---

name: database-schema description: Schema awareness - read before coding, type generation, prevent column errors when-to-use: Before writing any database queries or modifying data models user-invocable: false paths: ["/schema.*", "/migrations/", "/models/", "/*.prisma", "/drizzle/"] effort: medium

Database Schema Awareness Skill

Problem: Claude forgets schema details mid-session - wrong column names, missing fields, incorrect types. TDD catches this at runtime, but we can prevent it earlier.

---

Core Rule: Read Schema Before Writing Database Code

MANDATORY: Before writing ANY code that touches the database:

┌─────────────────────────────────────────────────────────────┐
│  1. READ the schema file (see locations below)              │
│  2. VERIFY columns/types you're about to use exist          │
│  3. REFERENCE schema in your response when writing queries  │
│  4. TYPE-CHECK using generated types (Drizzle/Prisma/etc)   │
└─────────────────────────────────────────────────────────────┘

If schema file doesn't exist → CREATE IT before proceeding.

---

Schema File Locations (By Stack)

Stack	Schema Location	Type Generation
Drizzle	src/db/schema.ts or drizzle/schema.ts	Built-in TypeScript
Prisma	prisma/schema.prisma	npx prisma generate
Supabase	supabase/migrations/*.sql + types	supabase gen types typescript
SQLAlchemy	app/models/*.py or src/models.py	Pydantic models
TypeORM	src/entities/*.ts	Decorators = types
Raw SQL	schema.sql or migrations/	Manual types required

Schema Reference File (Recommended)

Create _project_specs/schema-reference.md for quick lookup:

# Database Schema Reference

*Auto-generated or manually maintained. Claude: READ THIS before database work.*

## Tables

### users
| Column | Type | Nullable | Default | Notes |
|--------|------|----------|---------|-------|
| id | uuid | NO | gen_random_uuid() | PK |
| email | text | NO | - | Unique |
| name | text | YES | - | Display name |
| created_at | timestamptz | NO | now() | - |
| updated_at | timestamptz | NO | now() | - |

### orders
| Column | Type | Nullable | Default | Notes |
|--------|------|----------|---------|-------|
| id | uuid | NO | gen_random_uuid() | PK |
| user_id | uuid | NO | - | FK → users.id |
| status | text | NO | 'pending' | enum: pending/paid/shipped/delivered |
| total_cents | integer | NO | - | Amount in cents |
| created_at | timestamptz | NO | now() | - |

## Relationships
- users 1:N orders (user_id)

## Enums
- order_status: pending, paid, shipped, delivered

---

Pre-Code Checklist (Database Work)

Before writing any database code, Claude MUST:

### Schema Verification Checklist
- [ ] Read schema file: `[path to schema]`
- [ ] Columns I'm using exist: [list columns]
- [ ] Types match my code: [list type mappings]
- [ ] Relationships are correct: [list FKs]
- [ ] Nullable fields handled: [list nullable columns]

Example in practice:

### Schema Verification for TODO-042 (Add order history endpoint)

- [x] Read schema: `src/db/schema.ts`
- [x] Columns exist: orders.id, orders.user_id, orders.status, orders.total_cents, orders.created_at
- [x] Types: id=uuid→string, total_cents=integer→number, status=text→OrderStatus enum
- [x] Relationships: orders.user_id → users.id (many-to-one)
- [x] Nullable: none of these columns are nullable

---

Type Generation Commands

Drizzle (TypeScript)

// Schema defines types automatically
// src/db/schema.ts
import { pgTable, uuid, text, integer, timestamp } from 'drizzle-orm/pg-core';

export const users = pgTable('users', {
  id: uuid('id').primaryKey().defaultRandom(),
  email: text('email').notNull().unique(),
  name: text('name'),
  createdAt: timestamp('created_at').notNull().defaultNow(),
});

export const orders = pgTable('orders', {
  id: uuid('id').primaryKey().defaultRandom(),
  userId: uuid('user_id').notNull().references(() => users.id),
  status: text('status').notNull().default('pending'),
  totalCents: integer('total_cents').notNull(),
  createdAt: timestamp('created_at').notNull().defaultNow(),
});

// Inferred types - USE THESE
export type User = typeof users.$inferSelect;
export type NewUser = typeof users.$inferInsert;
export type Order = typeof orders.$inferSelect;
export type NewOrder = typeof orders.$inferInsert;

Prisma

// prisma/schema.prisma
model User {
  id        String   @id @default(uuid())
  email     String   @unique
  name      String?
  orders    Order[]
  createdAt DateTime @default(now()) @map("created_at")

  @@map("users")
}

model Order {
  id         String   @id @default(uuid())
  userId     String   @map("user_id")
  user       User     @relation(fields: [userId], references: [id])
  status     String   @default("pending")
  totalCents Int      @map("total_cents")
  createdAt  DateTime @default(now()) @map("created_at")

  @@map("orders")
}

# Generate types after schema changes
npx prisma generate

Supabase

# Generate TypeScript types from live database
supabase gen types typescript --local > src/types/database.ts

# Or from remote
supabase gen types typescript --project-id your-project-id > src/types/database.ts

// Use generated types
import { Database } from '@/types/database';

type User = Database['public']['Tables']['users']['Row'];
type NewUser = Database['public']['Tables']['users']['Insert'];
type Order = Database['public']['Tables']['orders']['Row'];

SQLAlchemy (Python)

