Can this skill help with performance tuning for large datasets?

Absolutely. It covers declarative partitioning, BRIN indexes for time-series data, and MVCC-aware design to handle tables with over 100 million rows.

Does this skill support modern PostgreSQL features like UUID v7?

Yes, it provides guidance on using UUID v7 for distributed systems while explaining when to prefer BIGINT identity columns for better performance.

What PostgreSQL-specific 'gotchas' does it address?

It specifically manages identifier casing rules, manual indexing of foreign keys, unique constraint behavior with NULLs, and MVCC storage management.

Does it include security best practices for multi-tenant apps?

Yes, it includes detailed instructions for enabling Row-Level Security (RLS) and creating policies to restrict data access at the database level.

How does it handle JSON and semi-structured data?

The skill recommends JSONB for most use cases and provides specific indexing patterns using GIN to ensure fast containment and existence queries.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: HermeticOrmus

byHermeticOrmus

Gestión de Bases de Datos

Architects production-grade PostgreSQL schemas utilizing optimized data types, advanced indexing, and performance-first design patterns.

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The PostgreSQL Table Design skill provides Claude with deep expertise in relational database architecture specifically for the PostgreSQL ecosystem. It enforces industry best practices such as 3NF normalization, precise data type selection (like TIMESTAMPTZ, BIGINT, and JSONB), and sophisticated indexing strategies including B-tree, GIN, and GiST. The skill helps developers avoid common pitfalls like silent coercions and unindexed foreign keys while providing implementation patterns for advanced features like Row-Level Security (RLS), declarative partitioning for massive datasets, and performance tuning for update-heavy workloads.

Características Principales

Optimized data type selection including identity columns, precise decimals, and binary storage
Advanced indexing guidance for B-tree, GIN, GiST, BRIN, and partial/expression indexes
Comprehensive partitioning strategies (Range, List, Hash) for high-scale data management
Security-focused design featuring Row-Level Security (RLS) and custom domain types
Performance patterns for high-concurrency inserts and update-heavy table structures
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Casos de Uso

Designing a scalable relational schema for a new SaaS application with multi-tenant security requirements
Architecting high-volume time-series or event-log systems using PostgreSQL partitioning and BRIN indexes
Optimizing an existing database to improve query performance and reduce storage bloat through proper indexing

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Características Principales

Optimized data type selection including identity columns, precise decimals, and binary storage
Advanced indexing guidance for B-tree, GIN, GiST, BRIN, and partial/expression indexes
Comprehensive partitioning strategies (Range, List, Hash) for high-scale data management
Security-focused design featuring Row-Level Security (RLS) and custom domain types
Performance patterns for high-concurrency inserts and update-heavy table structures
0 GitHub stars

Casos de Uso

Designing a scalable relational schema for a new SaaS application with multi-tenant security requirements
Architecting high-volume time-series or event-log systems using PostgreSQL partitioning and BRIN indexes
Optimizing an existing database to improve query performance and reduce storage bloat through proper indexing