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postgres-patterns

PostgreSQL 데이터베이스 패턴: 쿼리 최적화, 스키마 설계, 인덱싱 및 보안. Supabase 모범 사례를 기반으로 합니다. PostgreSQL 스키마, 인덱스 또는 RLS 정책을 설계하거나 쿼리가 너무 느릴 때 사용하세요.

postgres-patterns란 무엇인가요?

이 스킬은 PostgreSQL 데이터베이스 작업 시 개발자와 AI 에이전트가 즉시 참조할 수 있는 모범 사례 모음입니다. 쿼리 최적화, 스키마 디자인, 인덱싱 전략, 보안(특히 Row-Level Security)을 다루며, Supabase 팀의 실제 경험을 기반으로 합니다. 주로 Claude Code, Cursor, Codex 등의 터미널 기반 AI 코딩 어시스턴트에서 사용되며, SQL 마이그레이션 작성, 느린 쿼리 문제 해결, 연결 풀링 설정 시 활성화하는 것이 좋습니다. 제공되는 인덱스 치트 시트, 데이터 타입 가이드, 공통 패턴(복합 인덱스, 커버링 인덱스, 부분 인덱스, UPSERT, 커서 페이지네이션, 큐 처리) 및 안티패턴 감지 쿼리는 실무에서 바로 적용 가능합니다. 또한 PostgreSQL 설정 템플릿을 제공하여 보안 기본값과 모니터링 확장을 쉽게 구성할 수 있도록 도와줍니다.

지원 대상~Claude Code~Codex CLI✓Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/postgres-patterns

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이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

PostgreSQL 패턴

PostgreSQL 모범 사례에 대한 빠른 참조입니다. 자세한 안내는 database-reviewer 에이전트를 사용하세요.

활성화 시점

  • SQL 쿼리 또는 마이그레이션 작성 시
  • 데이터베이스 스키마 설계 시
  • 느린 쿼리 문제 해결 시
  • Row-Level Security 구현 시
  • 연결 풀링 설정 시

빠른 참조

인덱스 치트 시트

쿼리 패턴인덱스 유형예시
WHERE col = valueB-tree (기본)CREATE INDEX idx ON t (col)
WHERE col > valueB-treeCREATE INDEX idx ON t (col)
WHERE a = x AND b > y복합CREATE INDEX idx ON t (a, b)
WHERE jsonb @> '{}'GINCREATE INDEX idx ON t USING gin (col)
WHERE tsv @@ queryGINCREATE INDEX idx ON t USING gin (col)
시계열 범위BRINCREATE INDEX idx ON t USING brin (col)

데이터 타입 빠른 참조

사용 사례올바른 타입피해야 할 타입
IDbigintint, 랜덤 UUID
문자열textvarchar(255)
타임스탬프timestamptztimestamp
금액numeric(10,2)float
플래그booleanvarchar, int

공통 패턴

복합 인덱스 순서:

-- 동등 조건 컬럼 먼저, 그 다음 범위 조건 컬럼
CREATE INDEX idx ON orders (status, created_at);
-- 다음에 대해 작동: WHERE status = 'pending' AND created_at > '2024-01-01'

커버링 인덱스:

CREATE INDEX idx ON users (email) INCLUDE (name, created_at);
-- SELECT email, name, created_at 에 대해 테이블 조회 회피

부분 인덱스:

CREATE INDEX idx ON users (email) WHERE deleted_at IS NULL;
-- 더 작은 인덱스, 활성 사용자만 포함

RLS 정책 (최적화됨):

CREATE POLICY policy ON orders
  USING ((SELECT auth.uid()) = user_id);  -- SELECT로 감싸기!

UPSERT:

INSERT INTO settings (user_id, key, value)
VALUES (123, 'theme', 'dark')
ON CONFLICT (user_id, key)
DO UPDATE SET value = EXCLUDED.value;

커서 페이지네이션:

SELECT * FROM products WHERE id > $last_id ORDER BY id LIMIT 20;
-- O(1) vs OFFSET은 O(n)

큐 처리:

UPDATE jobs SET status = 'processing'
WHERE id = (
  SELECT id FROM jobs WHERE status = 'pending'
  ORDER BY created_at LIMIT 1
  FOR UPDATE SKIP LOCKED
) RETURNING *;

안티패턴 탐지

-- 인덱스되지 않은 외래 키 찾기
SELECT conrelid::regclass, a.attname
FROM pg_constraint c
JOIN pg_attribute a ON a.attrelid = c.conrelid AND a.attnum = ANY(c.conkey)
WHERE c.contype = 'f'
  AND NOT EXISTS (
    SELECT 1 FROM pg_index i
    WHERE i.indrelid = c.conrelid AND a.attnum = ANY(i.indkey)
  );

-- 느린 쿼리 찾기
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
WHERE mean_exec_time > 100
ORDER BY mean_exec_time DESC;

-- 테이블 블로트 확인
SELECT relname, n_dead_tup, last_vacuum
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;

구성 템플릿

-- 연결 제한 (RAM에 따라 조정)
ALTER SYSTEM SET max_connections = 100;
ALTER SYSTEM SET work_mem = '8MB';

-- 타임아웃
ALTER SYSTEM SET idle_in_transaction_session_timeout = '30s';
ALTER SYSTEM SET statement_timeout = '30s';

-- 모니터링
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- 보안 기본값
REVOKE ALL ON SCHEMA public FROM public;

SELECT pg_reload_conf();

관련 항목

  • 에이전트: database-reviewer - 전체 데이터베이스 리뷰 워크플로우
  • 스킬: clickhouse-io - ClickHouse 분석 패턴
  • 스킬: backend-patterns - API 및 백엔드 패턴

Supabase Agent Skills 기반 (크레딧: Supabase 팀) (MIT 라이선스)

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

관련 스킬