Can it help with very large datasets?

Yes, it includes detailed patterns for RANGE, LIST, and HASH partitioning to manage tables exceeding 100 million rows.

Does this skill handle PostgreSQL-specific data types?

Yes, it prioritizes Postgres-specific types like TIMESTAMPTZ, BIGINT GENERATED ALWAYS AS IDENTITY, and JSONB over generic SQL types.

How does it improve query performance?

It provides patterns for advanced indexing (GIN, GiST, BRIN), ensures Foreign Keys are manually indexed, and suggests partial or expression indexes for specific query paths.

Does it support modern features like vector search?

Yes, the skill includes support for vector types via pgvector and JSONB for semi-structured data requirements.

PostgreSQL Schema Design

Name: PostgreSQL Schema Design
Author: amurata

byamurata

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Database Management

Architects production-grade PostgreSQL schemas using performance-tuned patterns, advanced data types, and indexing best practices.

About

This skill equips Claude with specialized expertise to design and optimize PostgreSQL databases. It moves beyond basic SQL by enforcing domain-specific best practices such as identity-based primary keys, proper timezone handling with TIMESTAMPTZ, and efficient use of JSONB for semi-structured data. It provides deep guidance on advanced indexing strategies—including GIN, GiST, and BRIN—and covers complex architectural patterns like declarative partitioning, row-level security (RLS), and specialized optimization for update-heavy or insert-heavy workloads. Ideal for developers building scalable, reliable backends that require more than just default database behavior.

Key Features

Declarative partitioning patterns for large-scale time-series or multi-tenant data.
Advanced indexing strategies covering Partial, Expression, GIN, and BRIN indexes.
Optimized data type selection including TIMESTAMPTZ, NUMERIC, and Identity columns.
Performance-focused design for MVCC management and high-concurrency workloads.
Row-Level Security (RLS) and complex constraint implementation (EXCLUDE, CHECK).
3 GitHub stars

Use Cases

Architecting time-series data storage using range partitioning and BRIN indexing.
Designing a scalable relational schema for a high-traffic SaaS application.
Optimizing slow queries by implementing partial indexes or JSONB containment searches.

About

Key Features

Declarative partitioning patterns for large-scale time-series or multi-tenant data.
Advanced indexing strategies covering Partial, Expression, GIN, and BRIN indexes.
Optimized data type selection including TIMESTAMPTZ, NUMERIC, and Identity columns.
Performance-focused design for MVCC management and high-concurrency workloads.
Row-Level Security (RLS) and complex constraint implementation (EXCLUDE, CHECK).
3 GitHub stars

Use Cases

Architecting time-series data storage using range partitioning and BRIN indexing.
Designing a scalable relational schema for a high-traffic SaaS application.
Optimizing slow queries by implementing partial indexes or JSONB containment searches.