Can it handle modern PostgreSQL features like JSONB and Vectors?

Absolutely. It includes best practices for using JSONB for semi-structured data and vector types for AI embeddings, including appropriate indexing for both.

How does it handle database security?

The skill provides patterns for implementing Row-Level Security (RLS) and custom policies, ensuring data access is controlled directly at the database level.

Does this skill help with database performance tuning?

Yes, it provides specific guidance on selecting the right index types (B-tree, GIN, GiST, BRIN), partitioning large tables, and optimizing for update-heavy or insert-heavy workloads.

Does it follow standard SQL naming conventions?

Yes, it enforces PostgreSQL-specific conventions like snake_case for identifiers to avoid the common pitfalls associated with mixed-case or quoted names.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: GaitanS

byGaitanS

数据库管理

Designs high-performance, production-ready PostgreSQL schemas using industry best practices and advanced database features.

关于

The PostgreSQL Table Design skill empowers Claude to architect robust and scalable database schemas by applying rigorous normalization principles and optimized indexing strategies. It provides domain-specific guidance on PostgreSQL-specific nuances, such as MVCC management, transactional DDL, and Row-Level Security, ensuring that generated structures are performant across various workloads. Whether building a new application or optimizing an existing system, this skill helps users select precise data types, implement complex constraints, and leverage advanced features like partitioning and JSONB to create maintainable, enterprise-grade databases.

主要功能

Advanced data type selection including JSONB, vector types, and range types.
0 GitHub stars
Implementation of Row-Level Security (RLS) and complex CHECK constraints.
Automated schema normalization (3NF) and optimized indexing strategy selection.
Safe schema evolution patterns using transactional DDL and concurrent indexing.
Database partitioning strategies (Range, List, Hash) for high-scale data handling.

使用场景

Optimizing slow database performance by implementing partial indexes and partitioning.
Architecting a new SaaS backend with complex relational data and row-level security.
Designing specialized storage for time-series data or AI-driven vector embeddings.

关于

主要功能

Advanced data type selection including JSONB, vector types, and range types.
0 GitHub stars
Implementation of Row-Level Security (RLS) and complex CHECK constraints.
Automated schema normalization (3NF) and optimized indexing strategy selection.
Safe schema evolution patterns using transactional DDL and concurrent indexing.
Database partitioning strategies (Range, List, Hash) for high-scale data handling.

使用场景

Optimizing slow database performance by implementing partial indexes and partitioning.
Architecting a new SaaS backend with complex relational data and row-level security.
Designing specialized storage for time-series data or AI-driven vector embeddings.