Skill UI

This meta-skill automates the entire process of transforming raw Excel data into structured business insights. It reads the raw data, performs advanced trend and anomaly analysis, writes the resulting findings to a dedicated 'Insights' sheet, and persists key performance indicators (KPIs) to long-term memory. Ideal for comprehensive business reviews and data-driven decision-making.

This skill provides a single CLI interface to query numerous web search engines, including Brave, DuckDuckGo, Bing, Baidu, Sogou, and 360. It runs parallel searches, aggregates the results from various sources, and normalizes them into a uniform JSON format. Ideal for deep research, fact-checking claims across multiple platforms, or comparing global vs. regional search results.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This tool reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.

This utility reads structured JSON data from multiple search engines (multi-search-engine JSON) provided via standard input. It processes this academic data and converts it into a minimal, standardized BibTeX format, primarily generating @misc{} entries. This feature is ideal for automating the creation of a bibliography or reference list for scholarly papers, enhancing research workflow efficiency.

This advanced helper assists users in writing, debugging, and validating complex formulas, pivot tables, and array functions for platforms like Excel and Google Sheets. It ensures formulas are robust by handling common edge cases (e.g., blank rows, mixed data types, time zone quirks) and provides detailed, worked examples. It also manages cross-platform dialect translation.

This comprehensive guide provides advanced skills and references for writing, debugging, and systematically optimizing DAX in semantic models. It covers deep architectural topics, including engine internals, multi-tiered optimization workflows, and best practices for performance profiling. Use it to troubleshoot slow queries and ensure robust data model performance across Power BI Desktop and Fabric Service.

Deneb is a certified custom visual for Power BI, enabling users to embed and author complex, declarative data visualizations using Vega and Vega-Lite specifications. This skill guides the entire PBIR workflow, covering spec writing, data binding rules, complex JSON injection (including escaping), and validation, allowing for advanced control beyond standard Power BI charts.

This skill traces downstream dependencies from a core semantic model to all connected reports and assets across multiple Fabric workspaces. It is essential for impact analysis before making structural changes, auditing data flow, or mapping cross-workspace report connections. Use it to identify which reports rely on a specific dataset, ensuring minimal disruption during development or maintenance.

A comprehensive command-line interface (CLI) for the entire Power BI report lifecycle. It enables users to explore, build, format, validate, and publish reports using structured commands (`pbir`). The tool supports advanced features such as managing connections to semantic models, simulating desktop interactions (refresh, screenshot), and enforcing iterative development best practices for robust data reporting workflows.

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.