sql-queries

phuryn/pm-skills

Generate SQL queries from natural language across BigQuery, PostgreSQL, MySQL, and more.

What is sql-queries?

This skill transforms natural language requirements into optimized SQL queries for multiple database platforms. Use it when you need to write queries, build data reports, explore databases, or translate business questions into SQL without manual syntax work.

Converts natural language descriptions into production-ready SQL queries
Reads and analyzes database schemas from files, documentation, or diagram descriptions
Supports multiple SQL dialects including BigQuery, PostgreSQL, MySQL, Snowflake, and SQL Server
Generates optimized queries with performance considerations and indexing suggestions
Provides plain-English explanations of query logic and test validation approaches
Creates executable SQL scripts and sample data for testing

How to install sql-queries

npx skills add https://github.com/phuryn/pm-skills --skill sql-queries

Claude Code

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How to use sql-queries

1.Upload your database schema file (SQL, documentation, or diagram description) or describe your database structure
2.Specify which SQL dialect you're using (BigQuery, PostgreSQL, MySQL, etc.)
3.Describe the data you need to retrieve, including any filters, aggregations, or sorting requirements
4.Receive the optimized query with explanations and performance notes
5.Test the query using provided test scripts or sample data if requested

Use cases

Good for

Find users who signed up in the last 30 days with at least 5 active sessions
Calculate average session duration per user by month across a Sessions table
Analyze revenue by region and customer tier with year-over-year growth rates
Generate cohort analysis and funnel queries for product analytics
Create data validation and testing scripts for database exploration

Who it's for

Product managers building data reports
Data analysts exploring databases
Engineers translating business requirements into queries
Anyone needing SQL without deep syntax knowledge

sql-queries FAQ

What database formats can you read?

The skill reads SQL schema files, database documentation, and descriptions of database diagrams. It extracts table names, columns, data types, relationships, and key constraints.

Which SQL dialects are supported?

BigQuery, PostgreSQL, MySQL, Snowflake, and SQL Server are all supported. Specify your dialect when requesting a query.

Can you optimize existing queries?

Yes. You can provide an existing query and ask for optimization suggestions, including indexing strategies and performance improvements for large datasets.

Do you generate test data?

Yes. The skill can generate test scripts and sample data to help you validate query results before running them on production databases.

What if my database schema is complex?

Share as much detail as possible—table relationships, primary/foreign keys, data types, and any constraints. The more context you provide, the more accurate the generated query will be.

Full instructions (SKILL.md)

Source of truth, from phuryn/pm-skills.

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