Generated banner for Supabase Postgres Best Practices
Generated logo for Supabase Postgres Best Practices

Supabase Postgres Best Practices

JetBrainsOfficialCreated by SupabaseDistributed by JetBrains

  • Skill
  • Guidelines
  • Software development
  • MIT
  • No issues found

From the author

Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.
— the description by Supabase

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive set of performance-oriented guidelines for PostgreSQL, specifically curated for developers using Supabase. It organizes best practices into eight prioritized categories ranging from critical query performance and connection management to advanced features. Each rule follows a standardized format that includes clear explanations, anti-pattern identification, and concrete SQL transformation examples to help developers and AI agents write more efficient database code.

What it does

  • Provides actionable SQL transformation patterns to replace inefficient queries.
  • Categorizes database best practices by performance impact level.
  • Offers guidance on connection pooling and serverless database configurations.
  • Includes standards for Row-Level Security and schema design.
  • Supplies examples for resolving common issues like N+1 queries and missing indexes.
  • Standardizes the documentation of database performance rules for AI consumption.

When to use it

  • When writing or reviewing SQL queries for production applications.
  • During database schema design to ensure long-term scalability.
  • When troubleshooting slow query performance or high database load.
  • When configuring connection limits or implementing transaction management.
  • When optimizing Row-Level Security policies for better performance.

Advantages

  • Prioritizes optimizations based on real-world performance impact.
  • Uses a consistent error-first structure that makes anti-patterns easy to identify.
  • Provides concrete, runnable SQL examples for immediate application.
  • Includes quantified impact metrics to help developers prioritize tasks.
  • Reduces ambiguity by focusing on specific transformation patterns.

Usage examples

“How can I optimize my Postgres query to avoid N+1 issues when fetching user posts?”
The agent will reference the data-n-plus-one guidelines, showing an incorrect loop-based query followed by a corrected, batched SQL query using the ANY operator.
“What is the best way to handle connection pooling in a serverless environment?”
The agent will provide guidance from the connection management section, explaining the necessity of connection pooling and how to configure it effectively.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/jetbrains/supabase-postgres-best-practices/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip supabase-postgres-best-practices.zip -d .agents/skills

Documentation by Supabase

Supabase Postgres Best Practices

Comprehensive performance optimization guide for Postgres, maintained by Supabase. Contains rules across 8 categories, prioritized by impact to guide automated query optimization and schema design.

When to Apply

Reference these guidelines when:

  • Writing SQL queries or designing schemas

  • Implementing indexes or query optimization

  • Reviewing database performance issues

  • Configuring connection pooling or scaling

  • Optimizing for Postgres-specific features

  • Working with Row-Level Security (RLS)

Rule Categories by Priority

PriorityCategoryImpactPrefix
1Query PerformanceCRITICALquery-
2Connection ManagementCRITICALconn-
3Security & RLSCRITICALsecurity-
4Schema DesignHIGHschema-
5Concurrency & LockingMEDIUM-HIGHlock-
6Data Access PatternsMEDIUMdata-
7Monitoring & DiagnosticsLOW-MEDIUMmonitor-
8Advanced FeaturesLOWadvanced-

How to Use

Read individual rule files for detailed explanations and SQL examples:

references/query-missing-indexes.md
references/schema-partial-indexes.md
references/_sections.md

Each rule file contains:

  • Brief explanation of why it matters

  • Incorrect SQL example with explanation

  • Correct SQL example with explanation

  • Optional EXPLAIN output or metrics

  • Additional context and references

  • Supabase-specific notes (when applicable)

References

Files

references/_contributing.md

Writing Guidelines for Postgres References

This document provides guidelines for creating effective Postgres best practice references that work well with AI agents and LLMs.

Key Principles

1. Concrete Transformation Patterns

Show exact SQL rewrites. Avoid philosophical advice.

