Skill UI

This skill is designed to translate vague, colloquial, or descriptive biological details (such as behaviors, appearances, or cultural uses) into precise scientific or common species names. It is essential for constructing effective search queries in biological databases when the question does not use formal taxonomic names. By accurately identifying candidate species names from descriptive clues, users can drastically improve search relevance and accuracy.

PrimeKG is a comprehensive knowledge graph integrating over 20 primary databases to map complex biological relationships. It allows users to query multiscale data, including genes, drugs, diseases, and phenotypes. Ideal for drug discovery, identifying repurposing opportunities, and analyzing deep disease contexts in network pharmacology.

A comprehensive, production-ready reference architecture for building robust, scalable transcription services using AssemblyAI. This guide demonstrates best practices for layered system design, covering API endpoints, service logic, client management, and infrastructure integration (webhooks, database). It is ideal for designing new high-throughput speech-to-text applications.

This skill manages comprehensive identity verification using the Persona API. It covers government ID checks (passport, driver's license), selfie liveness detection, and database lookups (SSN, watchlist). Use this when implementing Know Your Customer (KYC) workflows, reviewing detailed verification results, or building custom approval logic based on multi-source identity data.

A comprehensive guide to implementing production-grade reliability patterns for Snowflake environments. Covers advanced techniques like Time Travel for point-in-time recovery, cross-region database replication, account failover groups for disaster recovery, and application-level resilience coding (e.g., connection failover logic). Essential for building fault-tolerant, highly available data pipelines.

Provides essential database abstractions and utility classes for robust API development. This module includes components like FilterUtil, ValueFilter, and ValueFilterRegistry, allowing developers to implement standardized, efficient, and complex data querying and filtering logic across various database contexts.

This skill performs advanced pathway and gene-set enrichment analysis crucial for interpreting gene lists (e.g., DE genes, screen hits). It distinguishes between Over-Representation Analysis (ORA) for thresholded lists and Gene Set Enrichment Analysis (GSEA) for full ranked lists. Supports scoring pathway activity (ssGSEA/GSVA) and utilizes major databases like GO, KEGG, Reactome, and MSigDB for comprehensive biological interpretation, ensuring publication-ready results.

This guide details the Hexagonal Architecture (Ports and Adapters) pattern, a robust method for decoupling core business logic from external infrastructure concerns. It teaches how to structure applications to make the domain model independent of databases, UI frameworks, or message queues. Implementing this pattern drastically improves testability, maintainability, and the ability to swap out external services without rewriting business rules.

This tool validates all pending database migrations to ensure correctness, safety, and adherence to best practices. Use it before applying migrations to catch critical issues like broken foreign key references, incomplete rollback logic, unintended destructive operations (e.g., DROP TABLE), and naming convention violations. It checks for rollback completeness, schema integrity, and idempotency.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.

Biopython is a comprehensive Python library designed for computational molecular biology. It provides robust tools for handling biological sequences (DNA, RNA, Protein), structural analysis (PDB), sequence alignment, phylogenetic tree construction, and interfacing with major public databases like NCBI (Entrez). Ideal for researchers performing advanced bioinformatics tasks.