# app/models/user.py
from sqlalchemy import Column, String, DateTime
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.sql import func
from app.db import Base
import uuid

class User(Base):
    __tablename__ = "users"

    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    email = Column(String, nullable=False, unique=True)
    name = Column(String, nullable=True)
    created_at = Column(DateTime(timezone=True), server_default=func.now())

    # Relationships
    orders = relationship("Order", back_populates="user")

# app/schemas/user.py - Pydantic for API validation
from pydantic import BaseModel, EmailStr
from uuid import UUID
from datetime import datetime

class UserBase(BaseModel):
    email: EmailStr
    name: str | None = None

class UserCreate(UserBase):
    pass

class User(UserBase):
    id: UUID
    created_at: datetime

    class Config:
        from_attributes = True

---

Schema-Aware TDD Workflow

Extend the standard TDD workflow for database work:

┌─────────────────────────────────────────────────────────────┐
│  0. SCHEMA: Read and verify schema before anything else     │
│     └─ Read schema file                                     │
│     └─ Complete Schema Verification Checklist               │
│     └─ Note any missing columns/tables needed               │
├─────────────────────────────────────────────────────────────┤
│  1. RED: Write tests that use correct column names          │
│     └─ Import generated types                               │
│     └─ Use type-safe queries in tests                       │
│     └─ Tests should fail on logic, NOT schema errors        │
├─────────────────────────────────────────────────────────────┤
│  2. GREEN: Implement with type-safe queries                 │
│     └─ Use ORM types, not raw strings                       │
│     └─ TypeScript/mypy catches column mismatches            │
├─────────────────────────────────────────────────────────────┤
│  3. VALIDATE: Type check catches schema drift               │
│     └─ tsc --noEmit / mypy catches wrong columns            │
│     └─ Tests validate runtime behavior                      │
└─────────────────────────────────────────────────────────────┘

---

Common Schema Mistakes (And How to Prevent)

Mistake	Example	Prevention
Wrong column name	user.userName vs user.name	Read schema, use generated types
Wrong type	totalCents as string	Type generation catches this
Missing nullable check	user.name! when nullable	Schema shows nullable fields
Wrong FK relationship	order.userId vs order.user_id	Check schema column names
Missing column	Using user.avatar that doesn't exist	Read schema before coding
Wrong enum value	status: 'complete' vs 'completed'	Document enums in schema reference

Type-Safe Query Examples

Drizzle (catches errors at compile time):

// ✅ Correct - uses schema-defined columns
const user = await db.select().from(users).where(eq(users.email, email));

// ❌ Wrong - TypeScript error: 'userName' doesn't exist
const user = await db.select().from(users).where(eq(users.userName, email));

Prisma (catches errors at compile time):

// ✅ Correct
const user = await prisma.user.findUnique({ where: { email } });

// ❌ Wrong - TypeScript error
const user = await prisma.user.findUnique({ where: { userName: email } });

Raw SQL (NO protection - avoid):

// ❌ Dangerous - no type checking, easy to get wrong
const result = await db.query('SELECT * FROM users WHERE user_name = $1', [email]);
// Should be 'email' not 'user_name' - won't catch until runtime

---

Migration Workflow

When schema changes are needed:

┌─────────────────────────────────────────────────────────────┐
│  1. Update schema file (Drizzle/Prisma/SQLAlchemy)          │
├─────────────────────────────────────────────────────────────┤
│  2. Generate migration                                       │
│     └─ Drizzle: npx drizzle-kit generate                    │
│     └─ Prisma: npx prisma migrate dev --name add_column     │
│     └─ Supabase: supabase migration new add_column          │
├─────────────────────────────────────────────────────────────┤
│  3. Regenerate types                                         │
│     └─ Prisma: npx prisma generate                          │
│     └─ Supabase: supabase gen types typescript              │
├─────────────────────────────────────────────────────────────┤
│  4. Update schema-reference.md                               │
├─────────────────────────────────────────────────────────────┤
│  5. Run type check - find all broken code                    │
│     └─ npm run typecheck                                    │
├─────────────────────────────────────────────────────────────┤
│  6. Fix type errors, update tests, run full validation       │
└─────────────────────────────────────────────────────────────┘

---

Session Start Protocol

When starting a session that involves database work:

1. Read schema file immediately
2. Read _project_specs/schema-reference.md if exists
3. Note in session state what tables/columns are relevant
4. Reference schema explicitly when writing code

Session state example:

## Current Session - Database Context

**Schema read:** ✓ src/db/schema.ts
**Tables in scope:** users, orders, order_items
**Key columns:**
- users: id, email, name, created_at
- orders: id, user_id, status, total_cents
- order_items: id, order_id, product_id, quantity, price_cents

---

Anti-Patterns

• ❌ Guessing column names - Always read schema first
• ❌ Using raw SQL strings - Use ORM with type generation
• ❌ Hardcoding without verification - Check schema before using any column
• ❌ Ignoring type errors - Schema drift shows up as type errors
• ❌ Not regenerating types - After migration, always regenerate
• ❌ Assuming nullable - Check schema for nullable columns

---

Checklist

Setup

• Schema file exists in standard location
• Type generation configured
• _project_specs/schema-reference.md created
• Types regenerate on schema change

Per-Task

• Schema read before writing database code
• Schema Verification Checklist completed
• Using generated types (not raw strings)
• Type check passes (catches column errors)
• Tests use correct schema