database-schema-designer

name	database-schema-designer
description	Design relational database schemas from requirements with normalization, migration planning, ERD generation, RLS policies, index strategies, and type generation. Supports PostgreSQL, MySQL, and SQLite with Drizzle, Prisma, and Alembic migrations. Use when designing new features, reviewing schemas, or adding multi-tenancy.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"engineering","domain":"data-architecture","tier":"POWERFUL","updated":"2026-03-09T00:00:00.000Z","frameworks":"drizzle, prisma, alembic, typeorm"}

Database Schema Designer

Tier: POWERFUL Category: Engineering / Data Architecture Maintainer: Claude Skills Team

Overview

Design normalized relational database schemas from requirements and generate migrations, TypeScript/Python types, seed data, Row-Level Security policies, index strategies, and ERD diagrams. Handles multi-tenancy, soft deletes, audit trails, optimistic locking, polymorphic associations, and temporal data patterns. Supports PostgreSQL, MySQL, and SQLite with Drizzle, Prisma, TypeORM, and Alembic.

Keywords

database schema, schema design, normalization, migration, ERD, row-level security, indexing, multi-tenancy, soft deletes, audit trail, Drizzle, Prisma, PostgreSQL

Core Capabilities

1. Schema Design from Requirements

• Extract entities and relationships from natural language requirements
• Apply normalization rules (1NF through 3NF with denormalization guidance)
• Add cross-cutting concerns: timestamps, soft deletes, audit, versioning
• Generate complete DDL with constraints, defaults, and comments

2. Migration Planning

• Generate forward and rollback migrations
• Plan zero-downtime migrations for large tables
• Handle column additions, type changes, and data backfills
• Support Drizzle, Prisma, TypeORM, Alembic, and raw SQL

3. Index Strategy

• Composite indexes for common query patterns
• Partial indexes for filtered queries (e.g., active records only)
• Covering indexes to eliminate table lookups
• GIN/GiST indexes for full-text search and JSONB
• Index bloat detection and maintenance

4. Type Generation

• TypeScript interfaces and Zod schemas from DB schema
• Python dataclasses and Pydantic models
• Enums as string unions (not database enums for migration safety)

5. Security

• Row-Level Security policies for multi-tenant isolation
• Column-level encryption for PII
• Audit logging with before/after JSON snapshots

When to Use

• Designing tables for a new feature
• Reviewing an existing schema for normalization or performance issues
• Adding multi-tenancy to a single-tenant schema
• Planning a breaking schema migration
• Generating ERD documentation for a service

Schema Design Process

Step 1: Requirements to Entities

Given requirements like:

"Users can create workspaces. Each workspace has projects. Projects contain tasks with assignees, labels, and due dates. We need audit trails and multi-tenant isolation."

Extract entities:

User, Workspace, WorkspaceMember, Project, Task, TaskAssignment,
Label, TaskLabel (junction), AuditLog

Step 2: Identify Relationships

User 1──* WorkspaceMember *──1 Workspace
Workspace 1──* Project
Project 1──* Task
Task *──* User          (via TaskAssignment)
Task *──* Label         (via TaskLabel)
User 1──* AuditLog

Step 3: Add Cross-Cutting Concerns

Every table gets:

• id — CUID2 or UUIDv7 (sortable, non-sequential)
• created_at — TIMESTAMPTZ, server-side default
• updated_at — TIMESTAMPTZ, updated on every write

Tenant-scoped tables additionally get:

• workspace_id — FK to workspaces, included in every query
• RLS policy enforcing workspace isolation

Auditable tables additionally get:

• created_by_id — FK to users
• updated_by_id — FK to users
• deleted_at — TIMESTAMPTZ for soft deletes
• version — INTEGER for optimistic locking

Step 4: Full Schema (Drizzle ORM)

import {
  pgTable, text, timestamp, integer, boolean, uniqueIndex, index, pgEnum
} from 'drizzle-orm/pg-core'
import { createId } from '@paralleldrive/cuid2'

