Skill UI

Comprehensive guidelines covering best practices for Power BI report development. This includes establishing a consistent design identity, applying visual hierarchy rules (like the 3-30-300 rule), managing color discipline, ensuring accessibility, and structuring pages based on their intended question or purpose. It helps transform raw data into professional, actionable, and user-friendly reports.

A comprehensive guide for authors and developers on managing, writing, and validating Power Query M expressions within semantic model partitions. Covers critical concepts such as Query Folding for performance optimization, best practices for filtering and type handling, and advanced techniques for testing M code using the Power Query API.

A comprehensive guide and set of tools for embedding dynamic Python visualizations (using matplotlib and seaborn) into Power BI reports. It details the structured workflow, including script injection, data handling (dataset DataFrame), and mandatory best practices for generating static PNG output. Ideal for advanced data analysis and complex reporting.

This guide details how to integrate R code, primarily using the powerful ggplot2 library, into Power BI reports. It enables the creation of advanced, static, and publication-quality statistical visualizations. The process covers the technical workflow, including setting up the JSON structure, writing the mandatory R script (e.g., using `print(p)`), handling data injection, and adhering to best practices for professional BI reporting.

This comprehensive guide details the programmatic management of semantic model refreshes using the Power BI Enhanced Refresh REST API and Fabric CLI. It covers the entire refresh lifecycle, including resolving model IDs, validating baseline data, triggering various refresh types (full, data-only, calculate), monitoring status, and performing robust post-refresh validation. Essential for automating ETL and data pipeline processes in a modern data platform.

Provides a structured, actionable review of Power BI reports across their entire lifecycle—from development to active use. The tool assesses report effectiveness, usage adoption, data performance (load times), and structural integrity. It generates a prioritized list of findings and concrete recommendations, helping users determine if reports are worth maintaining, optimizing, or retiring.

An interactive workflow designed to audit and standardize naming conventions within Power BI semantic models stored as TMDL files. It ensures that tables, columns, measures, and display folders adhere to consistent, human-readable, and business-aligned standards. The tool guides users through gathering critical business context before auditing and applying complex, cross-file reference updates to maintain data integrity.

This skill enables the generation of rich, inline SVG graphics directly within Power BI reports. By writing DAX measures that return SVG markup strings and setting the data category to ImageUrl, users can visualize complex charts (like sparklines, gauges, and progress bars) within standard visuals such as Tables, Cards, and Matrices. It guides users through the entire process, including recommended best practices like using existing UDF libraries and adhering to strict VAR-based DAX structures.

A comprehensive guide to the Tabular Editor 2 command-line interface (CLI). It provides essential syntax and usage examples for automating data model management. Key functions include deploying models to XMLA endpoints, executing inline C# scripts, running Best Practice Analyzer (BPA), validating schemas, and integrating model processes into CI/CD pipelines in Windows environments.

This tool guides users through the critical process of designing experiments *before* data collection. It helps select appropriate structures—such as factorial, crossover, or blocked designs—to ensure that collected data is interpretable and allows for robust causal inference. Use it when planning scientific studies, assigning subjects, or optimizing responses across multiple factors.

Calculates statistical power and required sample sizes for study planning. It supports both closed-form methods (for standard tests like t-tests, ANOVA) and simulation-based methods (for complex models like mixed models or survival analysis). Use this skill to determine the minimum detectable effect (MDE) or justify sample sizes for grants and IRB protocols before data collection.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.