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ai-sql skill

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This skill converts natural language into optimized SQL queries across dialects, handling joins and conditions to accelerate data retrieval.

npx playbooks add skill openclaw/skills --skill ai-sql

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SKILL.md

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---
name: sql-gen
description: Generate SQL queries from natural language
---

# SQL Generator

Describe what you want, get the SQL. Works with any database.

## Quick Start

```bash
npx ai-sql "get all users who signed up this month and made a purchase"
```

## What It Does

- Converts English to SQL
- Handles complex JOINs
- Adds proper WHERE clauses
- Optimizes for performance

## Usage Examples

```bash
# Generate query
npx ai-sql "top 10 products by revenue last quarter"

# With schema context
npx ai-sql "users without orders" --schema ./schema.sql

# Specific dialect
npx ai-sql "monthly active users" --dialect postgres
```

## Dialects Supported

- PostgreSQL
- MySQL
- SQLite
- SQL Server
- Oracle

## Output Example

```sql
SELECT u.id, u.email, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY u.id, u.email
HAVING COUNT(o.id) > 0;
```

## Requirements

Node.js 18+. OPENAI_API_KEY required.

## License

MIT. Free forever.

---

**Built by LXGIC Studios**

- GitHub: [github.com/lxgicstudios/ai-sql](https://github.com/lxgicstudios/ai-sql)
- Twitter: [@lxgicstudios](https://x.com/lxgicstudios)

Overview

This skill turns plain English prompts into runnable SQL queries for a wide range of SQL dialects. It helps developers and analysts rapidly prototype queries, handle joins and aggregations, and tailor output to a target database dialect. Use it to save time and reduce manual query errors.

How this skill works

You provide a natural language description of the data you need and optionally supply a schema file or select a dialect. The skill parses intent, maps terms to tables and columns, constructs JOINs, WHERE clauses, GROUP BY and HAVING as needed, and outputs an optimized SQL statement. It supports dialect-specific syntax and can prioritize performance-friendly constructs.

When to use it

Exploratory data analysis when you need quick, accurate SQL without manual drafting
Generating complex JOINs and aggregated queries from a brief description
Translating business questions into queries for different SQL dialects
Prototyping reports or dashboards before integrating into code
Onboarding new team members who are not fluent in SQL syntax

Best practices

Provide schema context (DDL or sample table names) to improve column resolution
Specify the target dialect (Postgres, MySQL, SQLite, SQL Server, Oracle) for correct syntax
Include filtering details like date ranges, status values, or sampling rules to narrow results
Validate generated queries in a safe dev environment before running on production
Refine prompts iteratively—start broad, then add constraints or indexes to optimize

Example use cases

Produce a query for top 10 products by revenue last quarter, with GROUP BY and ORDER BY
Find users who signed up this month and made at least one purchase using LEFT JOINs
List users without orders by comparing users and orders tables with an anti-join
Create a monthly active users query optimized for a specific SQL dialect
Convert analytics questions into SQL to embed in BI dashboards or scripts

FAQ

Which SQL dialects are supported?

Common dialects are supported including PostgreSQL, MySQL, SQLite, SQL Server, and Oracle.

Can I provide a schema so the skill uses real column names?

Yes. Supplying a schema file or DDL improves accuracy by allowing the generator to map natural language to actual tables and columns.

Is the generated SQL production-ready?

Generated SQL is intended to be syntactically correct and performance-conscious, but you should validate and test queries in your environment before production use.