How does this skill handle time-series data?

It recommends using RANGE partitioning, TIMESTAMPTZ for event times, and provides patterns for insert-heavy workloads often found in time-series apps.

Does it help with database security?

Yes, it provides implementation patterns for Row-Level Security (RLS) and policy creation to restrict data access at the engine level.

What primary key strategy does this skill recommend?

It prefers BIGINT GENERATED ALWAYS AS IDENTITY for storage efficiency and performance, recommending UUIDs only when global uniqueness or opacity is required.

Does it support modern PostgreSQL features like vector search?

Yes, it includes guidance for using the vector type via pgvector for AI embeddings and GIN/GiST indexes for specialized search.

Why does it advise against using the VARCHAR type?

In PostgreSQL, TEXT is functionally equivalent to VARCHAR without the performance penalties of length-checking, making it the idiomatic choice for strings unless specific constraints are needed.

PostgreSQL Table Designer

Name: PostgreSQL Table Designer
Author: Activer007

byActiver007

データベース管理

Architects high-performance, production-grade PostgreSQL schemas using modern best practices and idiomatic data patterns.

概要

The PostgreSQL Table Design skill provides Claude with specialized expertise in building robust database architectures specifically for the PostgreSQL ecosystem. It moves beyond generic SQL to implement advanced features such as GIN/GiST indexing, declarative partitioning, identity columns, and Row-Level Security (RLS). Whether you are designing a normalized 3NF schema for a new application, optimizing for insert-heavy workloads, or integrating vector search with pgvector, this skill ensures your database is scalable, secure, and performant while avoiding common pitfalls like silent coercions and unindexed foreign keys.

主な機能

Advanced indexing strategies covering B-tree, GIN, GiST, BRIN, and partial indexes.
Modern schema evolution guidance using transactional DDL and concurrent indexing.
Optimized data type selection including BIGINT identity, TIMESTAMPTZ, and JSONB.
0 GitHub stars
Performance-tuned patterns for update-heavy vs. insert-heavy database workloads.
Implementation of complex constraints, Exclusion rules, and Row-Level Security policies.

ユースケース

Refactoring legacy database schemas to utilize modern PostgreSQL features like declarative partitioning.
Optimizing database performance for AI-driven applications using vector embeddings and similarity search.
Designing a scalable relational schema for SaaS applications requiring high data integrity.

概要

主な機能

Advanced indexing strategies covering B-tree, GIN, GiST, BRIN, and partial indexes.
Modern schema evolution guidance using transactional DDL and concurrent indexing.
Optimized data type selection including BIGINT identity, TIMESTAMPTZ, and JSONB.
0 GitHub stars
Performance-tuned patterns for update-heavy vs. insert-heavy database workloads.
Implementation of complex constraints, Exclusion rules, and Row-Level Security policies.

ユースケース

Refactoring legacy database schemas to utilize modern PostgreSQL features like declarative partitioning.
Optimizing database performance for AI-driven applications using vector embeddings and similarity search.
Designing a scalable relational schema for SaaS applications requiring high data integrity.