// Enums as pgEnum for type safety, but string columns also acceptable
export const taskStatusEnum = pgEnum('task_status', ['todo', 'in_progress', 'in_review', 'done'])
export const taskPriorityEnum = pgEnum('task_priority', ['low', 'medium', 'high', 'urgent'])
export const memberRoleEnum = pgEnum('member_role', ['owner', 'admin', 'member', 'viewer'])

// ──── WORKSPACES ────
export const workspaces = pgTable('workspaces', {
  id: text('id').primaryKey().$defaultFn(createId),
  name: text('name').notNull(),
  slug: text('slug').notNull(),
  plan: ().().(),
  : (, { :  }).().(),
  : (, { :  }).().(),
},  [
  ().(t.),
])


  users = (, {
  : ().().$defaultFn(createId),
  : ().(),
  : (),
  : (),
  : (),
  : (, { :  }).().(),
  : (, { :  }).().(),
},  [
  ().(t.),
])


  workspaceMembers = (, {
  : ().().$defaultFn(createId),
  : ().().( workspaces., { :  }),
  : ().().( users., { :  }),
  : ().().(),
  : (, { :  }).().(),
},  [
  ().(t., t.),
  ().(t.),
  ().(t.),
])


  projects = (, {
  : ().().$defaultFn(createId),
  : ().().( workspaces., { :  }),
  : ().(),
  : (),
  : ().().(),
  : ().().( users.),
  : ().( users.),
  : ().( users.),
  : (, { :  }).().(),
  : (, { :  }).().(),
  : (, { :  }),
},  [
  ().(t.),
  ().(t., t.),
])


  tasks = (, {
  : ().().$defaultFn(createId),
  : ().().( projects., { :  }),
  : ().(),
  : (),
  : ().().(),
  : ().().(),
  : ().().(),
  : (, { :  }),
  : ().().(),
  : ().().( users.),
  : ().( users.),
  : (, { :  }).().(),
  : (, { :  }).().(),
  : (, { :  }),
},  [
  ().(t.),
  ().(t., t.),
  ().(t.).(sql),
])


  auditLog = (, {
  : ().().$defaultFn(createId),
  : ().().( workspaces.),
  : ().().( users.),
  : ().(), 
  : ().(), 
  : ().(),
  : (), 
  : (),   
  : (),
  : (, { :  }).().(),
},  [
  ().(t.),
  ().(t., t.),
  ().(t.),
  ().(t.),
])

Row-Level Security (PostgreSQL)

-- Enable RLS on tenant-scoped tables
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;
ALTER TABLE tasks ENABLE ROW LEVEL SECURITY;

-- Create application role
CREATE ROLE app_user;

-- Projects: users can only see projects in their workspace
CREATE POLICY projects_workspace_isolation ON projects
  FOR ALL TO app_user
  USING (
    workspace_id IN (
      SELECT wm.workspace_id FROM workspace_members wm
      WHERE wm.user_id = current_setting('app.current_user_id')::text
    )
  );

-- Tasks: access through project's workspace membership
CREATE POLICY tasks_workspace_isolation ON tasks
  FOR ALL TO app_user
  USING (
    project_id IN (
      SELECT p.id FROM projects p
      JOIN workspace_members wm ON wm.workspace_id = p.workspace_id
      WHERE wm.user_id = current_setting('app.current_user_id')::text
    )
  );

-- Soft delete filter: never show deleted records to app users
CREATE POLICY tasks_hide_deleted ON tasks
  FOR SELECT TO app_user
  USING (deleted_at IS NULL);

-- Set user context at request start (in middleware)

Index Strategy Decision Framework

Query Pattern                          → Index Type
─────────────────────────────────────────────────────
WHERE col = value                      → B-tree (default)
WHERE col1 = v1 AND col2 = v2         → Composite B-tree (col1, col2)
WHERE col = value AND deleted_at IS NULL → Partial index with WHERE clause
WHERE col IN (v1, v2, v3)             → B-tree (handles IN efficiently)
WHERE col LIKE 'prefix%'              → B-tree (prefix match only)
WHERE col LIKE '%substring%'          → GIN with pg_trgm extension
WHERE jsonb_col @> '{"key": "val"}'   → GIN on JSONB column
WHERE to_tsvector(col) @@ query       → GIN on tsvector
ORDER BY col DESC LIMIT N             → B-tree DESC
SELECT a, b WHERE a = v               → Covering index INCLUDE(b)

