How does it handle database security?

It includes patterns for Row-Level Security (RLS) and complex constraints, such as EXCLUDE for preventing overlapping ranges and CHECK for domain-specific data validation.

What PostgreSQL data types does this skill recommend?

The skill prioritizes modern, efficient types: BIGINT for IDs, TIMESTAMPTZ for time, TEXT for strings, and NUMERIC for currency, specifically advising against legacy types like SERIAL, MONEY, and VARCHAR(n).

Can this skill help with advanced PostgreSQL features like JSONB or Vector search?

Absolutely. It covers the optimal use of JSONB for semi-structured data, GIN indexing for search, and the implementation of vector types for AI-driven similarity search.

Does this skill help with database performance tuning?

Yes, it provides detailed guidance on indexing access paths, implementing partitioning for large tables, and using specific storage strategies like FILLFACTOR for update-heavy workloads.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: drgaciw

bydrgaciw

Database Management

Designs high-performance, production-grade PostgreSQL schemas using domain-specific best practices and advanced indexing strategies.

About

This skill provides Claude with expert-level guidance for architecting PostgreSQL databases that are both performant and maintainable. It covers deep technical aspects of schema design, including optimal data type selection, 3NF normalization, and advanced indexing techniques like GIN and GiST. By following these patterns, developers can avoid common PostgreSQL pitfalls—such as silent coercions or unindexed foreign keys—while leveraging modern features like Row-Level Security (RLS), declarative partitioning, and efficient JSONB storage. It is particularly useful for developers building scalable backends, time-series applications, or complex relational systems that require strict data integrity.

Key Features

Strict adherence to modern PostgreSQL standards and naming conventions.
Expert data type selection focusing on performance and precision (TIMESTAMPTZ, BIGINT, JSONB).
Implementation of robust constraints and Row-Level Security (RLS) policies.
Optimization patterns for insert-heavy, update-heavy, and large-scale partitioned workloads.
Comprehensive indexing strategies including B-tree, GIN, GiST, and partial indexes.
0 GitHub stars

Use Cases

Refactoring existing SQL tables to improve query performance and eliminate data redundancy.
Implementing specialized data structures for full-text search, vector embeddings, or JSON semi-structured data.
Designing a scalable relational database schema for a new enterprise application.

About

Key Features

Strict adherence to modern PostgreSQL standards and naming conventions.
Expert data type selection focusing on performance and precision (TIMESTAMPTZ, BIGINT, JSONB).
Implementation of robust constraints and Row-Level Security (RLS) policies.
Optimization patterns for insert-heavy, update-heavy, and large-scale partitioned workloads.
Comprehensive indexing strategies including B-tree, GIN, GiST, and partial indexes.
0 GitHub stars

Use Cases

Refactoring existing SQL tables to improve query performance and eliminate data redundancy.
Implementing specialized data structures for full-text search, vector embeddings, or JSON semi-structured data.
Designing a scalable relational database schema for a new enterprise application.