Skill UI

This technique leverages structured data from music awards (like Grammy nominations and wins) to act as powerful search anchors. By extracting clues such as the award category, year, and status (nominated/won), users can cross-reference potential musicians against verifiable award databases. This method significantly narrows down the pool of candidates, allowing for highly accurate identification and verification of artists.

A powerful tool for scientific literature review and evidence synthesis. This skill searches a curated database of full-text scientific papers, going beyond traditional abstracts. It extracts and returns highly structured, quantitative data, including methodologies, quantitative results, sample sizes, quality scores, and 25+ metadata fields per paper, making it ideal for meta-analyses and systematic reviews.

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.

Provides comprehensive tools for academic research, allowing users to search databases like Google Scholar and PubMed. It extracts accurate metadata, validates citations, and generates standard formats like BibTeX. The skill ensures reference accuracy and supports the creation of scientific schematics to enhance document visualization.

gget is a comprehensive command-line tool and Python package offering unified access to over 20 genomic databases. It facilitates quick lookups for gene information, sequence analysis (BLAST), protein structures, and expression data, supporting both interactive CLI use and advanced programmatic Python workflows for biological research.

This skill provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

Pymatgen is a comprehensive Python toolkit designed for computational materials science. It enables users to create, analyze, and manipulate complex crystal structures, calculate electronic properties (DOS, band structures), simulate phase transitions, and interact with large material databases like the Materials Project. Ideal for solid-state physics, chemistry, and advanced materials research.

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

A comprehensive guide to implementing advanced user behavior tracking and real-time data synchronization with Algolia. This guide demonstrates how to use the Insights API to track clicks, conversions, and views, and how to build event-driven pipelines using database change listeners (e.g., Prisma middleware) to keep your Algolia index perfectly synced with your core application data. Essential for deep search analytics and relevance tuning.

A comprehensive mentor guiding users through the entire lifecycle of academic literature reviews. It teaches systematic search strategies (PICO/PCC), critical appraisal methods, and advanced synthesis techniques. The skill covers searching major English and Chinese databases (e.g., PubMed, Web of Science, CNKI), organizing findings, and effectively utilizing AI research tools like Elicit and Semantic Scholar to build robust, evidence-based research papers.

This comprehensive guide provides a production-ready reference architecture for implementing scalable search using Algolia. It details best practices for index design (including multi-index strategies), establishing robust data pipelines from source databases, structuring the service layer (search, indexing, settings), and integrating the frontend for optimal performance.

Provides techniques and indicators for researching music bands from non-Western cultures (e.g., African, Asian, Middle Eastern). This guide helps users move beyond mainstream Western databases, offering search strategies for bands whose information is often found on regional sites or through unique contextual clues like recording conditions or historical milestones.