Skill UI

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

The HypoGeniC skill facilitates the iterative proposal and scoring of textual hypotheses from labeled datasets using LLMs. It provides tools for generating candidate hypothesis banks and task-prediction statistics. Crucially, it mandates a rigorous, local review workflow, emphasizing dataset auditing (checksums, schemas), configuration validation, and explicitly separating candidate results from definitive scientific conclusions.

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 core skill is designed for academic publishing, combining deep AI research with structured, professional writing. It guides the creation of manuscripts using IMRAD structure and handles complex citation styles (APA, AMA, etc.). Crucially, it mandates the generation of graphical abstracts and rich technical schematics to ensure the highest level of visual rigor for research papers.