Good: "Use WHERE id = ANY(ARRAY[...]) instead of WHERE id IN (SELECT ...)" Bad: "Design good schemas"

2. Error-First Structure

Always show the problematic pattern first, then the solution. This trains agents to recognize anti-patterns.

**Incorrect (sequential queries):** [bad example]

**Correct (batched query):** [good example]
3. Quantified Impact

Include specific metrics. Helps agents prioritize fixes.

Good: "10x faster queries", "50% smaller index", "Eliminates N+1" Bad: "Faster", "Better", "More efficient"

4. Self-Contained Examples

Examples should be complete and runnable (or close to it). Include CREATE TABLE if context is needed.

-- Include table definition when needed for clarity
CREATE TABLE users (
  id bigint PRIMARY KEY,
  email text NOT NULL,
  deleted_at timestamptz
);

-- Now show the index
CREATE INDEX users_active_email_idx ON users(email) WHERE deleted_at IS NULL;
5. Semantic Naming

Use meaningful table/column names. Names carry intent for LLMs.

Good: users, email, created_at, is_active Bad: table1, col1, field, flag


Code Example Standards

SQL Formatting
-- Use lowercase keywords, clear formatting
CREATE INDEX CONCURRENTLY users_email_idx
  ON users(email)
  WHERE deleted_at IS NULL;

-- Not cramped or ALL CAPS
CREATE INDEX CONCURRENTLY USERS_EMAIL_IDX ON USERS(EMAIL) WHERE DELETED_AT IS NULL;
Comments
  • Explain why, not what

  • Highlight performance implications

  • Point out common pitfalls

Language Tags
  • sql - Standard SQL queries

  • plpgsql - Stored procedures/functions

  • typescript - Application code (when needed)

  • python - Application code (when needed)


When to Include Application Code

Default: SQL Only

Most references should focus on pure SQL patterns. This keeps examples portable.

Include Application Code When:

  • Connection pooling configuration

  • Transaction management in application context

  • ORM anti-patterns (N+1 in Prisma/TypeORM)

  • Prepared statement usage

Format for Mixed Examples:

**Incorrect (N+1 in application):**

```typescript
for (const user of users) {
  const posts = await db.query("SELECT * FROM posts WHERE user_id = $1", [
    user.id,
  ]);
}
```

Correct (batch query):

const posts = await db.query("SELECT * FROM posts WHERE user_id = ANY($1)", [
  userIds,
]);

Impact Level Guidelines

LevelImprovementUse When
CRITICAL10-100xMissing indexes, connection exhaustion, sequential scans on large tables
HIGH5-20xWrong index types, poor partitioning, missing covering indexes
MEDIUM-HIGH2-5xN+1 queries, inefficient pagination, RLS optimization
MEDIUM1.5-3xRedundant indexes, query plan instability
LOW-MEDIUM1.2-2xVACUUM tuning, configuration tweaks
LOWIncrementalAdvanced patterns, edge cases

Reference Standards

Primary Sources:

  • Official Postgres documentation

  • Supabase documentation

  • Postgres wiki

  • Established blogs (2ndQuadrant, Crunchy Data)

Format:

Reference:
[Postgres Indexes](https://www.postgresql.org/docs/current/indexes.html)

Review Checklist

Before submitting a reference:

  • [ ] Title is clear and action-oriented

  • [ ] Impact level matches the performance gain

  • [ ] impactDescription includes quantification

  • [ ] Explanation is concise (1-2 sentences)

  • [ ] Has at least 1 Incorrect SQL example

  • [ ] Has at least 1 Correct SQL example

  • [ ] SQL uses semantic naming

  • [ ] Comments explain why, not what

  • [ ] Trade-offs mentioned if applicable

  • [ ] Reference links included

  • [ ] npm run validate passes

  • [ ] npm run build generates correct output

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Supabase
Publisher
JetBrains (Official)
Distributed by
JetBrains
Source
JetBrains/skills · postgres-best-practices · commit 3f53782
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Supabase or JetBrains.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download supabase-postgres-best-practices.zip