Skill UI

This comprehensive framework allows users to validate and test both crypto and traditional trading strategies against extensive historical market data. It calculates essential performance metrics (Sharpe Ratio, Sortino Ratio, Max Drawdown), simulates trade execution, generates equity curves, and includes advanced parameter grid search optimization tools for robust strategy validation.

A comprehensive guide detailing optimization techniques for Databricks environments. It covers optimal cluster sizing for various workloads (ETL, ML, Streaming), advanced Spark configurations (AQE, memory management), and deep data optimization strategies for Delta Lake, including Z-Ordering and Liquid Clustering. It also provides methods for query plan analysis and join optimization.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

Guides a Power BI DAX expert through performance, readability, best-practice, and maintainability reviews; recommends variable reuse, context tuning, and error handling; then outputs an optimized formula plus justification for analytics models.

A comprehensive expert prompt for evaluating Power BI data models. It guides the assessment of model architecture (Star Schema, relationships), storage optimization (Import, DirectQuery, Dual), data quality, performance scalability, and governance best practices. Use this when reviewing an existing BI model to ensure it is robust, maintainable, and highly performant.

Provides systematic Power BI performance troubleshooting guidance covering model optimization, DAX design, report layout, and capacity diagnostics, plus tool-driven measurement tips for faster page loads, interactions, and query execution.

High-performance Python DataFrame library built on Apache Arrow. Covers expression-based API, lazy query optimization, streaming out-of-core processing, aggregations, window functions, joins, and pandas migration for ETL and analytics pipelines.

This skill provides a comprehensive framework for evaluating Power BI reports. It assesses report quality, usage adoption rates, performance metrics, and structural integrity across different lifecycle stages (development, testing, active use). It analyzes usage data, audience reach, and performance bottlenecks, generating actionable feedback and optimization recommendations to ensure reports are effective and maintainable.

ClickHouse database patterns for high-performance analytics, including table design, query optimization, bulk inserts, materialized views, and performance monitoring.

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

This skill diagnoses latency and throughput in weather data workflows by measuring stages like discovery, transfer, parsing, and rendering separately. It identifies bottlenecks before code changes, supporting profiling and validation of performance fixes. It ensures accurate optimization by isolating slow steps in the pipeline.