Why does it suggest avoiding VARCHAR(n)?

PostgreSQL handles the TEXT type efficiently without performance penalties, and the skill recommends using TEXT with CHECK constraints if length limits are strictly necessary.

What primary key type does this skill recommend?

It prefers BIGINT GENERATED ALWAYS AS IDENTITY for standard tables and UUID for distributed systems where global uniqueness is required.

Does it support JSONB and vector types?

Yes, it includes best practices for GIN indexing on JSONB columns and support for pgvector types for similarity searches.

How does this skill handle PostgreSQL performance?

It provides specific patterns for hot/cold column separation, fillfactor adjustments, and automated partitioning strategies for large datasets.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: HermeticOrmus

byHermeticOrmus

数据库管理

Architects optimized PostgreSQL schemas using industry-standard best practices for data integrity and high-performance indexing.

关于

This skill provides specialized guidance for designing PostgreSQL databases, covering everything from 3NF normalization and indexing strategies to advanced features like Row-Level Security (RLS) and partitioning. It helps developers avoid common 'gotchas'—such as unindexed foreign keys or silent coercions—while ensuring the use of modern data types like BIGINT identity columns instead of legacy SERIAL types. Whether you are building time-series tables, JSONB-heavy document stores, or standard relational models, this skill ensures your database architecture is scalable, maintainable, and performant.

主要功能

Implementation patterns for Row-Level Security (RLS) and complex CHECK constraints.
0 GitHub stars
Expert data type selection including TIMESTAMPTZ, NUMERIC, and modern identity columns.
Performance-oriented design for update-heavy and insert-heavy workloads.
Declarative partitioning patterns for large-scale data management.
Optimized indexing strategies for B-tree, GIN, GiST, and BRIN index types.

使用场景

Refactoring legacy schemas to modern PostgreSQL standards for better performance and type safety.
Designing a multi-tenant SaaS database with strict data isolation using Row-Level Security.
Optimizing a time-series logging system using partitioning and BRIN indexing.

关于

主要功能

Implementation patterns for Row-Level Security (RLS) and complex CHECK constraints.
0 GitHub stars
Expert data type selection including TIMESTAMPTZ, NUMERIC, and modern identity columns.
Performance-oriented design for update-heavy and insert-heavy workloads.
Declarative partitioning patterns for large-scale data management.
Optimized indexing strategies for B-tree, GIN, GiST, and BRIN index types.

使用场景

Refactoring legacy schemas to modern PostgreSQL standards for better performance and type safety.
Designing a multi-tenant SaaS database with strict data isolation using Row-Level Security.
Optimizing a time-series logging system using partitioning and BRIN indexing.