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database-architect

Design database schemas from query patterns with constraints and reversible migrations.

3|2|Updated Feb 27, 2026
Search Tags:#indexing#migration#query-optimization#data-modeling#schema-design#database-design
One-click install
npx skills add https://github.com/grasberg/sofia --skill database-architect-grasberg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: database-architect
Source: https://github.com/grasberg/sofia/tree/main/workspace/skills/database-architect
Command: npx skills add https://github.com/grasberg/sofia --skill database-architect-grasberg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Database Architect designs schemas that reflect real query patterns and enforce data integrity, enabling scalable, maintainable data stores.

Core Features & Use Cases

  • Phase-driven design process: Requirements gathering, platform selection, schema design, implementation, and verification.
  • Flexible platform guidance: PostgreSQL with pgvector, SQLite, MongoDB, Redis, and other tools to match workload needs.
  • Verification and migration planning: Use EXPLAIN ANALYZE, define reversible migrations, and test against realistic data volumes.

Quick Start

Design a schema plan for your current application and validate critical queries with EXPLAIN ANALYZE.

Frequently Asked Questions about database-architect

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a database schema optimized for real query patterns?▼

Database schema design for real query patterns involves a phase-driven process: gathering requirements, selecting a platform, structuring tables, and validating critical queries using EXPLAIN ANALYZE to ensure performance.

What is the best way to plan reversible database migrations?▼

Planning reversible database migrations involves defining up and down procedures for each schema change, allowing you to safely roll back structural modifications while maintaining data integrity constraints.

When do I need specialized indexing strategies for time-series workloads?▼

Specialized indexing strategies for time-series workloads are needed when standard indexing fails to handle high-volume sequential inserts and time-range queries efficiently across massive, continuously growing data volumes.

Can I use this schema design process for document and time-series databases?▼

Yes, you can apply this schema design process to document and time-series databases, as it includes specific platform guidance for MongoDB and other workloads, adapting normalization and constraints accordingly.

How do I validate query optimization using EXPLAIN ANALYZE?▼

You validate query optimization using EXPLAIN ANALYZE by executing it against your schema with realistic data volumes to inspect execution plans, verify index usage, and identify performance bottlenecks.

Does this database design approach support PostgreSQL with pgvector?▼

Yes, this database design approach supports PostgreSQL with pgvector, providing tailored platform guidance to ensure your schema, constraints, and indexing strategies match specific workload requirements.

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