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sql-queries

Generate dialect-specific SQL for major warehouse platforms with built-in patterns and error handling guidance.

Updated Jun 19, 2026
Search Tags:#sql#query-optimization#data-engineering#data-warehouse#cohort-analysis#funnel-analysis#dialect-compatibility
One-click install
npx skills add https://github.com/MuhammadUA/Axe --skill sql-queries-muhammadua
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sql-queries
Source: https://github.com/MuhammadUA/Axe/tree/main/.kortix/opencode/skills/GENERAL-KNOWLEDGE-WORKER/sql-queries
Command: npx skills add https://github.com/MuhammadUA/Axe --skill sql-queries-muhammadua

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the common pain points of writing SQL that fails across different data warehouse platforms, avoids dialect-specific syntax errors, and ensures queries are both performant and maintainable for analytics and data engineering teams.

Core Features & Use Cases

  • Multi-Dialect Reference: Includes syntax and best practices for PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks SQL covering date/time operations, string functions, JSON/array handling, and platform-specific performance optimizations.
  • Pre-Built Common Patterns: Provides ready-to-use templates for window functions, CTEs, cohort retention analysis, funnel analysis, and record deduplication that can be adapted to any supported dialect.
  • Error Handling Guidance: Offers troubleshooting steps for common SQL failures including syntax errors, type mismatches, division by zero, and ambiguous column issues.
  • Use Case Example: An analytics engineer building a monthly active user report can use the pre-built cohort pattern and dialect-specific date functions to write a single query that works on both Snowflake and BigQuery without rewriting core logic.

Quick Start

Use the sql-queries skill to write a performant funnel analysis query for your e-commerce events table that calculates conversion rates between page view, signup start, signup complete, and first purchase steps, compatible with Snowflake syntax.

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 PostgreSQL, Snowflake, and BigQuery without syntax errors?▼

To write cross-dialect SQL queries without syntax errors, use dialect-specific reference patterns for date/time operations, string manipulation, and JSON/array handling tailored to PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks to ensure compatibility and performance.

What's the best way to write a cohort retention analysis query for a data warehouse?▼

The best way to write a cohort retention analysis query is to use pre-built SQL templates that leverage window functions and dialect-specific date functions, allowing you to adapt the core logic for platforms like Snowflake or BigQuery without rewriting the entire query.

How do I optimize funnel analysis queries for conversion rates in BigQuery?▼

To optimize funnel analysis queries for conversion rates in BigQuery, apply pre-built SQL patterns that calculate step-by-step conversion using platform-specific performance optimizations, ensuring efficient processing of e-commerce events tables and accurate tracking of user progression.

Why does my SQL query fail with ambiguous column issues and type mismatches across different data warehouses?▼

SQL queries fail with ambiguous column issues and type mismatches due to dialect-specific differences in how data warehouses handle types and column references. Use error handling guidance and troubleshooting steps for common failures to resolve these syntax and type conflicts.

Can I use window functions and CTEs for record deduplication in Redshift and Databricks?▼

Yes, you can use window functions and CTEs for record deduplication in Redshift and Databricks. Pre-built common patterns provide ready-to-use templates for deduplication that are adapted to supported dialects with specific performance optimizations.

How do I handle division by zero and JSON array data in Snowflake SQL?▼

To handle division by zero and JSON array data in Snowflake SQL, reference dialect-specific syntax for JSON/array handling and apply error handling guidance for common SQL failures like division by zero, ensuring robust and performant query execution.

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