Skill UI

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.

A comprehensive, expert-level workflow for debugging complex software issues in production environments. This skill guides users through initial triage, advanced observability data collection (APM, distributed traces, logs), intelligent hypothesis generation, and full root cause analysis. Use it when dealing with intermittent failures, performance bottlenecks, or needing a complete fix and prevention plan.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

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.

Statsmodels is a comprehensive Python library dedicated to advanced statistical modeling and econometrics. It provides tools for rigorous statistical inference, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM), mixed-effects models, and time series analysis (ARIMA). Use this skill for detailed diagnostics, coefficient estimation, hypothesis testing, and producing publication-ready statistical reports in fields like econometrics and biostatistics.

Guides PMs through a full discovery cycle—from framing a hypothesis to validating solutions—by combining problem definition, customer interviews, synthesis, and experiments so teams can test assumptions before investing in build work.

Guides product teams to convert major initiatives into structured hypotheses that spell out the action, beneficiary, outcome, and validation steps so uncertainties surface before roadmaps or delivery planning.

Defines and documents Proof of Life probes—lightweight, disposable experiments that test a single risky hypothesis fast so teams learn harsh truth before building production software.

Guides experts through auditing and experimenting on non-registration forms to boost completion rates, covering assessment, design, copy, layout, and hypothesis testing for lead, contact, demo, survey, or checkout flows.

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.