The problem
AI agents often generate Prisma schemas that work in development but cause catastrophic failures in production. Standard LLM outputs frequently miss critical database constraints, lead to N+1 query performance death spirals, or trigger the dreaded "migration reset" that wipes production data.
What it does
- Enforces a 12-point anti-pattern scan to catch missing unique constraints, unsafe cascading deletes, and unbounded text fields before they hit your database.
- Implements a 9-point pre-migration checklist to ensure backups exist and destructive operations are blocked.
- Generates production-grade patterns for multi-tenancy, audit trails, and soft deletes that actually work under load.
- Forces optimistic locking and atomic operations to prevent data corruption from concurrent writes.
Frameworks & tools
Prisma ORM, TypeScript, PostgreSQL, MySQL, SQL Server, and SQLite. Optimized for agents using Claude Code and Cursor.
Why this beats prompting it yourself
Generic prompts don't account for Prisma-specific footguns like migration dev resets or the lack of automatic foreign key indexing. This skill provides a specialized safety layer that turns a "compilable" schema into a "production-ready" one by checking for race conditions and data retention risks that basic LLM training ignores.
Use cases
- Designing a multi-tenant database schema with strict tenant isolation.
- Refactoring existing Prisma models to optimize query performance and fix N+1 issues.
- Implementing a safe soft-delete system that automatically filters records at the client level.
- Conducting a pre-deployment audit of a schema migration to prevent data loss.
Known limitations
This tool focuses on schema architecture and safety rather than basic CRUD logic or non-Prisma ORMs. It requires the developer to handle the final migration execution manually for maximum safety.