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 correlation analyzer workflows by offering best-practice guidance, executable SQL, visualization tips, and statistical validation for data analytics requests.

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 models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

Automatically analyzes CI/CD performance metrics, compares them against baselines and thresholds, and flags statistical regressions so teams catch response-time or throughput degradations before deployment.

Automates pivot table creation in data analytics workflows, guiding SQL queries, visualization choices, statistical checks, and BI configurations whenever the user invokes pivot table creator support.

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.