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sql-queries

Write portable SQL across Snowflake, BigQuery, Databricks, and PostgreSQL dialects.

Updated Aug 27, 2026
Search Tags:#performance#queries#sql#cte#dialects#window-functions#data-warehouses
One-click install
npx skills add https://github.com/PoyoPoak/FastAPI-App-Template --skill sql-queries-poyopoak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/PoyoPoak/FastAPI-App-Template/tree/main/.agents/skills/sql-queries
Command: npx skills add https://github.com/PoyoPoak/FastAPI-App-Template --skill sql-queries-poyopoak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Write correct, portable SQL across Snowflake, BigQuery, Databricks, PostgreSQL, and other major dialects to reduce translation errors and accelerate analytics work.

Core Features & Use Cases

  • Dialect-aware SQL templates and examples for common analytical patterns across warehouses.
  • Guidance on translating queries between dialects and optimizing performance with CTEs, window functions, and aggregations.
  • Use cases include migrating queries between platforms, refactoring slow SQL, and building reusable SQL patterns for analytics teams.

Quick Start

Provide a minimal cross-dialect SQL example and show how to adapt it for PostgreSQL, Snowflake, and BigQuery.

Frequently Asked Questions about sql-queries

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write cross-dialect SQL that works across Snowflake, BigQuery, and PostgreSQL?▼

Migrate SQL queries between warehouse platforms by translating dialect-specific syntax into standardized, portable equivalents. This involves adapting functions and structures like CTEs and window functions to match the target dialect's requirements.

Can I optimize slow SQL query performance using CTEs and window functions?▼

Refactoring slow SQL involves restructuring queries with CTEs, window functions, and optimized aggregations to improve execution speed. This process targets inefficient analytical patterns to accelerate data warehouse performance.

Does this approach support migrating analytical queries to Databricks?▼

Yes, this approach supports migrating analytical queries to Databricks. It provides dialect-aware guidance to translate and adapt common SQL patterns, ensuring correct execution within the Databricks environment.

What is the best way to build reusable SQL patterns for analytics teams?▼

The best way to build reusable SQL patterns is to create dialect-aware templates for common analytical tasks. Standardizing structures like CTEs and window functions allows analytics teams to share and execute queries across different warehouses reliably.

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