Skill UI

Provides production-ready Apache Airflow DAG patterns covering operators, sensors, testing, deployment, and observability for orchestrating dependable data pipelines.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

Guides quantitative analysts through building financial models, backtesting strategies with realistic assumptions, analyzing risk metrics, optimizing portfolios, and supporting statistical arbitrage using pandas, numpy, and scipy.

Automates hypothesis generation and testing on tabular datasets by blending LLM-driven data insights with literature synthesis, enabling researchers to explore patterns, validate competing claims, and surface non-redundant ideas across domains like deception detection or content analysis.

Guides hypothesis testing, regression, correlation, and Bayesian analyses with automated assumption checks, diagnostics, power planning, and APA-style reporting so researchers can choose proper tests, interpret effect sizes, and document academic findings.

Statsmodels is a Python library for detailed regression and time-series models, giving econometrics-grade inference, diagnostics, residual analysis, model comparison, and forecasting tools when you need publication-ready tables and rigorous testing.