Skill UI

Automated retention calculator guidance within data analytics workflows, assisting with SQL, visualization, BI, and statistical analysis to produce and validate production-ready outputs for retention insights.

Automates clustering workflows on supplied datasets with K-means, DBSCAN, or hierarchical algorithms, handling preprocessing, validation, metrics, and visualizations to expose groupings and anomalies.

Automates SQL query optimization guidance for data analytics workflows, offering best practices, pattern advice, and production-ready code for queries, BI, and visualization tasks whenever the user invokes SQL query optimizer support.

Automates guidance for statistical significance calculations in data analytics, offering triggered best practices, example implementations in SQL, visualization, and BI workflows, plus validation against common standards.

This skill generates code to create complex, interactive 3D scenes and visualizations on the web using the Three.js library and WebGL. It covers the entire development lifecycle, including setting up the core components (Scene, Camera, Renderer), defining geometry, applying materials, adding lighting, and implementing animation loops. Ideal for web developers needing to display product mockups, scientific data visualizations, or immersive user experiences.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

Automated help for data analytics visualization best practices, offering step-by-step guidance, industry patterns, production-ready code, and validation for dashboards and reports.

deepTools is a Python command-line suite for NGS workflows, converting BAM files into normalized coverage, running QC (fingerprint, correlation, PCA), comparing samples, and rendering heatmaps or profiles for ChIP-seq, RNA-seq, and ATAC-seq analyses.

Automates detection and deep exploratory analysis across 200+ scientific data formats, assessing quality, extracting metadata, and recommending visualizations while generating markdown reports that guide downstream workflows and highlight structure and distributions.

Offers MATLAB and GNU Octave guidance for matrix math, data import/export, visualizations, statistics, signal processing, differential equations, and general scripting—helpful when writing or translating MATLAB/Octave code and running scripts under either interpreter.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.