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database-optimizer

Analyze execution plans and indexes to resolve database performance bottlenecks.

8|11|Updated Feb 15, 2026
Search Tags:#indexing#database#postgresql#optimization#mysql#mongodb#execution-plan
One-click install
npx skills add https://github.com/belokonm/claude-supercode-skills --skill database-optimizer-belokonm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: database-optimizer
Source: https://github.com/belokonm/claude-supercode-skills/tree/main/database-optimizer-skill
Command: npx skills add https://github.com/belokonm/claude-supercode-skills --skill database-optimizer-belokonm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficiently diagnosing and solving database performance issues across PostgreSQL, MySQL, MongoDB, and Redis to deliver sub-second queries and stable workloads.

Core Features & Use Cases

  • Query optimization: analyze execution plans, rewrite queries, optimize joins and aggregations.
  • Index design and maintenance: create and manage composite, partial, and covering indexes to improve filter and join performance.
  • Configuration and capacity planning: tune memory, connections, and partitioning to fit OLTP vs OLAP workloads.
  • Monitoring and validation: baseline performance, track improvements, and provide actionable recommendations.

Quick Start

Run an EXPLAIN ANALYZE on a slow query to identify bottlenecks and begin indexing.

Frequently Asked Questions about database-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Why does my PostgreSQL query have high latency and how do I analyze the execution plan?▼

Database indexing creates secondary data structures to accelerate query filtering and joins. By designing composite, partial, and covering indexes, you drastically reduce disk I/O and improve filter performance across PostgreSQL, MySQL, and MongoDB workloads.

What's the best way to tune database configuration for OLTP versus OLAP workloads?▼

Yes, MongoDB query optimization relies on proper index design and execution plan analysis. You can identify performance bottlenecks using execution plans, then create targeted indexes and rewrite queries to improve filter performance and aggregation pipeline efficiency.

How do I optimize slow database queries using EXPLAIN ANALYZE?▼

Redis performance optimization focuses on configuration tuning and capacity planning. By monitoring memory usage and connection limits, you can identify bottlenecks and adjust configurations to stabilize workloads alongside your primary database systems.

When should I use partial or covering indexes for database optimization?▼

Validating database optimization changes requires benchmarking and monitoring before and after adjustments. By baselining performance with EXPLAIN ANALYZE and tracking metrics through incremental changes, you ensure safe, measurable improvements to query latency and workload stability.

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