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SQL Queries

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Tool integration
  • Data and AI
  • MIT
  • No issues found
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From the author

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.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The SQL Query Generator transforms natural language business requirements into production-ready SQL code. By analyzing provided database schemas, documentation, or diagram descriptions, the skill constructs efficient queries tailored to specific dialects like BigQuery, PostgreSQL, MySQL, and Snowflake. It assists users in bridging the gap between business questions and technical data retrieval, providing not only the code but also explanations, performance optimization tips, and validation scripts.

What it does

  • Translates natural language requests into syntactically correct SQL queries
  • Parses database schemas from SQL files, documentation, or text-based descriptions
  • Supports multiple SQL dialects including BigQuery, PostgreSQL, MySQL, and Snowflake
  • Provides performance optimization advice such as indexing and partitioning strategies
  • Generates plain-English explanations for complex query logic
  • Creates test scripts and sample data for query validation

When to use it

  • When you need to translate complex business questions into database queries
  • When working with unfamiliar database schemas and requiring structural analysis
  • When you need to generate optimized SQL for specific platforms like BigQuery or PostgreSQL
  • When preparing data reports and requiring efficient aggregation or filtering logic
  • When you need to document or explain existing SQL query logic to team members

Advantages

  • Reduces manual syntax work and accelerates query development
  • Ensures queries are optimized for the specific target database dialect
  • Improves data literacy by providing clear explanations of query logic
  • Facilitates better performance through built-in optimization suggestions
  • Supports diverse input formats including schema files and diagram descriptions

Usage examples

“Generate a query to find users who signed up in the last 30 days and had at least 5 active sessions based on my uploaded schema.”
The agent analyzes the schema file to identify the correct table relationships and generates a SQL query with the appropriate filters and count aggregations.
“Create a BigQuery query to analyze our revenue by region and customer tier, including year-over-year growth rates.”
The agent produces a BigQuery-compatible SQL script using window functions or CTEs to calculate growth rates and provides performance notes for large datasets.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/phuryn/sql-queries/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip sql-queries.zip -d .agents/skills

Documentation by Pawel Huryn

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

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-data-analytics/skills/sql-queries · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download sql-queries.zip