Skill UI

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

This skill generates a sophisticated, single-screen consumer dashboard mimicking a high-end lifestyle publication's dating column. It moves beyond simple swiping mechanics to visualize complex social metrics, mutual matches, and connectivity trends using editorial typography and restrained design. Ideal for concepts requiring data-driven storytelling about personal or community relationships.

This tool performs deep pathfinding traversal within a knowledge graph, starting from a specified seed entity. It expands outwards through causal edges, calculates the cumulative relevance score for each path, and synthesizes the top results into a comprehensive report. Ideal for performing impact analysis, mapping complex dependency chains, or understanding abstract system relationships.

This skill clusters a collection of vectors (embeddings, e.g., code snippets) within a designated namespace using graph community detection algorithms (Spectral/Louvain). It is essential for discovering underlying themes, identifying natural groupings, and analyzing the relationships within large, unstructured vector datasets. Use it when you need to organize embeddings based on semantic relatedness rather than simple keyword matching.

This skill enables the embedding of hierarchical datasets, such as dependency trees, ontologies, and module structures, into the Poincare ball model. Hyperbolic space is uniquely suited for capturing deep, non-linear relationships, allowing the preserved geodesic distance to accurately reflect structural depth and similarity within a hierarchy. Ideal for analyzing code architecture, complex knowledge graphs, and taxonomy.