Skill UI

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

A comprehensive tool designed to extract structured data from any e-commerce platform. Use it to monitor competitor pricing, analyze customer reviews for sentiment, gather detailed product specifications, and discover seller networks. Ideal for market research, brand protection, and product management teams needing deep market insights.

Query the GTEx portal and API to retrieve tissue-specific gene expression, eQTL, sQTL, and variant-level regulatory data for interpreting GWAS loci and exploring gene regulation across 54 human tissues.

Query the Monarch Initiative API to explore disease-gene-phenotype associations across species, enabling rare disease gene discovery, phenotype-to-gene mapping, and cross-species modeling via integrated HPO and model organism data.

Transforms raw input into intelligent visualizations by selecting from 26 chart types, extracting required parameters from reference specs, and running a Node.js generator to return the final chart image URL and payload.

Analyzes Excel and CSV uploads with DuckDB, inspecting schemas, running SQL, summarizing statistics, and exporting results so users can explore structured data efficiently.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

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.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

Guides Power BI visualization and UX decisions—capturing business context, selecting optimal charts, specifying layouts, color, typography, and interaction patterns to create accessible, insight-driven dashboards across audiences.