Skill UI

This skill enables rigorous tracking of physical units and the systematic propagation of measurement uncertainty in scientific calculations. It supports unit conversions, building comprehensive GUM uncertainty budgets, and performing dimensional and physical plausibility checks. Use it whenever a calculation requires adherence to strict metrological standards and accurate error reporting.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

A comprehensive guide for ensuring the scientific reproducibility of research papers that rely on dynamic web data, such as web crawls, platform APIs, or live system traffic. It defines three reproducibility regimes (Frozen, Decaying, Unreplayable) and provides detailed instructions on documenting data decay, temporal splits, environment configurations, and computational seeds to maintain the rigor of published findings.

This guide helps researchers structure and refine academic papers for the Journal on Communications (JOC). It provides a framework for separating core, decision-making content (main text) from supporting details (appendices/supplementary material), ensuring the manuscript remains scientifically rigorous yet adheres to strict length constraints (e.g., ~12000 characters). It teaches how to use a 'decisive three-question' standard.

A comprehensive guide for positioning high-impact scientific research for journals like Nature. It covers scope fit, methodological rigor, structural constraints, and techniques for framing complex findings into a universal, compelling narrative for a broad, non-specialist audience.

Astropy is the essential Python package for astronomical research and data analysis. It provides a comprehensive suite of tools for handling celestial coordinate transformations (e.g., ICRS, Galactic), managing physical units and quantities, processing FITS files, performing complex cosmological calculations, and ensuring precise time handling. It is indispensable for any project involving scientific data in the field of astronomy and astrophysics.

This skill provides a comprehensive, reproducible rubric for evaluating scholarly and scientific work. It covers nine critical dimensions—including problem statement, methodology, data evidence, analysis rigor, and citation quality—making it ideal for reviewing research papers, grant proposals, literature reviews, and academic theses. Use it to provide structured, actionable, and professional feedback to enhance scholarly output.

This core skill is designed for academic publishing, combining deep AI research with structured, professional writing. It guides the creation of manuscripts using IMRAD structure and handles complex citation styles (APA, AMA, etc.). Crucially, it mandates the generation of graphical abstracts and rich technical schematics to ensure the highest level of visual rigor for research papers.

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical solvers, it performs exact calculations using mathematical symbols, allowing users to model and solve complex problems across various domains. Core capabilities include symbolic calculus (derivatives, integrals, limits), advanced algebra, solving systems of equations, linear algebra (determinants, eigenvalues), and specialized modules for physics and number theory. It can also generate executable code in languages like C and Fortran, making it invaluable for academic research and scientific computing.

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

A rigorous methodology for debugging complex anomalies or incidents when multiple plausible causes exist. It guides the user to enumerate competing, falsifiable hypotheses and prioritizes running the cheapest, most discriminating observation first. By iteratively eliminating hypotheses and preferring the least-assumptive survivor, it systematically localizes the root cause efficiently.