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postgres patterns

PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

GitHub
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0.0
0 installs262.1K GitHub starsby affaan-m

Skill Introduction

Overview
PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community work faster.

Target users

  • Developers, testers, and maintainers who handle Development tasks in Focus Code.
  • Teams that already trust workflows or content from affaan-m.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Development, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, so it can be filtered by concrete task intent.

Install and use

Install
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focus install postgres-patterns-1687a9
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SKILL.md

Primary filemarkdown5 KB

name: postgres-patterns description: PostgreSQL database patterns for query optimization, schema design, indexing, and security. Based on Supabase best practices.

PostgreSQL パターン

PostgreSQLベストプラクティスのクイックリファレンス。詳細なガイダンスについては、database-reviewer エージェントを使用してください。

起動タイミング

  • SQLクエリまたはマイグレーションの作成時
  • データベーススキーマの設計時
  • 低速クエリのトラブルシューティング時
  • Row Level Securityの実装時
  • コネクションプーリングの設定時

クイックリファレンス

インデックスチートシート

クエリパターンインデックスタイプ例
WHERE col = valueB-tree(デフォルト)CREATE INDEX idx ON t (col)
WHERE col > valueB-treeCREATE INDEX idx ON t (col)
WHERE a = x AND b > y複合CREATE 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)
時系列範囲BRINCREATE INDEX idx ON t USING brin (col)

データタイプクイックリファレンス

用途正しいタイプ避けるべき
IDbigintint、ランダムUUID
文字列textvarchar(255)
タイムスタンプtimestamptztimestamp
金額numeric(10,2)float
フラグbooleanvarchar、int

一般的なパターン

複合インデックスの順序:

-- 等価列を最初に、次に範囲列
CREATE INDEX idx ON orders (status, created_at);
-- 次の場合に機能: WHERE status = 'pending' AND created_at > '2024-01-01'

カバリングインデックス:

CREATE INDEX idx ON users (email) INCLUDE (name, created_at);
-- SELECT email, name, created_at のテーブル検索を回避

部分インデックス:

CREATE INDEX idx ON users (email) WHERE deleted_at IS NULL;
-- より小さなインデックス、アクティブユーザーのみを含む

RLSポリシー(最適化):

CREATE POLICY policy ON orders
  USING ((SELECT auth.uid()) = user_id);  -- SELECTでラップ!

UPSERT:

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

カーソルページネーション:

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

キュー処理:

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 *;

アンチパターン検出

-- インデックスのない外部キーを検索
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)
  );

-- 低速クエリを検索
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
WHERE mean_exec_time > 100
ORDER BY mean_exec_time DESC;

-- テーブル肥大化をチェック
SELECT relname, n_dead_tup, last_vacuum
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;

設定テンプレート

-- 接続制限(RAMに応じて調整)
ALTER SYSTEM SET max_connections = 100;
ALTER SYSTEM SET work_mem = '8MB';

-- タイムアウト
ALTER SYSTEM SET idle_in_transaction_session_timeout = '30s';
ALTER SYSTEM SET statement_timeout = '30s';

-- モニタリング
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- セキュリティデフォルト
REVOKE ALL ON SCHEMA public FROM public;

SELECT pg_reload_conf();

関連

  • Agent: database-reviewer - 完全なデータベースレビューワークフロー
  • Skill: clickhouse-io - ClickHouse分析パターン
  • Skill: backend-patterns - APIとバックエンドパターン

[Supabase Agent Skills](Supabase Agent Skills (credit: Supabase team))(MITライセンス)に基づく

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