How does it help with query performance?

It analyzes current query patterns, identifies missing indexes, and provides optimized DDL scripts to reduce execution time and resource consumption.

Can it help remove unused or redundant indexes?

Yes, it can identify overlapping or unnecessary indexes that may be slowing down write operations and suggest refactoring or removal plans.

Which databases does the Index Advisor support?

The skill provides comprehensive support for major relational and document databases including PostgreSQL, MySQL, and MongoDB via native CLI tools.

Is it safe to use on production databases?

The skill follows a strict safety protocol, emphasizing assessment in staging environments, data backups, and monitoring before any production deployment.

Database Index Advisor

Name: Database Index Advisor
Author: BbgnsurfTech

byBbgnsurfTech

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Database Management

Optimizes database query performance through intelligent index design, implementation, and automated performance validation.

About

The Database Index Advisor is a comprehensive Claude Code skill designed to streamline database performance tuning. It enables Claude to analyze existing schema configurations, identify slow queries, and recommend optimal indexing strategies for PostgreSQL, MySQL, and MongoDB. By following a structured five-step workflow—from initial assessment and risk mitigation to staging implementation and production deployment—it ensures that performance improvements are data-driven, safe, and fully documented with accompanying runbooks and monitoring setups.

Key Features

Risk-aware implementation plans including rollback and recovery procedures
Comprehensive validation against baseline performance metrics
Automated query performance analysis and bottleneck identification
Multi-database support for PostgreSQL, MySQL, and MongoDB environments
3 GitHub stars
Automated generation of migration scripts and configuration files

Use Cases

Refactoring redundant or inefficient indexes to reduce storage and write overhead
Designing initial indexing strategies for new database schemas and models
Resolving slow query performance in high-traffic production applications

About

Key Features

Risk-aware implementation plans including rollback and recovery procedures
Comprehensive validation against baseline performance metrics
Automated query performance analysis and bottleneck identification
Multi-database support for PostgreSQL, MySQL, and MongoDB environments
3 GitHub stars
Automated generation of migration scripts and configuration files

Use Cases

Refactoring redundant or inefficient indexes to reduce storage and write overhead
Designing initial indexing strategies for new database schemas and models
Resolving slow query performance in high-traffic production applications