Skill UI

This skill performs User and Entity Behavior Analytics (UEBA) to identify anomalous user activities such as impossible travel, unusual access patterns, and privilege abuse. It establishes behavioral baselines using SIEM logs and statistical analysis, helping SOC teams detect insider threats and compromised accounts by flagging deviations from normal user behavior. Use when monitoring security hygiene and suspicious activity is critical.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

Geniml provides a robust framework for running machine learning and statistical workflows over genomic interval sets. It treats coordinates, assemblies, and model artifacts as explicit contracts, enabling users to validate data structures (like BED files) and plan complex runs (such as Region2Vec or scEmbed) locally. The tool emphasizes reproducibility, adherence to biological data standards, and running without unnecessary network calls.

Seaborn is a high-level Python visualization library built on Matplotlib, designed specifically for creating publication-quality statistical graphics. It excels at dataset-oriented plotting, enabling users to perform complex multivariate analyses, visualize data distributions (like box plots, violin plots), and explore relationships with minimal code. It simplifies the process of generating multi-panel figures and statistical plots directly from pandas DataFrames.

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 orchestrates a complete, reproducible pipeline for bulk RNA-seq differential expression analysis. It guides users from raw FASTQ reads through QC, trimming, alignment (STAR/Salmon), quantification, generating a gene-level counts matrix, and concluding with differential expression analysis (DESeq2) and pathway enrichment. It ensures statistical soundness and reproducibility by connecting dedicated tools and enforcing proper experimental design.

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

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 guides the design of statistically rigorous A/B tests for optimizing product features, UI/UX changes, onboarding flows, and pricing models. It generates a complete test plan, including formulating hypotheses, calculating required sample sizes, estimating test duration, defining primary and guardrail metrics, and providing a detailed guide on interpreting results (ship, iterate, or reject).

This skill generates detailed, structured research protocols for various academic, clinical, or market research studies. It ensures all critical components—such as objectives, study design, participant criteria, methodology, data management, statistical analysis plan, and ethical considerations—are comprehensively documented, providing a robust framework for any scientific investigation.