Skill UI

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.

A comprehensive guide and toolset for managing the entire lifecycle of semantic models (Power BI/Analysis Services). Use this skill for designing star schemas, building measures, optimizing performance, setting up incremental refreshes, and auditing models against best practices, all driven through the terminal CLI.

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 powerful, cross-platform Command Line Interface (CLI) tool for managing and automating semantic models across various platforms (Power BI, Azure AS, Fabric). It allows users to scaffold, inspect, edit, validate, deploy, refresh, query, and migrate complex data models entirely from the terminal. Essential for integrating data model operations into CI/CD pipelines and performing programmatic data interaction.

A comprehensive guide for searching documentation across multiple BI sources (TE docs, DAX.guide, SQLBI, Power BI/Fabric) using the pbi-search CLI. It also details the structure and use of critical configuration files, such as .tmuo, Preferences.json, and UiPreferences.json, essential for advanced data modeling and deployment.

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.

Provides expert guidance for authoring and editing Tabular Model Definition Language (TMDL) files directly within PBIP projects. This skill is useful for making quick text-level fixes (like descriptions, format strings, or adding measures) when specialized tools (like Tabular Editor CLI or Power BI Desktop) are unavailable, especially for Git workflow or manual adjustments.

This index serves as the central router and map for the comprehensive C-level advisory bundle. It structures high-stakes business processes by indexing 14 core C-suite roles (CEO, CFO, CTO, etc.), orchestration capabilities, and cross-cutting functions (e.g., board meetings, onboarding). It guides users on where to start for strategic planning, corporate governance, and complex decision-making.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

This agent provides a critical framework for navigating corporate environments, contrasting internal jargon and process visibility (Ding In) with objective, real-world evidence and user experience (Ding Out). It helps users move beyond subjective 'feelings' or polished reports, structuring complex tasks into verifiable components: Goal, Acceptance Criteria, Action, Evidence, and Risk. Ideal for project management, status reporting, and critical thinking in a corporate setting.

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.