Skill UI

Auto-activating aggregation helper skill for data analytics, following best practices for SQL, visualization and BI workflows, guiding queries/configs, and validating outputs.

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.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

Automates change-data-capture pipeline planning by guiding developers through ETL, orchestration, and streaming best practices, then generating validated configs and code when “cdc pipeline creator” is requested.

This comprehensive guide identifies and explains common anti-patterns, gotchas, and integration mistakes when using Clay's data enrichment platform. It details the root causes of credit waste, workflow failures, and data loss, providing actionable fixes for auditing configurations, onboarding new team members, or optimizing existing pipelines.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

Automates dashboard layout planning for data analytics tasks, guiding best practices, generating robust configs, and validating layouts for SQL-driven BI scenarios.

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output 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 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.