sql-queries

Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.

SQL Query Generator

Purpose

Transform natural language requirements into optimized SQL queries across multiple database platforms. This skill helps product managers, analysts, and engineers generate accurate queries without manual syntax work.

How It Works

Step 1: Understand Your Database Schema

If you provide a schema file (SQL, documentation, or diagram description), I will read and analyze it
Extract table names, column definitions, data types, and relationships
Identify primary keys, foreign keys, and indexing strategies

Step 2: Process Your Request

Clarify the exact data you need to retrieve or analyze
Confirm the SQL dialect (BigQuery, PostgreSQL, MySQL, Snowflake, etc.)
Ask for any additional requirements (filters, aggregations, sorting)

Step 3: Generate Optimized Query

Write efficient SQL that leverages your database structure
Include comments explaining complex logic
Add performance considerations for large datasets
Provide alternative approaches if applicable

Step 4: Explain and Test

Explain the query logic in plain English
Suggest how to test or validate results
Offer tips for performance optimization
If you want, generate a test script or sample data

Usage Examples

Example 1: Query from Schema File

Upload your database_schema.sql file and say:
"Generate a query to find users who signed up in the last 30 days
and had at least 5 active sessions"

Example 2: Query from Diagram Description

"Here's my database: Users table (id, email, created_at), Sessions table
(id, user_id, timestamp, duration). Generate a query for average session
duration per user in January 2026."

Example 3: Complex Analysis Query

"Create a BigQuery query to analyze our revenue by region and customer tier,
including year-over-year growth rates."

Key Capabilities

Multi-Dialect Support: Works with BigQuery, PostgreSQL, MySQL, Snowflake, SQL Server
File Reading: Reads schema files, SQL dumps, and data documentation
Query Optimization: Suggests indexes, partitioning, and performance improvements
Explanation: Breaks down queries for learning and documentation
Testing: Can generate test queries and sample data scripts
Script Execution: Create executable SQL scripts for your database

Tips for Best Results

Provide context: Share your database schema or structure
Be specific: Clearly describe what data you need and any filters
Mention database: Specify which SQL dialect you're using
Include constraints: Mention data volume, time ranges, and performance needs
Request format: Ask for the query result format if you need specific output

Output Format

You'll receive:

SQL Query: Production-ready SQL code with comments
Explanation: What the query does and how it works
Performance Notes: Optimization tips and considerations
Test Script (if requested): Sample data and validation queries