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