Skill UI

Provides direct REST API access to UniProt, the premier resource for protein sequence and functional information. This skill enables users to search proteins by name, gene, or accession number, retrieve sequences in FASTA format, perform cross-database ID mapping (e.g., Ensembl, RefSeq), and analyze functional annotations (GO terms) for building comprehensive bioinformatics pipelines.

Generates a single formula-driven Excel dashboard from CSV/tabular data, complete with KPI cards, charts, sparklines, and conditional formatting so analysts can instantly review metrics and share an executive-ready .xlsx output.

This skill provides comprehensive commands for creating, managing, versioning, and querying structured datasets within the Arize platform. It covers dataset CRUD operations, appending examples, and exporting data in various formats (JSON, CSV, Parquet) using the `ax` CLI. Ideal for preparing test data, generating evaluation examples, or retrieving golden datasets for LLM development and model testing.

Structured pipeline for credit risk pre-loan modeling that loads and formats raw data, filters abnormal periods, trims high-missing/low-IV/high-PSI/high-correlation features, denoises via Null Importance, and exports an Excel cleaning report for review.

This skill provides direct command-line access to the Paperzilla platform, allowing users to manage, summarize, and retrieve academic research data. Users can fetch canonical papers in markdown format, get the latest project recommendations, analyze paper relevance, export data (JSON/Atom), and provide feedback on recommendations. It is an essential tool for researchers managing a large volume of academic literature.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

Use this skill to comprehensively verify all constraints related to a candidate cricket player. By cross-referencing structured data from sources like Wikipedia or Cricinfo, you can confirm details such as birth date, debut match specifics (format, opponent, score), career records, and teams played for. It is crucial for establishing factual accuracy, especially when distinguishing between different formats like ODI, T20I, and Test matches.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.

AnnData is a specialized Python package designed for managing and analyzing annotated matrices, particularly crucial in single-cell genomics and bioinformatics. It efficiently stores experimental measurements (X) alongside comprehensive metadata (obs, var, obsm, etc.). This skill allows users to read, write, subset, and concatenate large, complex datasets in formats like .h5ad and .zarr, making it essential for scRNA-seq analysis workflows and integration with the scverse ecosystem.

Astropy is the fundamental Python package for astronomical research and astrophysics data processing. It provides essential tools for handling physical units, performing coordinate transformations across celestial frames (ICRS, Galactic, etc.), managing FITS files, conducting cosmological calculations, and precise time handling. It is indispensable for any task involving celestial coordinates, unit conversions, or analyzing large astronomical catalogs.

Biopython is a robust, comprehensive toolkit for computational molecular biology written in Python. It enables users to manipulate biological sequences (DNA, RNA, protein), parse standard formats (FASTA, GenBank, PDB, etc.), and programmatically access major bioinformatics databases like NCBI via Entrez. This skill is ideal for developing complex bioinformatics pipelines, including sequence alignment, structural analysis, and phylogenetic tree construction.

A comprehensive Python package providing a unified interface to over 40 major bioinformatics web services and databases. Use it for complex biological workflows, including cross-database querying (e.g., UniProt, KEGG, ChEMBL), identifier mapping, protein sequence retrieval, pathway analysis, and running sequence similarity searches like BLAST. It streamlines data integration from multiple life science resources into consistent Python code.