Skill UI

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.

Analyzes data coverage gaps and creates monitors for warehouse tables and AI agents. Covers use-case analysis, data monitor creation, monitors-as-code YAML, and agent observability.

Diagnoses data pipeline performance issues such as slow jobs, expensive queries, and latency trends using Monte Carlo's cross-platform observability. Works across Airflow, dbt, Databricks, and warehouse query engines to find bottlenecks, detect regressions, and identify root causes.

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 provides actionable cleanup recommendations. Supports Snowflake, BigQuery, Redshift, and Databricks. Ideal for reducing cloud infrastructure expenses and optimizing data governance.

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.

This skill helps plan and review read-only data warehouse analysis with explicit scope, privacy, and validation checks. It turns business questions into reproducible analysis plans without assuming specific schemas. It identifies needed data, reviews proposed queries for risks like grain or privacy, and ensures results are communicated accurately. Suitable for authorized data warehouse usage, query review, and analysis documentation while avoiding administrative tasks.

Prove data/AI workflows end-to-end on tiny samples, find cost-saving wins, then scale within budget using cost-gated steps. Ideal for multi-stage pipelines involving paid compute like LLM tokens or warehouse credits.