name: sql-queries slug: sql-queries version: 1.0.1 displayName: "自然语言需求转SQL查询｜简诗 AI" summary: "将自然语言需求转化为跨多数据库平台的优化 SQL 查询，帮助产品经理、分析师和工程师无需手写语法即可获得准确查询。" description: "将自然语言需求转化为跨多数据库平台的优化 SQL 查询，帮助产品经理、分析师和工程师无需手写语法即可获得准确查询。" tags: ["data-automation", "jianshi-ai"]

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

简诗 AI 安全边界

• 不自动安装依赖、修改系统权限、创建持久化任务或执行下载内容。
• 涉及发送、发布、删除、付款、部署或其他外部写入时，先展示目标与影响并取得用户明确确认。
• 凭据只用于用户指定的对应官方服务，不回显、不记录，也不转发到无关地址。
• 命令和代码默认作为参考；只有用户明确要求执行且目标范围清楚时才可运行。

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这个 Skill 质量中等偏上，文档内容完整、示例丰富且安全提示到位，但本质上只是一份使用指南，缺少真正的代码实现和模板支持，实际使用时可能无法直接生成高质量 SQL，建议配合具体数据库的查询规范文档使用效果更佳。