Mmcp.market

sql-queries skill

by phuryn·phuryn/pm-skills·27k stars·MIT

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.

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Install the sql-queries skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p ~/.claude/skills
cp -r /tmp/pm-skills/pm-data-analytics/skills/sql-queries ~/.claude/skills/sql-queries
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

  1. Provide context: Share your database schema or structure
  2. Be specific: Clearly describe what data you need and any filters
  3. Mention database: Specify which SQL dialect you're using
  4. Include constraints: Mention data volume, time ranges, and performance needs
  5. 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

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