Can this skill help with large-scale data management?

Yes, it includes detailed patterns for declarative partitioning (Range, List, and Hash) to manage tables exceeding 100 million rows efficiently.

How does this skill handle time-zone sensitive data?

The skill strictly enforces the use of TIMESTAMPTZ over TIMESTAMP to ensure all time data is stored with UTC offsets, preventing common timezone-related bugs.

Does it provide advice for JSON storage?

It recommends JSONB for most semi-structured data needs due to its support for GIN indexing and efficient binary storage, reserving JSON only for cases where original ordering must be preserved.

Why does this skill discourage using the SERIAL data type?

It favors 'GENERATED ALWAYS AS IDENTITY' which is SQL standard compliant and provides cleaner behavior for identity management compared to the legacy SERIAL macro.

PostgreSQL Schema Architect

Name: PostgreSQL Schema Architect
Author: HermeticOrmus

byHermeticOrmus

데이터베이스 관리

Designs high-performance, production-grade PostgreSQL schemas optimized for durability, scalability, and modern data standards.

소개

This skill empowers Claude with expert-level PostgreSQL architectural guidance, focusing on 3rd Normal Form (3NF) normalization, efficient data type selection, and advanced indexing strategies. It enforces strict industry best practices, such as using identity columns instead of legacy serial types and prioritizing TIMESTAMPTZ for global consistency. Whether designing multi-tenant SaaS databases or high-throughput time-series tables, this skill provides the patterns needed for Row-Level Security, partitioning, and performance tuning to ensure your database remains robust under heavy workloads.

주요 기능

0 GitHub stars
Advanced indexing strategies (B-tree, GIN, GiST, BRIN, and Partial)
Partitioning patterns for massive datasets (Range, List, and Hash)
Optimized data type selection including JSONB, Vector, and Range types
Standardized identity columns and primary key best practices
Row-Level Security (RLS) and policy-driven access control

사용 사례

Refactoring legacy database structures to modern PostgreSQL standards
Designing a scalable relational schema for a multi-tenant web application
Optimizing database performance for high-concurrency write and read operations

소개

주요 기능

0 GitHub stars
Advanced indexing strategies (B-tree, GIN, GiST, BRIN, and Partial)
Partitioning patterns for massive datasets (Range, List, and Hash)
Optimized data type selection including JSONB, Vector, and Range types
Standardized identity columns and primary key best practices
Row-Level Security (RLS) and policy-driven access control

사용 사례

Refactoring legacy database structures to modern PostgreSQL standards
Designing a scalable relational schema for a multi-tenant web application
Optimizing database performance for high-concurrency write and read operations