Skill UI

A comprehensive guide for using C# scripting within Tabular Editor to programmatically manipulate and automate Power BI semantic models. This skill supports bulk metadata operations, creating calculation groups, managing security roles, and advanced data model restructuring using the TOM API and LINQ.

Guidance for designing, building, refreshing, and reviewing Power BI / Analysis Services tabular models from the terminal. Covers star schema, relationships, measures, calculation groups, roles, storage modes, incremental refresh, and model optimization. Routes every operation to the narrowest capable tool: te CLI first, then TOM or MCP, then TMDL. Ensures model quality, performance, and readiness for Copilot/AI.

Perform a priori power analysis to determine required sample sizes for studies. Supports closed-form calculations for t-tests, ANOVA, and regression, plus Monte Carlo simulations for complex models like mixed models and survival analysis. Helps justify sample sizes for grants and protocols by calculating minimum detectable effects and power curves.

A comprehensive reference guide providing quantitative benchmarks and formulas for system architects. It covers user-to-RPS conversion, storage estimation, bandwidth calculation, known scale limits of major technologies (databases, queues, caches), and capacity planning for various user loads (1K DAU to 10M DAU). Essential for designing scalable, high-availability applications.

This skill scrapes Substack Notes analytics by extracting key engagement metrics (likes, comments, restacks) and content details. It overcomes the lack of a public API, allowing users to analyze performance data from a specified date range into a highly formatted, actionable Excel spreadsheet. The output includes detailed per-note data, total engagement calculations, and summary statistics.

This skill calculates model interpretability for algorithmic trading signals using PageRank. It constructs a feature-contribution graph from an LSTM or Transformer signal and executes a single-entry PageRank calculation. The result identifies the top-K features that most significantly drove the prediction, providing a "regulator-grade" audit trail essential for compliance with regulations like the EU AI Act.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

This sophisticated tool optimizes marketing budget allocation across multiple channels using performance data, ROI calculations, and diminishing returns modeling. It analyzes current spend efficiency (ROAS, CPA) and generates a data-driven reallocation plan. Ideal for justifying spend shifts, maximizing investment returns, and creating phased implementation timelines for measurable growth.

A comprehensive guide for finalizing academic manuscripts in finance and economics. It enforces strict formatting rules for regression tables (three-line style, standard error presentation), requires detailed variable definition tables with calculation formulas, mandates specific data source citation, and ensures high-resolution, professionally designed figures, moving beyond simple copy-pasting of results.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

A comprehensive guide for structuring academic empirical research projects to ensure the full reproducibility of findings. This methodology covers standard folder layouts, master scripting, data provenance documentation, and best practices required for passing rigorous peer review scrutiny, thereby preventing data loss and calculation errors during revisions.

This guide provides structural best practices for writing highly technical academic papers, particularly for top-tier conferences. It teaches authors how to critically distinguish between the core material that must be visible in the main body (for the referee to follow and judge) and the auxiliary details, complete proofs, or routine calculations that can be safely deferred to an appendix. Following this structure prevents common writing pitfalls, such as 'hollow bodies,' ensuring the paper is both highly readable and technically rigorous.