Does it include security best practices?

Yes, it covers Row-Level Security (RLS) and the correct application of PK, FK, and UNIQUE constraints to ensure data integrity and security.

Can it help improve database performance?

Absolutely. It includes rules for indexing access paths, partitioning large tables, and using appropriate table types like UNLOGGED for staging data.

Is this skill suitable for high-scale applications?

Yes, it includes advanced strategies for partitioning (Range, List, Hash) and insert-heavy workload optimizations suitable for enterprise scale.

Why does it prefer TEXT over VARCHAR(n)?

PostgreSQL handles both similarly under the hood; using TEXT with CHECK constraints provides more flexibility for schema evolution without performance penalties.

Does this skill support advanced PostgreSQL types?

Yes, it provides specific guidance on JSONB, Arrays, Range types, Network types, and Vector types for AI similarity searches.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: EngineerWithAI

byEngineerWithAI

Database Management

Architects optimized, production-grade PostgreSQL schemas using industry-standard best practices and performance patterns.

About

This skill empowers Claude to design robust PostgreSQL databases by applying professional schema design principles. It provides comprehensive guidance on selecting the most efficient data types, implementing advanced indexing strategies like GIN and GiST, and enforcing data integrity through constraints. Whether you are building a new application or refactoring an existing database, this skill ensures your tables are normalized for consistency, partitioned for scale, and optimized for specific workloads like high-concurrency inserts or complex analytical queries.

Key Features

Advanced indexing strategies including partial, covering, and expression indexes
0 GitHub stars
Expert schema normalization and 3NF architecture guidance
PostgreSQL-specific data type optimization and 'gotcha' avoidance
Security implementation through Row-Level Security (RLS) and constraints
Scalable partitioning strategies for large-scale time-series or multi-tenant data

Use Cases

Implementing complex data requirements using JSONB, arrays, or range types
Optimizing existing database structures to reduce storage bloat and improve query speed
Designing high-performance relational schemas for new web applications

About

Key Features

Advanced indexing strategies including partial, covering, and expression indexes
0 GitHub stars
Expert schema normalization and 3NF architecture guidance
PostgreSQL-specific data type optimization and 'gotcha' avoidance
Security implementation through Row-Level Security (RLS) and constraints
Scalable partitioning strategies for large-scale time-series or multi-tenant data

Use Cases

Implementing complex data requirements using JSONB, arrays, or range types
Optimizing existing database structures to reduce storage bloat and improve query speed
Designing high-performance relational schemas for new web applications