sql-query-helper

name	sql-query-helper
description	Generates, explains, optimizes, and migrates SQL queries across multiple database dialects
version	1.0.0
author	go-on-team
tags	["sql","database","query","postgres","mysql","sqlite","migration"]
min_go_on_version	1.0.0

SQL Query Helper Skill

Assists with all SQL-related tasks: writing queries from natural language, explaining what existing queries do, optimizing slow queries, generating schema migrations, and translating between database dialects (PostgreSQL, MySQL, SQLite, BigQuery, Snowflake).

How It Works

1. Understand — Accepts a natural-language description or an existing SQL query
2. Generate — Produces correct, idiomatic SQL for the target dialect with appropriate indexing hints and join strategies
3. Explain — Annotates queries with logical operator breakdown, estimated complexity (sequential scan vs index scan), and data flow
4. Optimize — Identifies anti-patterns (SELECT *, N+1 queries, missing JOIN conditions, implicit type coercions) and suggests rewrites
5. Translate — Converts between dialects, handling syntax differences (LIMIT vs TOP, ILIKE vs LOWER, JSON operators)

Input Schema

Parameter	Type	Description
description	string	Natural-language description of what the query should do
sql	string	Optional: existing SQL to explain/optimize instead of generating new
dialect	string	Target SQL dialect: postgres, mysql, sqlite, bigquery, snowflake (default: postgres)
schema	string	Optional: table schema context for accurate generation
action	string	Action: generate, explain, optimize, translate (default: generate)
target_dialect	string	Required when action=translate: destination dialect

Example

{
  "description": "Find the top 10 customers by total order value in the last 30 days, including their email and order count",
  "dialect": "postgres",
  "action": "generate",
  "schema": "customers(id, email, name, created_at)\norders(id, customer_id, total, ordered_at)"
}

Example output:

-- Generated for PostgreSQL
-- Estimated complexity: Index Scan (efficient with proper indexes)
SELECT
    c.email,
    c.name,
    COUNT(o.id) AS order_count,
    SUM(o.total) AS total_spent
FROM customers c
JOIN orders o ON o.customer_id = c.id
WHERE o.ordered_at >= NOW() - INTERVAL '30 days'
GROUP BY c.id, c.email, c.name
ORDER BY total_spent DESC
LIMIT 10;

-- Recommended indexes:
-- CREATE INDEX idx_orders_customer_date ON orders(customer_id, ordered_at);