Database Query Optimization

Speeds up slow database queries by walking you through execution plan analysis, indexing strategies, and query rewrites. You'll use EXPLAIN ANALYZE to spot sequential scans that should be index scans, then apply fixes based on the specific bottleneck. Works well when you're debugging performance regressions or trying to reduce database CPU before scaling up hardware. The reference guides cover common patterns like avoiding N+1 queries and choosing between different index types. Honest take: this is solid for the 80% of query problems that come down to missing indexes or bad joins, but complex optimization still needs domain expertise.

skills.sh installs: 593
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill database-query-optimization --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Slow database queries are a common performance bottleneck. Optimization through indexing, efficient queries, and caching dramatically improves application performance.

When to Use

Slow response times
High database CPU usage
Performance regression
New feature deployment
Regular maintenance

Quick Start

Minimal working example:

-- Analyze query performance

EXPLAIN ANALYZE
SELECT users.id, users.name, COUNT(orders.id) as order_count
FROM users
LEFT JOIN orders ON users.id = orders.user_id
WHERE users.created_at > '2024-01-01'
GROUP BY users.id, users.name
ORDER BY order_count DESC;

-- Results show:
-- - Seq Scan (slow) vs Index Scan (fast)
-- - Rows: actual vs planned (high variance = bad)
-- - Execution time (milliseconds)

-- Key metrics:
-- - Sequential Scan: Full table read (slow)
-- - Index Scan: Uses index (fast)
-- - Nested Loop: Joins with loops
-- - Sort: In-memory or disk sort