sql-queries

Name: sql-queries
Availability: InStock
Author: jaimedhenriques

Write performant SQL queries across Snowflake, BigQuery, Databricks, PostgreSQL, and Redshift.

Updated Apr 1, 2026

Search Tags:#analytics #queries #sql #bigquery #snowflake #window-functions

Ready to run

npx skills add https://github.com/jaimedhenriques/finsyt --skill sql-queries-jaimedhenriques

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/jaimedhenriques/finsyt/tree/main/artifacts/platform/.agents/skills/sql-queries
Command: npx skills add https://github.com/jaimedhenriques/finsyt --skill sql-queries-jaimedhenriques

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you write correct, performant SQL without constantly managing dialect differences or common query-performance mistakes.

Core Features & Use Cases

Dialect Coverage: Handles PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks SQL syntax and function differences.
Analytical Query Patterns: Supports CTEs, window functions, cohort retention, funnels, deduplication, and complex aggregations.
Performance Guidance: Recommends practical tuning strategies such as pruning, clustering, indexing, distribution keys, and query plan review.
Use Case: Turn a business question into a production-ready SQL query that is readable, efficient, and portable across data warehouses.

Quick Start

Ask the skill to write or optimize a SQL query for your warehouse and include the dialect, schema details, and desired output.

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 performant SQL query that works across Snowflake and BigQuery?▼

To write performant SQL across Snowflake and BigQuery, you need dialect-specific syntax guidance for window functions, CTE patterns, and analytical transformations alongside performance tuning practices like pruning and clustering.

What is the best way to translate PostgreSQL SQL syntax to Redshift?▼

Translating PostgreSQL SQL syntax to Redshift requires handling dialect differences in functions and query structures, utilizing specific patterns for semi-structured data handling and complex aggregations to ensure query portability.

How do I optimize slow window functions in Databricks SQL?▼

Optimizing slow window functions in Databricks SQL involves applying performance tuning strategies such as query plan review, clustering, and distribution keys to improve analytical transformation efficiency.

Can I use CTE patterns for cohort retention analysis in major data warehouses?▼

Yes, you can use CTE patterns for cohort retention analysis in major data warehouses like PostgreSQL and Snowflake by structuring complex aggregations and analytical query patterns to track user behavior over time.

When do I need clustering or distribution keys for SQL query optimization?▼

You need clustering or distribution keys for SQL query optimization when handling large datasets in warehouses like Redshift or Snowflake, ensuring practical tuning strategies prune data effectively during complex aggregations.