Skill UI

Anndata is a Python package for handling annotated data matrices, storing experimental measurements alongside observation and variable metadata. It supports single-cell genomics analysis, managing h5ad and Zarr files efficiently. Users can read, write, concatenate, and subset data with sparse matrix support. It integrates with the scverse ecosystem including Scanpy and scvi-tools. Ideal for biological data workflows requiring efficient storage and manipulation.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.

This skill guides authors on meeting stringent academic requirements for manuscript submission, specifically addressing the mandatory replicability standard set by The Journal of Politics (JOP). Acceptance is strictly contingent on providing a complete, documented, and executable package. The required materials include the dataset(s), master code, codebook, and a comprehensive readme file, ensuring that any published finding can be independently verified and reproduced by a journal analyst. It also provides guidelines for handling restricted data.

A comprehensive guide for packaging and submitting artifacts for Information Retrieval (IR) conferences (like SIGIR). It details the strict requirements for reproducibility, including mandatory components like TREC-format run files, qrels, and index recipes. The process covers repository structure, anonymization for review, handling data provenance, and ensuring that results can be audited by any reviewer using standard community tooling.

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.