SQL Pro：跨平台数据库专家 | 复杂查询、性能优化与架构设计

sql-pro by 404kidwiz/claude-supercode-skills

76 周安装量

63 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill sql-pro

数据库性能优化数据处理

🇨🇳中文介绍

SQL Pro

目的

提供跨主流数据库平台（PostgreSQL、MySQL、SQL Server、Oracle）的专业 SQL 开发能力，专注于复杂查询设计、性能优化和数据库架构。精通 ANSI SQL 标准、平台特定的优化以及现代数据模式，注重效率和可扩展性。

使用时机

编写包含连接、CTE、窗口函数或递归查询的复杂 SQL 查询
为新应用程序设计数据库模式或重构现有模式
通过执行计划分析优化缓慢的 SQL 查询
在不同数据库平台之间迁移数据（MySQL → PostgreSQL）
实现存储过程、函数或触发器
使用高级聚合和窗口函数构建分析报告
将业务需求转化为 SQL 查询逻辑
跨平台 SQL 兼容性问题（不同方言）

快速开始

在以下情况调用此技能：

编写包含 CTE、窗口函数或递归模式的复杂查询
设计或重构数据库模式
通过执行计划分析优化缓慢查询
在不同数据库平台之间迁移数据
实现存储过程、函数或触发器
使用高级聚合构建分析报告

在以下情况不要调用：

需要 PostgreSQL 特定功能 → 使用 postgres-pro
MySQL 特定管理 → 使用 database-administrator
简单的 CRUD 操作 → 使用 backend-developer
ORM 查询模式 → 使用相应的语言技能

决策框架

CTE 对比子查询对比 JOIN 决策树

Query Requirement Analysis
│
├─ Need to reference result multiple times?
│  └─ YES → Use CTE (avoids duplicate subquery evaluation)
│     WITH user_totals AS (SELECT ...)
│     SELECT * FROM user_totals WHERE ...
│     UNION ALL
│     SELECT * FROM user_totals WHERE ...
│
├─ Recursive data traversal (hierarchy, graph)?
│  └─ YES → Use Recursive CTE (ONLY option for recursion)
│     WITH RECURSIVE tree AS (
│       SELECT ... -- anchor
│       UNION ALL
│       SELECT ... FROM tree ... -- recursive
│     )
│
├─ Simple lookup or filter?
│  └─ Use JOIN (most optimizable by query planner)
│     SELECT u.*, o.total
│     FROM users u
│     JOIN orders o ON u.id = o.user_id
│
├─ Correlated subquery in WHERE clause?
│  ├─ Checking existence → Use EXISTS (stops at first match)
│  │  WHERE EXISTS (SELECT 1 FROM orders WHERE user_id = u.id)
│  │
│  └─ Value comparison → Use JOIN instead
│     -- BAD: WHERE (SELECT COUNT(*) FROM orders WHERE user_id = users.id) > 5
│     -- GOOD: JOIN (SELECT user_id, COUNT(*) as cnt FROM orders GROUP BY user_id)
│
└─ Readability vs Performance trade-off?
   ├─ Complex logic, readability critical → CTE
   │  (Easier to understand, debug, maintain)
   │
   └─ Performance critical, simple logic → Subquery or JOIN
      (Query planner can inline and optimize)

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需求	解决方案	示例
需要聚合 + 行级详细信息	窗口函数	`SELECT name, salary, AVG(salary) OVER () as avg_salary FROM employees`
仅需要聚合结果	GROUP BY	`SELECT dept, AVG(salary) FROM employees GROUP BY dept`
排名/行号	窗口函数 (ROW_NUMBER, RANK, DENSE_RANK)	`ROW_NUMBER() OVER (ORDER BY sales DESC)`
累计总计 / 移动平均	带框架的窗口函数	`SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)`
LAG/LEAD（访问前/后行）	窗口函数	`LAG(price, 1) OVER (ORDER BY date) as prev_price`
百分位数 / NTILE	窗口函数	`NTILE(4) OVER (ORDER BY score) as quartile`
按组进行简单计数/求和/平均	GROUP BY（更高效）	`SELECT category, COUNT(*) FROM products GROUP BY category`

