Skill UI

This skill provides comprehensive guidance and established patterns for conducting rigorous scientific research and complex data analysis. It helps users structure their thinking process, ensuring a systematic and methodical approach to scholarly inquiry. Ideal for academic projects, literature reviews, and complex data interpretation.

Evaluate scientific claims, experimental design, and evidence quality using GRADE and Cochrane risk-of-bias frameworks. Identifies biases, confounders, statistical pitfalls, and logical fallacies, and guides constructive critical analysis of research papers.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.