What kind of output does the SQL Query Optimizer produce?

It generates implementation artifacts (scripts/code), comprehensive documentation of changes, validation test results, and monitoring configuration dashboards.

Which database systems are compatible with this skill?

The SQL Query Optimizer supports PostgreSQL (psql), MySQL (mysql), and MongoDB (mongosh) through its integrated bash tool capabilities.

How does this skill ensure safe database changes?

It follows a strict five-step process that mandates assessment, non-production testing, monitoring setup, and the creation of rollback procedures before any production deployment.

Can this skill help with indexing strategies?

Yes, it analyzes existing schemas and query patterns to suggest optimal indexing strategies that can significantly reduce data retrieval times.

SQL Query Optimizer

Name: SQL Query Optimizer
Author: BbgnsurfTech

byBbgnsurfTech

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데이터베이스 관리

Analyzes and optimizes database queries to improve performance, reduce latency, and minimize resource consumption across SQL and NoSQL systems.

소개

The SQL Query Optimizer skill provides Claude with a structured framework to diagnose and resolve database performance bottlenecks. It enables automated query analysis, indexing strategy suggestions, and execution plan optimization for PostgreSQL, MySQL, and MongoDB. By following a rigorous five-step workflow—from initial assessment to production deployment—this skill ensures that database changes are implemented safely, validated against performance baselines, and documented thoroughly to prevent regressions and improve overall application scalability.

주요 기능

Multi-database support including PostgreSQL, MySQL, and MongoDB (mongosh)
1 GitHub stars
Structured implementation workflow with built-in rollback and recovery planning
Comprehensive query performance analysis and bottleneck identification
Automated refactoring of inefficient queries based on best practices
Post-optimization validation through metric comparison and test results

사용 사례

Optimizing database resource usage to reduce cloud infrastructure costs
Identifying and fixing slow-running queries that cause application timeouts
Modernizing legacy database schemas and query patterns for better scalability

소개

주요 기능

Multi-database support including PostgreSQL, MySQL, and MongoDB (mongosh)
1 GitHub stars
Structured implementation workflow with built-in rollback and recovery planning
Comprehensive query performance analysis and bottleneck identification
Automated refactoring of inefficient queries based on best practices
Post-optimization validation through metric comparison and test results

사용 사례

Optimizing database resource usage to reduce cloud infrastructure costs
Identifying and fixing slow-running queries that cause application timeouts
Modernizing legacy database schemas and query patterns for better scalability