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sql-queries

Write cross-dialect SQL for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks.

345|12|Updated Apr 14, 2026
Search Tags:#performance#analytics#queries#sql#dialects#data-warehouses
One-click install
npx skills add https://github.com/minicoohei/ai-agent-camp --skill sql-queries-minicoohei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/sql-queries
Command: npx skills add https://github.com/minicoohei/ai-agent-camp --skill sql-queries-minicoohei

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

SQL across data warehouses is fragmented by dialects, making queries error-prone and hard to maintain.

Core Features & Use Cases

  • Dialect-aware SQL references for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks
  • Practical patterns: CTEs, window functions, and common pitfalls
  • Quick-start guidance and ready-to-run examples for real-world analytics tasks

Quick Start

Ask it to generate a dialect-ready SQL query for your dataset.

Frequently Asked Questions about sql-queries

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

FAQPage Schema
How do I write a SQL query that works correctly across PostgreSQL, Snowflake, and BigQuery?▼

Cross-dialect SQL requires dialect-specific syntax references for date, time, string, and array functions. Applying correct syntax patterns ensures your SQL queries execute properly across PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks engines.

What is the best way to convert date and time functions between different SQL data warehouse dialects?▼

The best way to convert date and time functions between SQL dialects is to reference dialect-aware mappings. This ensures accurate date/time manipulation across PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks without syntax errors.

Does this SQL query generator support window functions and CTEs for Redshift and Databricks?▼

Yes, it supports window functions and CTEs for Redshift and Databricks. It provides practical patterns and ready-to-run examples for common analytics tasks, ensuring correct syntax and robust performance across these data warehouse engines.

Why does my SQL query fail when moving from standard SQL to a specific data warehouse dialect?▼

Your SQL query fails because data warehouses fragment standard SQL with dialect-specific syntax. Using dialect-aware references helps you avoid common pitfalls and provides error handling guidance for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks.

Can I get performance tips and common SQL patterns for analytics queries?▼

Yes, you can get performance tips and common SQL patterns for analytics queries. It enforces dialect-specific performance guidance and provides practical examples for CTEs and window functions to optimize your data warehouse operations.

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