Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Automates the creation of Airflow DAGs for ETL and streaming data workflows, offering best-practice guidance, production-ready code samples, and validation against common pipeline standards.

A comprehensive guide to building production-ready Apache Airflow Directed Acyclic Graphs (DAGs). This skill covers best practices for operators, sensors, testing, deployment, and designing robust data pipelines. Use this when orchestrating complex workflows, managing ETL processes, or ensuring reliable batch job execution in a production environment.

Guides you through building Airflow operators for data pipelines with automated best practices, production-ready code/config generation, and validation for ETL, transformation, orchestration, and streaming scenarios.

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

This powerful tool calculates detailed cryptocurrency tax obligations by processing transaction history from multiple exchanges. It supports various cost basis methods (FIFO, LIFO, HIFO) to accurately determine capital gains and losses. Key features include identifying staking, airdrop, and mining income, and generating comprehensive reports compatible with Form 8949, simplifying complex tax filing processes.

This skill guides the construction of robust, production-grade backtesting systems for financial trading strategies. It helps users systematically validate performance, avoid common biases (like look-ahead bias), and implement advanced techniques such as walk-forward analysis, ensuring reliable performance estimates before live deployment.

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 comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

This comprehensive guide provides structured steps and code examples for upgrading Databricks Runtime versions and performing critical data migrations. It covers best practices for version compatibility checking, transitioning from Hive Metastore to Unity Catalog using methods like SYNC and CTAS, and updating deprecated API endpoints, ensuring a smooth platform evolution for data pipelines.