Skill UI

This comprehensive guide provides step-by-step instructions for upgrading Snowflake drivers (Node.js and Python) and managing complex data warehouse migrations. It covers prerequisite checks, reviewing release notes, handling common API breaking changes (e.g., cursor types, async connections), and performing post-migration validation to ensure application stability during platform or edition upgrades.

This skill simulates a Chief Data Officer's deep interrogation, pressure-testing any plan touching core data strategy. Use it before approving ML model training runs, selecting data infrastructure (warehouse vs. mesh), productizing data assets, or making major team hires. It ensures that all data decisions are grounded in clear business decisions, compliance, and structural necessity.

Provides high-level strategic guidance for founders and Chief Data Officers (CDOs) in startups. This skill covers critical decisions such as AI training data rights and consent provenance, selecting optimal data architectures (warehouse, lakehouse, data mesh), valuing customer data as a strategic asset for M&A or fundraising, and planning data team organizational evolution. It focuses exclusively on strategic decision-making, not tactical engineering.

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 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.

Use this skill for optimizing large-scale data ingestion, ETL, and data warehouse loading. It helps diagnose bottlenecks (such as extraction speed, network transfer, or loading time) and implements best practices like idempotent writes, native cloud scans, and checkpointing to achieve maximum processing speed while strictly guaranteeing data integrity and traceability.

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.