Why is capturing a baseline necessary?

Capturing a baseline using EXPLAIN (ANALYZE, BUFFERS) ensures that every change is measured against real performance metrics to verify actual improvement before proceeding.

How does this differ from standard SQL query optimization?

While it can assist with queries, this skill focuses on server-side configuration, indexing strategies, and infrastructure settings that query rewrites alone cannot fix.

Can it help with database locking issues?

Yes, it identifies lock contention and provides strategies to minimize blocking, such as recommending the use of CONCURRENTLY during index creation.

Does it support cloud databases like AWS RDS?

Yes, it recognizes constraints of cloud-managed platforms and suggests using parameter groups or console settings instead of direct configuration file edits.

Database Optimizer

Name: Database Optimizer
Author: newkayak12

bynewkayak12

0•

Database Management

Optimizes database infrastructure and server configurations to resolve performance bottlenecks beyond simple query rewriting.

Database Optimizer is a specialized skill for Claude Code designed to tackle deep-seated database performance issues rooted in infrastructure and server settings. Unlike basic query tuners, it focuses on memory management (shared_buffers, work_mem), I/O configuration, connection pooling, lock contention, and maintenance tasks like VACUUM and statistics updates. It provides a structured workflow—from capturing EXPLAIN ANALYZE baselines to implementing incremental changes—ensuring that PostgreSQL, MySQL, and cloud-managed databases like AWS RDS operate at peak efficiency with measurable results.

Key Features

01Infrastructure-level tuning for database memory, I/O, and WAL configurations

02Tailored recommendations for cloud-managed platforms including AWS RDS, Aurora, and Google Cloud SQL

03Maintenance optimization for VACUUM lag, statistics staleness, and lock contention

04Strategic design for table partitioning and advanced index structures including partial and expression indexes

050 GitHub stars

06Analysis of EXPLAIN (ANALYZE, BUFFERS) outputs to identify hidden cache and I/O bottlenecks

Use Cases

01Implementing non-blocking concurrent indexing strategies for multi-million row production tables

02Resolving disk-spilling sort errors by tuning session-level memory and buffer pool settings

03Scaling a PostgreSQL database that is hitting performance limits under high concurrent load

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add newkayak12/claude-skills database-optimizer

For use in Claude.ai and ChatGPT

Key Features

01Infrastructure-level tuning for database memory, I/O, and WAL configurations

02Tailored recommendations for cloud-managed platforms including AWS RDS, Aurora, and Google Cloud SQL

03Maintenance optimization for VACUUM lag, statistics staleness, and lock contention

04Strategic design for table partitioning and advanced index structures including partial and expression indexes

050 GitHub stars

06Analysis of EXPLAIN (ANALYZE, BUFFERS) outputs to identify hidden cache and I/O bottlenecks

Use Cases

01Implementing non-blocking concurrent indexing strategies for multi-million row production tables

02Resolving disk-spilling sort errors by tuning session-level memory and buffer pool settings

03Scaling a PostgreSQL database that is hitting performance limits under high concurrent load