Index Anti-Patterns

Anti-Pattern	Why It Hurts	Fix
Index on every column	Write overhead, storage bloat	Index only queried columns
No index on foreign keys	Slow JOINs and CASCADE deletes	Always index FK columns
Missing partial index for soft deletes	Full table scan on WHERE deleted_at IS NULL	Add WHERE deleted_at IS NULL to index
Composite index in wrong order	Index unused for prefix queries	Put most selective / equality column first
No index maintenance	Bloated indexes, slow queries	Schedule REINDEX CONCURRENTLY

Zero-Downtime Migration Patterns

Adding a NOT NULL Column to a Large Table

-- WRONG: locks table for duration of ALTER
ALTER TABLE tasks ADD COLUMN assignee_id TEXT NOT NULL;

-- RIGHT: three-phase migration
-- Phase 1: Add nullable column (instant, no lock)
ALTER TABLE tasks ADD COLUMN assignee_id TEXT;

-- Phase 2: Backfill in batches (no lock)
UPDATE tasks SET assignee_id = created_by_id
WHERE assignee_id IS NULL AND id > $last_processed_id
LIMIT 10000;
-- Repeat until all rows backfilled

-- Phase 3: Add NOT NULL constraint (brief lock, but validates existing data)
ALTER TABLE tasks ALTER COLUMN assignee_id SET NOT NULL;

Renaming a Column Safely

-- WRONG: ALTER TABLE RENAME COLUMN breaks all running code instantly

-- RIGHT: expand-contract pattern
-- Phase 1: Add new column, write to both
ALTER TABLE tasks ADD COLUMN assignee_user_id TEXT;
-- Deploy code that writes to BOTH old_name and new_name

-- Phase 2: Backfill
UPDATE tasks SET assignee_user_id = old_assignee WHERE assignee_user_id IS NULL;

-- Phase 3: Switch reads to new column
-- Deploy code that reads from new_name only

-- Phase 4: Drop old column (after all deployments use new name)
ALTER TABLE tasks DROP COLUMN old_assignee;

ERD Generation (Mermaid)

erDiagram
    Workspace ||--o{ WorkspaceMember : has
    Workspace ||--o{ Project : contains
    User ||--o{ WorkspaceMember : joins
    User ||--o{ Task : creates
    Project ||--o{ Task : contains
    Task ||--o{ TaskAssignment : has
    Task ||--o{ TaskLabel : has
    Label ||--o{ TaskLabel : tags
    User ||--o{ TaskAssignment : assigned

    Workspace {
        text id PK
        text name
        text slug UK
        text plan
    }
    User {
        text id PK
        text email UK
        text name
    }
    Project {
        text id PK
        text workspace_id FK
        text name
        text status
    }
    Task {
        text id PK
        text project_id FK
        text title
        enum status
        enum priority
        int version
        timestamp deleted_at
    }

Common Pitfalls

• No index on foreign keys — every FK column needs an index for JOIN and CASCADE performance
• Soft deletes without partial index — WHERE deleted_at IS NULL without index causes full table scans
• Sequential integer IDs exposed in URLs — reveals entity count; use CUID2 or UUIDv7 instead
• Adding NOT NULL to a large table — locks the table; use the three-phase pattern above
• Database enums for status fields — altering enums requires migration; use text with CHECK constraint
• No optimistic locking — concurrent updates silently overwrite each other; add a version column
• RLS not tested — always test RLS policies with a non-superuser role in staging
• Missing updated_at trigger — without a trigger, updated_at only updates when application code remembers to set it

Best Practices

1. Timestamps on every table — created_at and updated_at as TIMESTAMPTZ with server defaults
2. Soft deletes for user-facing data — deleted_at instead of hard DELETE for audit and recovery
3. CUID2 or UUIDv7 as primary keys — sortable, non-sequential, globally unique
4. Index every foreign key column — required for JOIN performance and CASCADE operations
5. Partial indexes for filtered queries — WHERE deleted_at IS NULL saves significant scan time
6. RLS over application-level filtering — the database enforces tenancy, not just application code
7. Version column for optimistic locking — WHERE version = $expected_version prevents lost updates
8. Audit log with JSON snapshots — store before/after state for compliance and debugging

