sql-queries

name	sql-queries
description	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.

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

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Further Reading

• The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
• How to Become a Technology-Literate PM

是什么

SQL Query Generator 用来把 数据分析师 场景里的任务输入转成可执行的流程、检查清单和交付物。

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 dat...

它的价值在于让 数据AI职能线 在 Claude Code、Codex、Gemini、Hermes 或 OpenClaw 中复用同一套岗位能力，而不是依赖一次性的聊天提示词。

怎么用

1. 明确当前任务目标、输入材料、约束和期望交付物，再加载 sql-queries。
2. 按 skill 文档中的流程、检查清单或工具建议执行，优先复用仓库已有规范与真实命令。
3. 把关键判断、风险、验证命令和产出路径记录到当前任务文档或交付说明中。
4. 用最小可证明的检查确认结果有效；发现缺口时回到 skill 清单补齐。

架构图

flowchart LR
  A[任务输入] --> B[加载 SQL Query Generator]
  B --> C[执行流程与检查清单]
  C --> D[生成交付物与风险记录]
  D --> E[验证结果并沉淀复盘]