Can this skill help optimize high-volume write operations?

Yes, it includes strategies for insert-heavy workloads, such as using UNLOGGED tables for staging, managing fillfactor for hot updates, and proper index management.

What is the recommended way to handle time and dates?

The skill strictly enforces the use of TIMESTAMPTZ for event times to ensure time zone awareness and avoid common pitfalls associated with the standard TIMESTAMP type.

Does this skill handle modern PostgreSQL features like JSONB and Vector types?

Yes, it provides specific guidance on using JSONB for semi-structured data and includes support for pgvector for managing AI-driven vector embeddings.

How does the skill help with database security?

It provides implementation patterns for Row-Level Security (RLS) and ensures that schemas use proper constraints and data types to prevent common integrity issues.

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

BIGINT GENERATED ALWAYS AS IDENTITY is generally more storage-efficient and offers better indexing performance. UUIDs are suggested only when global uniqueness or opacity is required for distributed systems.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: HermeticOrmus

byHermeticOrmus

Gestión de Bases de Datos

Architects production-grade PostgreSQL schemas using expert-level best practices for data integrity, performance, and scalability.

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The PostgreSQL Table Design skill transforms Claude into a database architect, providing rigorous guidance on schema creation, indexing strategies, and data modeling. It enforces modern PostgreSQL standards, such as using identity columns over serial types and TIMESTAMPTZ for global temporal accuracy. From designing complex partitioning schemes for high-volume time-series data to implementing Row-Level Security (RLS) for multi-tenant applications, this skill ensures your database is optimized for both read-heavy and write-heavy workloads while maintaining strict data integrity.

Características Principales

0 GitHub stars
Advanced indexing guidance covering B-tree, GIN, GiST, and partial indexes for optimized query paths.
Partitioning strategies (Range, List, Hash) for managing large-scale datasets and time-series logs.
Performance tuning for specific workloads like update-heavy tables and bulk insert operations.
Implementation of Row-Level Security (RLS) and complex constraints for robust data isolation.
Expert data type selection including JSONB, pgvector, and specific numeric types for financial accuracy.

Casos de Uso

Architecting high-performance time-series or logging systems using declarative partitioning and BRIN indexes.
Designing a scalable relational schema for high-traffic SaaS applications with multi-tenancy requirements.
Optimizing legacy database schemas to leverage modern PostgreSQL features like identity columns and improved JSONB indexing.

Acerca de

Características Principales

0 GitHub stars
Advanced indexing guidance covering B-tree, GIN, GiST, and partial indexes for optimized query paths.
Partitioning strategies (Range, List, Hash) for managing large-scale datasets and time-series logs.
Performance tuning for specific workloads like update-heavy tables and bulk insert operations.
Implementation of Row-Level Security (RLS) and complex constraints for robust data isolation.
Expert data type selection including JSONB, pgvector, and specific numeric types for financial accuracy.

Casos de Uso

Architecting high-performance time-series or logging systems using declarative partitioning and BRIN indexes.
Designing a scalable relational schema for high-traffic SaaS applications with multi-tenancy requirements.
Optimizing legacy database schemas to leverage modern PostgreSQL features like identity columns and improved JSONB indexing.