Skill UI

Provides automated guidance for ab test analyzer workflows, covering SQL queries, visualization, statistical analysis, and BI validation so teams can run reliable experiments within data analytics contexts.

Guides teams through planning, designing, and running statistically sound A/B tests with hypothesis framing, sample sizing, metric selection, implementation strategies, and analysis checkpoints for systematic growth experimentation.

Auto-activating data analytics skill that guides anomaly detection tasks, covering SQL, visualization, statistical analysis, and business intelligence best practices.

Automates churn analysis workflows by offering guidance, best practices, and production-ready code for SQL, visualization, statistical work, and BI outputs within data analytics projects.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

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

Automated retention calculator guidance within data analytics workflows, assisting with SQL, visualization, BI, and statistical analysis to produce and validate production-ready outputs for retention insights.

Generates publication-ready clinical decision support documents for pharma and clinical research, covering biomarker-stratified cohort analysis, evidence-graded treatment recommendations, statistical comparisons, and regulatory-ready LaTeX/PDF outputs.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

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.