Agent Skills
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sql-queries
GitHub将自然语言需求转换为优化的SQL查询，支持多种方言。读取数据库Schema后生成高效代码、性能建议及解释，辅助数据报告编写和数据库探索。
Trigger Scenarios
需要生成SQL查询
将业务问题转化为数据库查询
编写数据报表
探索数据库结构
Install
npx skills add phuryn/pm-skills --skill sql-queries -g -y
SKILL.md
Frontmatter
{
"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
- 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
Further Reading
Version History
- 18468a9 Current 2026-07-25 10:30
