Does it support advanced Postgres features like PostGIS or JSONB?

Yes, the skill includes patterns for semi-structured JSONB data, vector types for AI embeddings, and geometric types, along with advice on when to use GIN or GiST indexes for these types.

Can it help with database performance tuning?

Absolutely. It offers specific strategies for indexing access paths, managing MVCC bloat, and optimizing workloads for both insert-heavy and update-heavy tables.

Why use this skill instead of standard Claude for database design?

This skill provides specialized, opinionated guidance on PostgreSQL-specific features—such as using identity columns over serial and TIMESTAMPTZ over timestamp—ensuring your schema is optimized for Postgres internals rather than generic SQL.

How does this skill handle schema migrations?

It emphasizes safe schema evolution through transactional DDL patterns, ensuring your database changes can be rolled back and don't cause silent errors or data truncation.

PostgreSQL Schema & Table Design

Name: PostgreSQL Schema & Table Design
Author: Microck

byMicrock

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114

Database Management

Designs production-grade PostgreSQL schemas using industry-standard best practices for performance, scalability, and data integrity.

About

This skill empowers Claude with deep expertise in PostgreSQL database modeling, providing authoritative guidance on schema architecture. It covers everything from selecting the correct data types (like TIMESTAMPTZ and BIGINT) to advanced indexing strategies (GIN, GiST, BRIN) and partitioning for large-scale datasets. Whether you are building a new application or refactoring an existing database, this skill ensures your PostgreSQL design follows proven patterns to avoid common pitfalls like silent coercion, inefficient FK management, and dead tuple bloat.

Key Features

Best-practice data type selection for identity, time, and currency
Advanced indexing strategies including GIN, GiST, and Partial indexes
Guidance on 3rd Normal Form (3NF) vs. strategic denormalization
Scalable partitioning techniques (Range, List, Hash) for large datasets
Safe schema evolution patterns using transactional DDL
114 GitHub stars

Use Cases

Implementing row-level security and complex constraints for enterprise-grade data integrity
Architecting a high-performance relational database for a new backend application
Optimizing existing PostgreSQL tables for faster queries and reduced storage bloat

About

Key Features

Best-practice data type selection for identity, time, and currency
Advanced indexing strategies including GIN, GiST, and Partial indexes
Guidance on 3rd Normal Form (3NF) vs. strategic denormalization
Scalable partitioning techniques (Range, List, Hash) for large datasets
Safe schema evolution patterns using transactional DDL
114 GitHub stars

Use Cases

Implementing row-level security and complex constraints for enterprise-grade data integrity
Architecting a high-performance relational database for a new backend application
Optimizing existing PostgreSQL tables for faster queries and reduced storage bloat