sql-queries

Convert SQL queries across warehouse dialects while preserving semantics.

1|Updated Mar 19, 2026

Search Tags:#performance #debugging #sql #query-writing #dialects #data-warehouses

Ready to run

npx skills add https://github.com/cy-wali/knowledge --skill sql-queries-cy-wali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/cy-wali/knowledge/tree/main/data/skills/sql-queries
Command: npx skills add https://github.com/cy-wali/knowledge --skill sql-queries-cy-wali

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Write correct, performant SQL across major data warehouse dialects.

Core Features & Use Cases

Dialect-aware references for Snowflake, BigQuery, Redshift, PostgreSQL, and more.
Support for advanced SQL patterns: CTEs, window functions, arrays/JSON, and nested data.
Guidance on performance optimization, safe casting, and error handling to improve reliability.
Use Case: Refactor cross-dialect queries to preserve semantics and results.

Quick Start

Convert a given SQL query to the target dialect while preserving semantics.

Frequently Asked Questions about sql-queries

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

FAQPage Schema

How do I write SQL queries that work across different data warehouses?▼

To write portable SQL across data warehouses, use dialect-aware functions and safe casting to ensure semantics are preserved. This approach handles dialect-specific syntax differences across platforms like Snowflake, BigQuery, Redshift, and PostgreSQL.

What is the best way to refactor a PostgreSQL query for BigQuery?▼

The best way to refactor SQL for BigQuery is to convert dialect-specific syntax while preserving semantics and results. This involves mapping functions correctly, applying safe casting, and ensuring advanced patterns like CTEs and window functions translate properly.

How do I optimize SQL performance for analytics workflows?▼

Optimize SQL performance for analytics by applying best-practice query optimization techniques and utilizing advanced patterns like CTEs and window functions. Proper dialect-aware function usage and safe error handling also improve reliability and execution speed.

Does this approach support advanced SQL patterns like window functions and JSON?▼

Yes, this approach supports advanced SQL patterns including CTEs, window functions, arrays, and nested JSON data. It provides dialect-aware references to ensure these complex structures execute correctly across major warehouse platforms.

Why does my cross-dialect SQL query return inconsistent results?▼

Cross-dialect SQL queries return inconsistent results due to differences in dialect-specific syntax and implicit type casting. Resolving these inconsistencies requires using dialect-aware functions and explicit safe casting to preserve query semantics.

Can I use safe casting and error handling to improve SQL query reliability?▼

Yes, you can use safe casting and error handling to significantly improve SQL query reliability. Applying these practices alongside dialect-aware functions ensures queries execute consistently without failing across different warehouse environments.