ai-schema

name	ai-schema
description	Use when designing schemas, writing migrations, optimizing queries, or managing data lifecycle across PostgreSQL, MySQL, SQLite, and MongoDB.
effort	max
argument-hint	design|migrate|optimize|lifecycle
tags	["database","sql","migration","schema","optimization","enterprise"]

Database Engineering

Schema design, safe migration generation, query optimization, and data lifecycle management. Multi-DB: PostgreSQL, MySQL, SQLite, MongoDB. Multi-ORM: SQLAlchemy, Prisma, TypeORM, Drizzle, Entity Framework, Diesel.

When to Use

• Designing or modifying database schemas.
• Planning safe migrations with rollback.
• Optimizing slow queries.
• Defining retention policies or archival strategies.
• NOT for infrastructure provisioning -- use /ai-infra.

Modes

design -- Schema Design

1. Analyze data model -- entities, relationships, access patterns, data volume, growth projections.
2. Apply normalization -- 3NF+ by default. Document denormalization decisions with rationale.
3. Design schema -- tables, indexes, constraints, partitioning for large tables.
4. Validate referential integrity -- every FK has a matching PK, cascade rules defined.
5. Output: DDL script + entity relationship description.

migrate -- Safe Migrations

1. Assess impact -- locking impact, backward compatibility, data volume affected.
2. Use expand-contract -- for breaking changes (add new, migrate data, drop old).
3. Generate forward migration -- with explicit transaction boundaries.
4. Generate rollback migration -- ALWAYS required. No migration ships without rollback.
5. Test migration -- verify on representative data volume.
6. Output: forward script, rollback script, execution plan.

optimize -- Query Optimization

1. Analyze execution plan -- EXPLAIN ANALYZE (PostgreSQL), EXPLAIN (MySQL).
2. Identify bottlenecks -- sequential scans, missing indexes, N+1 patterns.
3. Recommend indexes -- composite indexes based on query patterns, partial indexes for filtered queries.
4. Connection pool tuning -- pool size, timeout, idle connection management.
5. Output: optimized query, index recommendations, before/after execution plan.

lifecycle -- Data Lifecycle

1. Retention policies -- define per-table retention based on regulatory requirements.
2. Archival strategies -- partition-based archival, cold storage migration.
3. GDPR compliance -- right to erasure procedures, data anonymization.
4. Multi-DB architecture -- read replicas, caching layers, write distribution.
5. Output: lifecycle policy document, archival procedures.

Quick Reference

/ai-schema design           # schema design with normalization
/ai-schema migrate          # safe migration with rollback
/ai-schema optimize         # query optimization with EXPLAIN
/ai-schema lifecycle        # retention and archival policies

Common Mistakes

• Shipping migrations without rollback scripts -- always generate both.
• Adding indexes without checking write impact -- indexes speed reads but slow writes.
• Denormalizing without documenting why -- future developers will re-normalize.
• Running DDL without --dry-run first -- destructive DDL requires explicit user approval.

Integration

• Migration files integrate with ORM migration systems (Alembic, Prisma Migrate, EF Migrations).
• Schema changes trigger /ai-security for injection pattern review.
• Destructive DDL (DROP, TRUNCATE) requires explicit user approval.

References

• .ai-engineering/manifest.yml -- governance rules for destructive operations. $ARGUMENTS