观察现象	升级原因	示例
执行计划中出现笛卡尔积	意外的交叉连接导致行数指数级增长	"查询返回数百万行"
复杂的多级递归 CTE 性能问题	需要高级优化	"递归 CTE 遍历 10+ 层，包含 100K 个节点"
具有不兼容功能的跨平台迁移	平台特定功能映射	"将 Oracle CONNECT BY 迁移到 PostgreSQL 递归 CTE"
包含 10+ 个连接和复杂逻辑的查询	架构异味，可能需要重新设计	"连接 15 个表的单个查询"
具有复杂时间序列逻辑的时间查询	高级分析模式	"带有历史快照的 SCD 类型 2"

🇺🇸English

SQL Pro

Purpose

Provides expert SQL development capabilities across major database platforms (PostgreSQL, MySQL, SQL Server, Oracle), specializing in complex query design, performance optimization, and database architecture. Masters ANSI SQL standards, platform-specific optimizations, and modern data patterns with focus on efficiency and scalability.

When to Use

Writing complex SQL queries with joins, CTEs, window functions, or recursive queries
Designing database schema for new application or refactoring existing schema
Optimizing slow SQL queries with execution plan analysis
Data migration between different database platforms (MySQL → PostgreSQL)
Implementing stored procedures, functions, or triggers
Building analytical reports with advanced aggregations and window functions
Translating business requirements into SQL query logic
Cross-platform SQL compatibility issues (different dialects)

Quick Start

Invoke this skill when:

Writing complex queries with CTEs, window functions, or recursive patterns
Designing or refactoring database schemas
Optimizing slow queries with execution plan analysis
Migrating data between different database platforms
Implementing stored procedures, functions, or triggers
Building analytical reports with advanced aggregations

Do NOT invoke when:

PostgreSQL-specific features needed → Use postgres-pro
MySQL-specific administration → Use database-administrator
Simple CRUD operations → Use backend-developer
ORM query patterns → Use appropriate language skill

Decision Framework

CTE vs Subquery vs JOIN Decision Tree

Query Requirement Analysis
│
├─ Need to reference result multiple times?
│  └─ YES → Use CTE (avoids duplicate subquery evaluation)
│     WITH user_totals AS (SELECT ...)
│     SELECT * FROM user_totals WHERE ...
│     UNION ALL
│     SELECT * FROM user_totals WHERE ...
│
├─ Recursive data traversal (hierarchy, graph)?
│  └─ YES → Use Recursive CTE (ONLY option for recursion)
│     WITH RECURSIVE tree AS (
│       SELECT ... -- anchor
│       UNION ALL
│       SELECT ... FROM tree ... -- recursive
│     )
│
├─ Simple lookup or filter?
│  └─ Use JOIN (most optimizable by query planner)
│     SELECT u.*, o.total
│     FROM users u
│     JOIN orders o ON u.id = o.user_id
│
├─ Correlated subquery in WHERE clause?
│  ├─ Checking existence → Use EXISTS (stops at first match)
│  │  WHERE EXISTS (SELECT 1 FROM orders WHERE user_id = u.id)
│  │
│  └─ Value comparison → Use JOIN instead
│     -- BAD: WHERE (SELECT COUNT(*) FROM orders WHERE user_id = users.id) > 5
│     -- GOOD: JOIN (SELECT user_id, COUNT(*) as cnt FROM orders GROUP BY user_id)
│
└─ Readability vs Performance trade-off?
   ├─ Complex logic, readability critical → CTE
   │  (Easier to understand, debug, maintain)
   │
   └─ Performance critical, simple logic → Subquery or JOIN
      (Query planner can inline and optimize)

