Why use this skill instead of basic SQL generation?

This skill provides deep domain-specific knowledge of PostgreSQL-specific 'gotchas,' such as unindexed foreign keys and heap storage behavior, that general models often miss.

Does this skill support PostgreSQL 15+ features?

Yes, it includes support for modern features like NULLS NOT DISTINCT and GENERATED ALWAYS AS IDENTITY for primary keys.

How does it handle database security?

It implements industry-standard security through Row-Level Security (RLS) policies and robust CHECK/FOREIGN KEY constraints.

Can it help with database performance tuning?

Absolutely. It provides specific guidance on efficient indexing (GIN, GiST, BRIN) and partitioning for datasets exceeding 100 million rows.

Does it support vector databases for AI applications?

Yes, it specifically includes support for the pgvector extension to handle vector similarity searches for embeddings.

PostgreSQL Schema & Table Design

Name: PostgreSQL Schema & Table Design
Author: HermeticOrmus

byHermeticOrmus

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Gestión de Bases de Datos

Optimizes PostgreSQL database architecture using professional best practices, advanced data typing, and high-performance indexing strategies.

Acerca de

This skill empowers Claude to design production-ready PostgreSQL schemas by enforcing strict normalization rules, selecting optimal data types, and implementing robust indexing patterns. It covers advanced PostgreSQL features such as Row-Level Security (RLS), declarative partitioning, JSONB optimization, and specialized indexing (GiST/GIN) for complex data types like geometric or full-text search. Ideal for developers building scalable backends, this skill ensures data integrity through precise constraints and improves query performance by avoiding common pitfalls like unindexed foreign keys or inefficient storage strategies.

Características Principales

Implementation of Row-Level Security (RLS) for multi-tenant data isolation
Expert normalization to 3NF with strategic denormalization patterns
2 GitHub stars
Advanced indexing strategies including B-tree, GIN, GiST, and BRIN
Scalable partitioning strategies for high-volume time-series or event data
Modern PostgreSQL data type selection including pgvector and JSONB

Casos de Uso

Designing a scalable backend for a SaaS application with multi-tenant data isolation
Refactoring legacy SQL schemas to utilize modern PostgreSQL identity columns and TIMESTAMPTZ
Optimizing read/write performance for high-traffic analytical or event-log datasets

Acerca de

Características Principales

Implementation of Row-Level Security (RLS) for multi-tenant data isolation
Expert normalization to 3NF with strategic denormalization patterns
2 GitHub stars
Advanced indexing strategies including B-tree, GIN, GiST, and BRIN
Scalable partitioning strategies for high-volume time-series or event data
Modern PostgreSQL data type selection including pgvector and JSONB

Casos de Uso

Designing a scalable backend for a SaaS application with multi-tenant data isolation
Refactoring legacy SQL schemas to utilize modern PostgreSQL identity columns and TIMESTAMPTZ
Optimizing read/write performance for high-traffic analytical or event-log datasets