Skill UI

This module provides minimal working examples for the OpenEvidence API, designed for clinical intelligence applications. Users can perform complex clinical queries to get treatment recommendations, check potential drug interactions based on patient context, and search for authoritative medical guidelines from major health organizations (e.g., ACC/AHA). Ideal for developing evidence-based healthcare decision support systems.

This advanced skill acts as a hybrid router for complex research queries. It deterministically classifies the user's intent, delegating to specialized modules (e.g., funding grants, academic literature, patent search, company dossier) when applicable. If no specialist fits, it executes a robust fallback plan, synthesizing multi-source information into a detailed, cited briefing or document. Ideal for comprehensive investigative research across multiple domains.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.