| 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
- Analyze data model -- entities, relationships, access patterns, data volume, growth projections.
- Apply normalization -- 3NF+ by default. Document denormalization decisions with rationale.
- Design schema -- tables, indexes, constraints, partitioning for large tables.
- Validate referential integrity -- every FK has a matching PK, cascade rules defined.
- Output: DDL script + entity relationship description.
migrate -- Safe Migrations
- Assess impact -- locking impact, backward compatibility, data volume affected.
- Use expand-contract -- for breaking changes (add new, migrate data, drop old).
- Generate forward migration -- with explicit transaction boundaries.
- Generate rollback migration -- ALWAYS required. No migration ships without rollback.
- Test migration -- verify on representative data volume.
- Output: forward script, rollback script, execution plan.
optimize -- Query Optimization
- Analyze execution plan --
EXPLAIN ANALYZE (PostgreSQL), EXPLAIN (MySQL).
- Identify bottlenecks -- sequential scans, missing indexes, N+1 patterns.
- Recommend indexes -- composite indexes based on query patterns, partial indexes for filtered queries.
- Connection pool tuning -- pool size, timeout, idle connection management.
- Output: optimized query, index recommendations, before/after execution plan.
lifecycle -- Data Lifecycle
- Retention policies -- define per-table retention based on regulatory requirements.
- Archival strategies -- partition-based archival, cold storage migration.
- GDPR compliance -- right to erasure procedures, data anonymization.
- Multi-DB architecture -- read replicas, caching layers, write distribution.
- 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
