Skill UI

Enables regression modeling via the regression-analysis-tool plugin by analyzing datasets, choosing linear or polynomial fits, validating models, and reporting metrics so users can predict outcomes or explore variable relationships.

Provides automated regression analysis guidance, best practices, code snippets, and validation for SQL, BI, visualization, and statistical workflows in data analytics projects.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series machine learning. It supports a wide range of temporal tasks, including classification, regression, clustering, forecasting, anomaly detection, and pattern recognition. Ideal for analyzing sequential data and extracting features from time-indexed observations.

This skill provides comprehensive guidance for classical machine learning using scikit-learn, the industry-standard Python library. It covers the full ML lifecycle, including supervised tasks (classification, regression), unsupervised tasks (clustering, dimensionality reduction), data preprocessing, robust model evaluation, and building scalable, production-ready ML pipelines.

This skill provides end-to-end guided statistical analysis for research data, covering hypothesis testing (t-tests, ANOVA, chi-square), regression, correlation, and modern Bayesian methods. It walks users through the entire research workflow, emphasizing proper assumption checking, calculating effect sizes, conducting power analysis, and generating reports in APA format.

Statsmodels is a comprehensive Python library for rigorous statistical modeling, enabling deep inference, diagnostics, and advanced econometric analysis. Use it for tasks like linear regression (OLS), generalized linear models (GLM), time series forecasting (ARIMA), and hypothesis testing, providing detailed results for academic and scientific reporting.

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.

This skill provides comprehensive tools for planning studies by calculating the required sample size, minimum detectable effect (MDE), and statistical power. It supports both closed-form statistical tests (t-tests, ANOVA, chi-square) and advanced simulation-based designs (mixed models, logistic/Poisson regression). Use this when planning a study, submitting an IRB protocol, or justifying sample size for a grant.

This skill helps diagnose performance bottlenecks, slow jobs, and expensive queries across various data platforms (Airflow, dbt, Databricks). It uses a tiered investigation approach, starting with broad discovery to pinpoint problematic jobs or queries, bridging to affected tables, and finally drilling down to identify root causes, latency trends, and change-related regressions.

Generates comprehensive quality intelligence reports by analyzing historical evaluation scores. This tool tracks content performance over time, identifies improving or declining content types, detects significant quality regressions against baselines, and provides prioritized, actionable recommendations for improving brand quality across various content dimensions.