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name: data-modeling description: Data modeling - entities, relationships, schemas. Use when designing data structures.

Data Modeling Guideline

Tech Stack

• API: tRPC
• Framework: Next.js (with Turbopack)
• Database: Neon (Postgres)
• ORM: Drizzle

Non-Negotiables

• All authorization must be server-enforced (no client-trust)
• Platform is source of truth — third-party services sync FROM platform
• UI must never contradict server-truth
• High-value mutations must have audit trail (who/when/why/before/after)

Context

Data modeling is conceptual — entities, relationships, domain boundaries. Physical implementation (indexes, migrations, query performance) lives in database.

Consider: what are the real-world entities? How do they relate? What invariants must hold? What will break when requirements change?

Driving Questions

• If we were designing this from scratch, what would be different?
• Where will the current model break as the product scales?
• What implicit assumptions are waiting to cause bugs?
• Where is complexity hiding that makes the system hard to reason about?
• What domain boundaries are we violating?