Window Function vs GROUP BY Decision Matrix

Requirement	Solution	Example
Need aggregation + row-level detail	Window function	`SELECT name, salary, AVG(salary) OVER () as avg_salary FROM employees`
Only aggregated results needed	GROUP BY	`SELECT dept, AVG(salary) FROM employees GROUP BY dept`
Ranking/row numbering	Window function (ROW_NUMBER, RANK, DENSE_RANK)	`ROW_NUMBER() OVER (ORDER BY sales DESC)`
Running totals / moving averages	Window function with frame	`SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)`

Red Flags → Escalate to Oracle

Observation	Why Escalate	Example
Cartesian product in execution plan	Unintended cross join causing exponential rows	"Query returning millions of rows"
Complex multi-level recursive CTE performance	Advanced optimization needed	"Recursive CTE traversing 10+ levels with 100K nodes"
Cross-platform migration with incompatible features	Platform-specific feature mapping	"Migrating Oracle CONNECT BY to PostgreSQL recursive CTE"
Query with 10+ joins and complex logic	Architecture smell, potential redesign	"Single query joining 15 tables"
Temporal query with complex time-series logic	Advanced analytical pattern	"SCD Type 2 with historical snapshots"

Core Capabilities

Advanced Query Patterns

Common Table Expressions (CTEs) and recursive queries
Window functions: ROW_NUMBER, RANK, LEAD, LAG, aggregate windows
PIVOT/UNPIVOT operations for data transformation
Hierarchical queries for tree/graph structures
Temporal queries for time-based analysis

Query Optimization

Execution plan analysis and interpretation
Index selection strategies and covering indexes
Statistics management and maintenance
Query hints and plan guides (when necessary)
Parallel query execution tuning

Index Design Patterns

Clustered vs. non-clustered indexes
Covering indexes for query optimization
Filtered/partial indexes for selective queries
Function-based/indexes on expressions
Composite index column ordering

Quality Checklist

Query Performance:

Execution time meets requirements (OLTP: <100ms, Analytics: <5s)
EXPLAIN ANALYZE reviewed for all complex queries
No sequential scans on large tables (unless intended)
Indexes utilized effectively (check execution plan)
No N+1 query patterns (correlated subqueries eliminated)

SQL Quality:

Only necessary columns in SELECT (no SELECT *)
Explicit table aliases used in multi-table queries
Proper NULL handling (COALESCE, IS NULL vs = NULL)
Data types match in comparisons (no implicit conversions)
Parameterized queries used (SQL injection prevention)

Optimization:

Window functions used instead of self-joins where applicable
EXISTS used instead of NOT IN for better NULL handling
Covering indexes suggested for frequent queries
Query rewritten to eliminate correlated subqueries

Documentation:

Complex query logic explained in comments
CTE names descriptive and self-documenting
Expected output format documented
Performance characteristics documented

Additional Resources

Detailed Technical Reference : See REFERENCE.md
Code Examples & Patterns: See EXAMPLES.md

Weekly Installs

Repository

404kidwiz/claud…e-skills

GitHub Stars

First Seen

Jan 24, 2026

Security Audits

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Installed on

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SQL Pro：跨平台数据库专家 | 复杂查询、性能优化与架构设计

🇨🇳中文介绍

SQL Pro

目的

使用时机

快速开始

决策框架

CTE 对比子查询对比 JOIN 决策树

相关 Skills

窗口函数对比 GROUP BY 决策矩阵

危险信号 → 升级到 Oracle

核心能力

高级查询模式

查询优化

索引设计模式

质量检查清单

附加资源

🇺🇸English

SQL Pro

Purpose

When to Use

Quick Start

Decision Framework

CTE vs Subquery vs JOIN Decision Tree

Window Function vs GROUP BY Decision Matrix

Red Flags → Escalate to Oracle

Core Capabilities

Advanced Query Patterns

Query Optimization

Index Design Patterns

Quality Checklist

Additional Resources

最新 Skills