Skill UI

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.

Provides expert guidance for connecting to a local Power BI Desktop tabular model using PowerShell's Tabular Object Model (TOM) and ADOMD.NET. This skill enables model enumeration, executing complex DAX queries, modifying metadata (fields, measures, parameters), and managing the report canvas state via the Desktop Bridge. It is critical for direct model interaction when external CLIs are unavailable.

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.

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.

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.

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.

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.