sql-queries

name sql-queries description 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 Further Reading The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs How to Become a Technology-Literate PM