Skill UI

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.

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 provides a comprehensive guide to configuring event-driven workflows and robust notification systems for Databricks jobs. It covers setting up various notification destinations (Slack, Teams, Email, PagerDuty) and triggering alerts based on job lifecycle events (success, failure, start). Furthermore, it demonstrates advanced monitoring using system audit tables and creating automated SQL alerts for critical data pipeline monitoring.

Enables Claude to apply anomaly-detection-system workflows for spotting outliers, fraud, or unusual patterns across datasets, selecting tailored algorithms like Isolation Forest and summarizing deviations for any data monitoring or security use case.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.

A comprehensive tool for querying and analyzing deep blockchain data across multiple EVM-compatible networks, including Ethereum, Polygon, Arbitrum, and Optimism. It supports tracking transactions by hash, checking address balances, inspecting blocks, and resolving ERC-20 token holdings. Ideal for crypto analysts, developers, and researchers requiring programmatic, detailed insights into on-chain financial activity and protocol interactions.

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

This tool detects profitable arbitrage opportunities across centralized exchanges (CEX), decentralized exchanges (DEX), and cross-chain markets in real-time. It aggregates price data, calculates net profit after fees, and identifies various arbitrage paths, including direct, triangular, and cross-chain opportunities. It is designed for quantitative traders needing to monitor market efficiency and exploit price discrepancies.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.