Does it automatically apply changes to my database?

No, it provides structured proposals including a summary, impact analysis, and verification steps, allowing developers to review and execute changes through their own migration workflows.

Which database systems does this skill support?

It is designed to work with major relational and non-relational systems including PostgreSQL, MySQL, SQLite, and other SQL-based environments.

Can it help with N+1 query problems?

Yes, one of its core responsibilities is identifying inefficient query patterns like N+1 and suggesting batching, improved joins, or pagination to resolve them.

Is the Database Optimizer safe for production use?

Yes, it follows a conservative 'safety-first' approach, requiring context discovery and impact measurement before suggesting any changes, and always provides a rollback plan.

How does it handle large-scale schema changes?

It analyzes data volume and growth patterns to suggest migrations with low-risk execution paths, such as partitioning or materialized views, while always prioritizing data preservation.

Database Optimizer

Name: Database Optimizer
Author: Masked-Kunsiquat

byMasked-Kunsiquat

데이터베이스 관리

Optimizes database performance and scalability through safe query rewriting, strategic indexing, and system configuration tuning.

소개

The Database Optimizer is a specialized Claude Code skill designed to identify and resolve performance bottlenecks in database systems like PostgreSQL, MySQL, and SQLite. It operates with a 'measurement-first' philosophy, ensuring that any changes to queries, indexes, or schemas are backed by execution plan analysis and data-driven insights. By prioritizing data integrity and operational stability, this skill helps developers eliminate slow queries, reduce lock contention, and implement robust scalability patterns without risking production downtime or data loss.

주요 기능

Safe query rewriting to eliminate N+1 patterns and inefficient joins
Schema-level optimization suggestions like partitioning and archival
Database system configuration tuning for memory and connection pooling
0 GitHub stars
Data-driven indexing strategies including partial and expression indexes
In-depth execution plan analysis and bottleneck identification

사용 사례

Designing efficient indexing strategies for rapidly growing application datasets
Troubleshooting and fixing slow-running production queries causing high latency
Auditing database schemas to reduce resource contention and improve write throughput

소개

주요 기능

Safe query rewriting to eliminate N+1 patterns and inefficient joins
Schema-level optimization suggestions like partitioning and archival
Database system configuration tuning for memory and connection pooling
0 GitHub stars
Data-driven indexing strategies including partial and expression indexes
In-depth execution plan analysis and bottleneck identification

사용 사례

Designing efficient indexing strategies for rapidly growing application datasets
Troubleshooting and fixing slow-running production queries causing high latency
Auditing database schemas to reduce resource contention and improve write throughput