Can it help with modern data types like JSONB and Vectors?

Absolutely. It includes best practices for semi-structured data using JSONB and vector similarity search capabilities via pgvector for AI applications.

How does it handle timezone-aware data?

The skill strictly recommends using TIMESTAMPTZ for event time to avoid common pitfalls with local time offsets and ensure consistent temporal data storage.

Does it support database partitioning?

Yes, it provides patterns for RANGE, LIST, and HASH partitioning, which are essential for managing very large datasets exceeding 100M rows.

Does this skill help with performance tuning?

Yes, it provides specific guidance on selecting the right index types (B-tree, GIN, GiST, BRIN), configuring fillfactors, and managing MVCC bloat.

Why does this skill prefer BIGINT over UUID for primary keys?

BIGINT identity columns are generally more performant for indexing and storage. UUIDs are only recommended when global uniqueness or ID opacity is a specific requirement.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: Tahir-yamin

byTahir-yamin

•

Database Management

Designs high-performance PostgreSQL schemas using industry-standard best practices for data types, indexing, and performance.

About

This skill provides comprehensive guidance for architecting production-grade PostgreSQL databases. It automates the selection of optimal data types (like BIGINT identities and TIMESTAMPTZ), enforces 3NF normalization standards, and implements advanced indexing strategies including B-tree, GIN, and GiST. Whether you are building a new schema or optimizing an existing one, this tool helps avoid common pitfalls like silent coercions and unindexed foreign keys while leveraging advanced features such as Row-Level Security, partitioning, and JSONB for semi-structured data.

Key Features

Safe schema evolution patterns using transactional DDL
3 GitHub stars
PostgreSQL-specific feature implementation (RLS, JSONB, pgvector)
Performance-focused partitioning and constraint enforcement
Optimal data type selection (BIGINT identity, TIMESTAMPTZ, TEXT)
Advanced indexing strategies (Partial, Expression, Covering, and GIN)

Use Cases

Designing a scalable relational schema for new application backends
Implementing secure multi-tenant data access with Row-Level Security
Optimizing existing tables to improve query speed and reduce bloat

About

Key Features

Safe schema evolution patterns using transactional DDL
3 GitHub stars
PostgreSQL-specific feature implementation (RLS, JSONB, pgvector)
Performance-focused partitioning and constraint enforcement
Optimal data type selection (BIGINT identity, TIMESTAMPTZ, TEXT)
Advanced indexing strategies (Partial, Expression, Covering, and GIN)

Use Cases

Designing a scalable relational schema for new application backends
Implementing secure multi-tenant data access with Row-Level Security
Optimizing existing tables to improve query speed and reduce bloat