SQL查询生成器：自然语言转优化SQL，支持多数据库平台

sql-queries by phuryn/pm-skills

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安装命令

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

自动化数据分析数据库

🇨🇳中文介绍

SQL 查询生成器

目的

将自然语言需求转换为跨多个数据库平台的优化 SQL 查询。此技能帮助产品经理、分析师和工程师无需手动处理语法即可生成准确的查询。

工作原理

步骤 1：理解您的数据库架构

如果您提供架构文件（SQL、文档或图表描述），我将读取并分析它
提取表名、列定义、数据类型和关系
识别主键、外键和索引策略

步骤 2：处理您的请求

澄清您需要检索或分析的确切数据
确认 SQL 方言（BigQuery、PostgreSQL、MySQL、Snowflake 等）
询问任何其他要求（过滤器、聚合、排序）

步骤 3：生成优化查询

编写利用您数据库结构的高效 SQL
包含解释复杂逻辑的注释
为大型数据集添加性能注意事项
如果适用，提供替代方法

步骤 4：解释和测试

用通俗易懂的英语解释查询逻辑
建议如何测试或验证结果
提供性能优化技巧
如果您需要，可以生成测试脚本或样本数据

使用示例

示例 1：根据架构文件生成查询

上传您的 database_schema.sql 文件并说：
"生成一个查询，查找过去 30 天内注册且至少有 5 个活跃会话的用户"

示例 2：根据图表描述生成查询

"这是我的数据库：用户表（id, email, created_at），会话表
（id, user_id, timestamp, duration）。生成一个查询，用于统计 2026 年 1 月
每个用户的平均会话时长。"

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🇺🇸English

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

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