knowledge inference.

This skill guides the systematic process of conducting comprehensive literature reviews across multiple academic databases (e.g., PubMed, arXiv, Semantic Scholar). It supports meta-analyses, scoping reviews, and state-of-the-art investigations. The rigorous workflow includes structured searching, data extraction, citation verification, thematic synthesis, and generating publication-quality reports (PDF/Markdown) complete with mandatory scientific schematics (like PRISMA flow diagrams) to ensure academic rigor and reproducibility.

A comprehensive toolkit for advanced web intelligence workflows. It facilitates deep research, structured data enrichment, URL extraction, entity discovery, and continuous web monitoring. Ideal for scientific topics, academic source discovery, and generating exhaustive, evidence-based reports.

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.

A comprehensive server providing research-grade access to arXiv. Users can search papers by topic, author, or date, download full texts, and analyze the scientific landscape by traversing citation graphs. It also supports local semantic search over downloaded documents and enables monitoring for new papers with real-time alerts. Ideal for academic research and literature review.

This powerful web toolkit uses Exa to perform advanced web searches and detailed URL content extraction. It is specifically tuned for scientific, technical, and academic research, allowing users to filter results by scholarly categories (e.g., research papers) and include academic domains. Use it for complex lookups, gathering current information, or batch extracting content from articles and PDFs.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.

A specialized tool for scientific data visualization, acting as a visualization advisor to analyze data, recommend chart types, prevent common errors, and generate publication-grade figures.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.