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supabase-postgres-best-practices

Identify and implement PostgreSQL performance improvements for Supabase projects.

Updated Aug 23, 2026
Search Tags:#performance#indexing#rls#partitioning#query-optimization#postgres#schema-design
One-click install
npx skills add https://github.com/siriwatknp/jun-cc --skill supabase-postgres-best-practices-siriwatknp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: supabase-postgres-best-practices
Source: https://github.com/siriwatknp/jun-cc/tree/main/skills/supabase-postgres-best-practices
Command: npx skills add https://github.com/siriwatknp/jun-cc --skill supabase-postgres-best-practices-siriwatknp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams optimize Postgres performance across queries, connections, schema design, and security to maximize efficiency and reliability.

Core Features & Use Cases

  • Query optimization: identify missing indexes, appropriate index types, and composite or covering indexes for faster queries.
  • Schema tuning: data types, foreign-key indexing, partitioning, and RLS patterns for multi-tenant data.
  • Operational guidance: connection pooling, prepared statements, autovacuum tuning, and monitoring recommendations.
  • Use case: optimize a large product catalog by applying indexing and partitioning strategies to improve response times.

Quick Start

Analyze the current PostgreSQL schema and workload to produce a prioritized optimization plan.

Frequently Asked Questions about supabase-postgres-best-practices

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

FAQPage Schema
What's the best way to optimize slow PostgreSQL queries in a Supabase project?▼

To optimize slow PostgreSQL queries, analyze your workload to identify missing indexes and apply appropriate index types like composite or covering indexes for faster data retrieval. This query tuning approach directly improves database response times.

How do I design a PostgreSQL schema for multi-tenant data with RLS?▼

Designing a multi-tenant PostgreSQL schema involves applying Row Level Security (RLS) patterns and partitioning strategies. Proper schema tuning ensures secure data isolation and efficient querying across tenant boundaries.

How does connection pooling affect PostgreSQL performance in Supabase?▼

Connection pooling manages database connections to prevent exhaustion and maintain PostgreSQL performance. Configuring pooling alongside prepared statements and autovacuum tuning ensures reliable operational throughput under concurrent load.

When do I need table partitioning for a large PostgreSQL product catalog?▼

You need table partitioning when managing large datasets like a product catalog that cause slow queries. Applying partitioning strategies alongside proper indexing breaks data into manageable chunks to improve response times.

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