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sql-queries

Generate dialect-specific SQL queries for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks.

Updated Apr 8, 2026
Search Tags:#analytics#postgresql#sql#query-optimization#bigquery#snowflake#data-warehouse
One-click install
npx skills add https://github.com/aimentor606/aether --skill sql-queries-aimentor606
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/aimentor606/aether/tree/main/core/kortix-master/opencode/skills/GENERAL-KNOWLEDGE-WORKER/sql-queries
Command: npx skills add https://github.com/aimentor606/aether --skill sql-queries-aimentor606

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides clear, dialect-aware guidance so analysts and engineers can write correct, readable, and high-performance SQL without repeatedly looking up syntax differences or performance patterns across warehouses.

Core Features & Use Cases

  • Dialect mappings: Practical examples for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks covering date/time, string, JSON/array, and semi-structured data access.
  • Common patterns: Window functions, CTE-driven workflows, cohort and funnel analyses, deduplication, and merge/upsert examples.
  • Performance and debugging: Actionable tips for profiling queries, partitioning/clustering, indexing strategies, and common error resolutions.
  • Use Case: Convert a business metric definition (e.g., monthly active users by cohort) into an optimized, dialect-specific query suitable for your warehouse.

Quick Start

Translate the reporting requirement "monthly active users by plan for the last 12 months" into a single optimized SQL query for the target warehouse.

Frequently Asked Questions about sql-queries

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

FAQPage Schema
How do I write optimized SQL queries for BigQuery or Snowflake?▼

Optimized SQL queries for BigQuery or Snowflake require dialect-specific syntax handling, window functions, and performance patterns like partitioning, clustering, and index-aware recommendations to ensure high performance.

What is the best way to convert business metrics into data warehouse SQL?▼

Converting business metrics into data warehouse SQL involves translating reporting requirements into CTE-driven workflows, cohort and funnel analyses, and deduplication patterns tailored to your specific warehouse dialect.

Does this approach support PostgreSQL and Redshift for ETL transformations?▼

Yes, this approach supports PostgreSQL and Redshift for ETL transformations, providing practical dialect mappings for date/time, string, JSON/array, and semi-structured data access across major warehouses.

How do window functions and CTE patterns differ across PostgreSQL, Databricks, and Redshift?▼

Window functions and CTE patterns differ across PostgreSQL, Databricks, and Redshift in syntax and execution, requiring dialect-aware guidance to ensure correct and readable SQL for analytics and reporting tasks.

Why does my SQL query performance drop when handling JSON or array data in Snowflake?▼

SQL query performance drops when handling JSON or array data in Snowflake due to inefficient semi-structured data access, requiring actionable profiling tips and dialect-specific optimizations to resolve.

Can I use these SQL patterns for ad-hoc analysis across multiple data warehouse platforms?▼

Yes, you can use these SQL patterns for ad-hoc analysis across multiple data warehouse platforms, as they provide clear, dialect-aware mappings to ensure correct and performant queries without repeated syntax lookups.

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