Skill UI

GeoPandas extends pandas for geospatial vector data, enabling reading/writing shapefiles, GeoJSON, GeoPackage and PostGIS, performing CRS management, spatial joins, overlays, buffering, area/distance calculations, and producing static or interactive choropleth maps.

Plotly is a Python graphing library for interactive, publication-quality visuals that bring hover, zoom, animation, and dashboard-ready exports to exploratory analysis and presentations.

An AI-driven comprehensive health analysis system that integrates multi-dimensional datasets, including biometrics, lifestyle, and medical history. It excels at anomaly detection, predicting various health risks (e.g., CVD, Diabetes), providing personalized, evidence-based recommendations, and generating detailed, interactive health reports.

This comprehensive tool helps users set, track, and optimize personal health goals using deep data analysis. It validates goals against the SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) and tracks progress by correlating diverse health data, including nutrition, exercise, and sleep patterns. Key features include habit formation analysis, motivation assessment, identifying roadblocks, and generating interactive reports with ECharts visualizations, providing a holistic view of wellness journeys.

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

AWS Detective automatically analyzes log data from CloudTrail, VPC Flow Logs, GuardDuty, and EKS audit logs to build interactive behavior graphs. This tool enables security analysts to investigate suspicious activity, trace lateral movement across accounts, profile compromised entities (IAM users, EC2 instances), and reconstruct complex attack timelines without manual log parsing. Ideal for triaging GuardDuty findings or investigating suspected AWS compromises.

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 skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

gget is a comprehensive command-line and Python package providing unified access to over 20 major bioinformatics databases. Users can perform deep analyses across genomics, proteomics, and disease associations. Key functions include gene information lookups, sequence alignment (BLAST), structural prediction (AlphaFold), single-cell analysis, and enrichment studies, supporting both interactive queries and automated pipelines.

Matplotlib is Python's foundational library for creating static, animated, and interactive visualizations. It provides fine-grained control over every plot element, supporting diverse plot types (line, scatter, heatmap, etc.) and complex multi-panel layouts. Users should leverage the object-oriented API for building robust, publication-quality figures and exporting them to formats like PNG, PDF, or SVG.

This skill provides expert, guided assistance for creating, auditing, and improving Best Practice Analyzer (BPA) rules for Power BI semantic models. It utilizes an interactive, double-diamond discovery process to gather deep context about your organizational standards, model structure, and business goals before generating tailored, reusable data validation and governance rules.

An interactive workflow for auditing and standardizing naming conventions (tables, columns, measures) in Power BI semantic models (TMDL). It ensures names are human-readable, consistent, and aligned with core business terminology, addressing technical issues like abbreviations, mixed casing, and inappropriate technical prefixes.