Skill UI

This comprehensive guide covers the entire lifecycle of Power Query M expressions within semantic models. Learn how to author correct M code, optimize performance using query folding techniques, validate syntax, and test expressions against live data sources. It provides best practices for data filtering, type handling, and building robust, efficient data pipelines.

This skill enables the creation of advanced, highly customized visualizations directly within Power BI reports (PBIR). By utilizing powerful Python libraries like Matplotlib and Seaborn, users can process data via pandas DataFrames and render complex statistical charts (e.g., bar charts, line graphs) into static PNG images. It provides a structured workflow, covering script injection, validation, and best practices, significantly enhancing the depth and professionalism of business reports.

Provides a structured framework for auditing Power BI reports across three lifecycles: development, testing, and active use. It assesses report quality, data usage adoption (e.g., user reach, view trends), and technical performance (load times), generating actionable recommendations for developers and consultants aiming for optimal data insights.

Provides comprehensive guidance for managing the entire lifecycle of tabular data models (e.g., Power BI, Analysis Services). This skill covers design (star schema, relationships), building (measures, calculated groups), refreshing (incremental refresh), and auditing (performance, best practices). It enforces a systematic 'tool cascade,' routing operations through the dedicated CLI first, then to model servers, and finally to TMDL authoring, ensuring optimal model quality and adherence to industry best practices.

This guide provides comprehensive assistance for searching Tabular Editor documentation and understanding complex configuration files. It details the use of the powerful pbi-search CLI tool to query multiple sources (DAX, Power BI, SQLBI, etc.) and outlines the structure and critical rules for managing model-specific files like .tmuo and application preferences.

Provides expert guidance for authoring and manually editing TMDL (Tabular Model Definition Language) files within PBIP projects. This skill is essential for making quick, text-level fixes (e.g., descriptions, format strings, measures) when professional tools like Tabular Editor CLI are unavailable. It covers TMDL syntax, object nesting rules, and the structure of various model components.

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.

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.