Skill UI

This skill analyzes your data warehouse to pinpoint stale, unused, or redundant tables that contribute to unnecessary storage costs. It classifies waste patterns (e.g., Zombie, Temporary) and computes safety tiers, helping users understand which assets can safely be dropped or archived to optimize spending and improve data governance.

Diagnose and accelerate large data movement pipelines by isolating bottlenecks, benchmarking variants, and codifying the fastest path with proof of correctness. Use when ingestion, backfill, ETL, or warehouse loading is too slow and must get faster without losing data integrity.

This skill facilitates the extraction of population-scale (cohort-level) clinical text from Electronic Health Records (EHR) or FHIR data warehouses using the FHIR Bulk Data Access ($export) operation. It manages the asynchronous job kickoff, polling, and downloading of NDJSON files. The resulting data stream is designed to be fed in batches into OpenMed for mandatory de-identification and Named Entity Recognition (NER) analysis.

Expert data engineer specializing in scalable data pipelines, modern data warehouses, lakehouse architectures, and real-time streaming. Proficient in Apache Spark, dbt, Airflow, Kafka, and cloud-native platforms (AWS, Azure, GCP). Focuses on reliable, performant, and cost-effective data solutions with strong emphasis on data quality and governance.

Analyze data coverage gaps, create monitors for warehouse tables and AI agents. Provides use-case analysis, data quality monitoring, and agent observability. Helps identify unmonitored tables with anomalies and generate monitoring configurations via MCP tools.

Diagnose slow data pipeline jobs, expensive queries and latency regressions using Monte Carlo cross-platform observability, with a tiered approach that bridges jobs to tables and drills into root causes across Airflow, dbt, Databricks and warehouses.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

This skill analyzes data warehouses to identify stale, unused, or redundant tables that increase storage costs. It classifies waste patterns, computes safety tiers, and supports category drill-downs. Compatible with Snowflake, BigQuery, Redshift, and Databricks. Use it when users ask about storage optimization, cost reduction, or cleanup opportunities.

A static analysis tool that audits SQL code for common cost and performance anti-patterns (e.g., full-table scans, Cartesian joins). It calculates a query health score (0-100) for data warehouses like BigQuery, Snowflake, and Redshift, providing a prioritized, actionable plan with concrete fixes and estimated cost savings. Essential for FinOps and data quality assurance.