Skill UI

This skill provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

Direct Python helpers for KEGG's REST API, covering database metadata, listing, search, retrieval, ID conversion, linkage, and drug interactions to power pathway and gene analysis.

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.

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

Timesketch is a powerful, open-source tool for security teams to build collaborative forensic timelines. It ingests, normalizes, and analyzes multi-source event data—including logs, artifacts, and Plaso outputs—to reconstruct complex attack chains and document incident investigations. Ideal for joint analysis and deep dive DFIR work.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

This comprehensive guide provides advanced workflows for interacting with ClickHouse. It covers efficient bulk data insertion (CSV, JSON), complex analytical querying (funnel analysis, retention), implementing materialized views for pre-aggregation, and utilizing powerful window functions. Ideal for building high-performance OLAP dashboards and executing large-scale data transformations.

A comprehensive workflow for scraping and extracting structured data from Google search results using the SerpApi. It captures all key components, including organic listings, knowledge graphs, answer boxes, People Also Ask (PAA), local pack results, and related searches. Ideal for SEO monitoring, market research, competitive analysis, or building search-powered features that require deep, structured data insights.

Guidance for designing, building, refreshing, and reviewing Power BI / Analysis Services tabular models from the terminal. Covers star schema, relationships, measures, calculation groups, roles, storage modes, incremental refresh, and model optimization. Routes every operation to the narrowest capable tool: te CLI first, then TOM or MCP, then TMDL. Ensures model quality, performance, and readiness for Copilot/AI.

Perform a priori power analysis to determine required sample sizes for studies. Supports closed-form calculations for t-tests, ANOVA, and regression, plus Monte Carlo simulations for complex models like mixed models and survival analysis. Helps justify sample sizes for grants and protocols by calculating minimum detectable effects and power curves.

This guide details the principles of conducting rigorous Monte Carlo simulations for econometric theory papers. It instructs users on designing simulations that test finite-sample behavior, ensuring that asymptotic approximations are useful and accurate across boundary cases (e.g., weak identification, heavy tails). Emphasis is placed on reproducibility, comprehensive metrics (bias, coverage, power), and ensuring the simulation supports the core theory.