Troubleshooting

Problem	Cause	Solution
Migration locks table for minutes	Adding NOT NULL column or index on large table without batching	Use the three-phase migration pattern: add nullable, backfill in batches, then set NOT NULL
RLS policies silently return empty results	current_setting('app.current_user_id') not set before query	Verify middleware calls set_config at the start of every request; add a test that queries as a non-superuser role
Composite index not used by query planner	Columns in the WHERE clause do not match the index prefix order	Reorder index columns so equality predicates come first, then range predicates; run EXPLAIN ANALYZE to confirm
Soft-deleted records appear in API responses	Application queries missing WHERE deleted_at IS NULL filter	Add a default scope or database view that excludes soft-deleted rows; prefer RLS policy for enforcement
Optimistic locking conflicts spike after deploy	New code path writes without incrementing the version column	Audit all UPDATE statements to include SET version = version + 1 and WHERE version = $expected
Foreign key CASCADE deletes are slow	Missing index on the child table's FK column	Add a B-tree index on every FK column; verify with EXPLAIN on a DELETE of the parent row
CUID2/UUIDv7 IDs cause index bloat over time	Text-based IDs are wider than integers, increasing B-tree page splits	Schedule REINDEX CONCURRENTLY during low-traffic windows; monitor pg_stat_user_indexes for bloat ratio

Success Criteria

• Schema passes 3NF validation — no transitive dependencies remain unless documented as intentional denormalization for read performance
• All foreign key columns are indexed — zero FK columns without a corresponding B-tree index, verified via pg_indexes query
• Zero-downtime migrations verified — every migration executes without ACCESS EXCLUSIVE locks exceeding 5 seconds on tables with 100K+ rows
• RLS policies tested with non-superuser role — at least one integration test per tenant-scoped table confirms cross-tenant data isolation
• Type generation matches schema — generated TypeScript interfaces or Pydantic models have zero drift from the current DDL, validated in CI
• Query performance meets SLA — 95th percentile query latency under 50ms for indexed queries on tables up to 10M rows
• Audit log captures all mutations — every INSERT, UPDATE, and DELETE on auditable tables produces a corresponding audit_log entry with before/after snapshots

Scope & Limitations

This skill covers:

• Relational schema design for PostgreSQL, MySQL, and SQLite including normalization through 3NF
• Migration generation and zero-downtime migration planning for Drizzle, Prisma, TypeORM, and Alembic
• Row-Level Security policies, index strategy, and type generation (TypeScript and Python)
• Cross-cutting patterns: multi-tenancy, soft deletes, audit trails, optimistic locking, and temporal data

This skill does NOT cover:

• NoSQL or document database design (MongoDB, DynamoDB, Cassandra) — see senior-data-engineer for broader data store guidance
• Query optimization and execution plan analysis beyond index recommendations — see performance-profiler for runtime profiling
• Database infrastructure provisioning, replication, or failover configuration — see senior-cloud-architect for cloud database setup
• Application-layer ORM patterns, connection pooling, or caching strategies — see senior-backend for backend architecture decisions

Integration Points

Skill	Integration	Data Flow
migration-architect	Hands off generated DDL and migration files for sequencing across services	Schema Designer produces migrations, Migration Architect orchestrates cross-service rollout order
api-design-reviewer	Schema entities map directly to API resource models and endpoint structure	Schema entities and relationships feed into REST/GraphQL resource definitions and validation rules
senior-backend	Generated types and ORM schemas plug into repository and service layers	TypeScript interfaces and Pydantic models from schema become the backend's data access contracts
performance-profiler	Index strategy recommendations are validated against real query execution plans	Schema Designer proposes indexes, Performance Profiler confirms effectiveness with EXPLAIN ANALYZE data
senior-secops	RLS policies and column encryption align with security compliance requirements	Security requirements flow in, RLS policies and encryption specifications flow out for audit verification
observability-designer	Audit log schema provides the foundation for operational dashboards and alerting	Audit log table structure feeds into observability pipelines for change tracking and anomaly detection