Skill UI

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

This guide details strategies for finding the full legal birth names of European actors, particularly those from French and Eastern European backgrounds. When English sources or IMDb only list stage names, this technique emphasizes using regional resources like French Wikipedia and specific cultural identifiers (e.g., "People's Artist" USSR) to perform deep, targeted searches, ensuring the retrieval of accurate birth details.

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.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series machine learning. It supports a wide range of temporal tasks, including classification, regression, clustering, forecasting, anomaly detection, and pattern recognition. Ideal for analyzing sequential data and extracting features from time-indexed observations.

AnnData is a robust Python package designed for handling annotated data matrices, primarily used in single-cell genomics and multi-omics analysis. It efficiently stores experimental measurements (X) alongside comprehensive metadata (observation, variable, and various annotations). This skill allows users to create, manipulate, subset, and concatenate large, complex datasets, supporting formats like .h5ad and .zarr, and integrating seamlessly with the scverse ecosystem (Scanpy, scvi-tools).

Astropy is the foundational Python package for astronomical research and astrophysical data analysis. It provides essential APIs for handling coordinates, physical units, time systems, FITS file I/O, table manipulation, and complex cosmological calculations. Use it for any workflow involving celestial data processing.

Biopython is a comprehensive Python library designed for molecular biology and bioinformatics. It provides powerful tools for sequence manipulation (DNA, RNA, Protein), parsing standard biological file formats (FASTA, GenBank, PDB), accessing external databases like NCBI (PubMed, GenBank) via Entrez, and performing complex analyses such as sequence alignment, phylogenetic tree construction, and structural bioinformatics. Ideal for building robust, automated bioinformatics pipelines.

This skill provides programmatic access to the CZ CELLxGENE Census, a comprehensive, versioned repository containing over 61 million single-cell genomics records from human and mouse. It is essential for conducting large-scale cross-dataset analyses, querying gene expression data across diverse tissues, cell types, and diseases, and training machine learning models in bioinformatic research. It supports both in-memory and out-of-core computation using advanced libraries like scanpy.

Specialized skill for generating professional, evidence-based Clinical Decision Support (CDS) documents for pharmaceutical and clinical research. It performs biomarker-stratified patient cohort analyses, generates evidence-based treatment guidelines (including GRADE grading), and produces publication-ready LaTeX/PDF reports optimized for regulatory submissions and drug development.