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database-schema-designer

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Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint des

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Database Schema Designer

Design production-ready database schemas with best practices built-in.


Quick Start

Just describe your data model:

design a schema for an e-commerce platform with users, products, orders

You'll get a complete SQL schema like:

CREATE TABLE users (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE orders (
  id BIGINT AUTO_INCREMENT PRIMARY KEY,
  user_id BIGINT NOT NULL REFERENCES users(id),
  total DECIMAL(10,2) NOT NULL,
  INDEX idx_orders_user (user_id)
);

What to include in your request:

  • Entities (users, products, orders)
  • Key relationships (users have orders, orders have items)
  • Scale hints (high-traffic, millions of records)
  • Database preference (SQL/NoSQL) - defaults to SQL if not specified

Triggers

Trigger Example
design schema "design a schema for user authentication"
database design "database design for multi-tenant SaaS"
create tables "create tables for a blog system"
schema for "schema for inventory management"
model data "model data for real-time analytics"
I need a database "I need a database for tracking orders"
design NoSQL "design NoSQL schema for product catalog"

Key Terms

Term Definition
Normalization Organizing data to reduce redundancy (1NF → 2NF → 3NF)
3NF Third Normal Form - no transitive dependencies between columns
OLTP Online Transaction Processing - write-heavy, needs normalization
OLAP Online Analytical Processing - read-heavy, benefits from denormalization
Foreign Key (FK) Column that references another table's primary key
Index Data structure that speeds up queries (at cost of slower writes)
Access Pattern How your app reads/writes data (queries, joins, filters)
Denormalization Intentionally duplicating data to speed up reads

Quick Reference

Task Approach Key Consideration
New schema Normalize to 3NF first Domain modeling over UI
SQL vs NoSQL Access patterns decide Read/write ratio matters
Primary keys INT or UUID UUID for distributed systems
Foreign keys Always constrain ON DELETE strategy critical
Indexes FKs + WHERE columns Column order matters
Migrations Always reversible Backward compatible first

Process Overview

Your Data Requirements
    |
    v
+-----------------------------------------------------+
| Phase 1: ANALYSIS                                   |
| * Identify entities and relationships               |
| * Determine access patterns (read vs write heavy)   |
| * Choose SQL or NoSQL based on requirements         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 2: DESIGN                                     |
| * Normalize to 3NF (SQL) or embed/reference (NoSQL) |
| * Define primary keys and foreign keys              |
| * Choose appropriate data types                     |
| * Add constraints (UNIQUE, CHECK, NOT NULL)         |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 3: OPTIMIZE                                   |
| * Plan indexing strategy                            |
| * Consider denormalization for read-heavy queries   |
| * Add timestamps (created_at, updated_at)           |
+-----------------------------------------------------+
    |
    v
+-----------------------------------------------------+
| Phase 4: MIGRATE                                    |
| * Generate migration scripts (up + down)            |
| * Ensure backward compatibility                     |
| * Plan zero-downtime deployment                     |
+-----------------------------------------------------+
    |
    v
Production-Ready Schema

Commands

Command When to Use Action
design schema for {domain} Starting fresh Full schema generation
normalize {table} Fixing existing table Apply normalization rules
add indexes for {table} Performance issues Generate index strategy
migration for {change} Schema evolution Create reversible migration
review schema Code review Audit existing schema

Workflow: Start with design schema → iterate with normalize → optimize with add indexes → evolve with migration


Core Principles

Principle WHY Implementation
Model the Domain UI changes, domain doesn't Entity names reflect business concepts
Data Integrity First Corruption is costly to fix Constraints at database level
Optimize for Access Pattern Can't optimize for both OLTP: normalized, OLAP: denormalized
Plan for Scale Retrofitting is painful Index strategy + partitioning plan

Anti-Patterns

Avoid Why Instead
VARCHAR(255) everywhere Wastes storage, hides intent Size appropriately per field
FLOAT for money Rounding errors DECIMAL(10,2)
Missing FK constraints Orphaned data Always define foreign keys
No indexes on FKs Slow JOINs Index every foreign key
Storing dates as strings Can't compare/sort DATE, TIMESTAMP types
SELECT * in queries Fetches unnecessary data Explicit column lists
Non-reversible migrations Can't rollback Always write DOWN migration
Adding NOT NULL without default Breaks existing rows Add nullable, backfill, then constrain

Verification Checklist

After designing a schema:

  • Every table has a primary key
  • All relationships have foreign key constraints
  • ON DELETE strategy defined for each FK
  • Indexes exist on all foreign keys
  • Indexes exist on frequently queried columns
  • Appropriate data types (DECIMAL for money, etc.)
  • NOT NULL on required fields
  • UNIQUE constraints where needed
  • CHECK constraints for validation
  • created_at and updated_at timestamps
  • Migration scripts are reversible
  • Tested on staging with production data

Deep Dive: Normalization (SQL)

Normal Forms

Form Rule Violation Example
1NF Atomic values, no repeating groups product_ids = '1,2,3'
2NF 1NF + no partial dependencies customer_name in order_items
3NF 2NF + no transitive dependencies country derived from postal_code

1st Normal Form (1NF)

-- BAD: Multiple values in column
CREATE TABLE orders (
  id INT PRIMARY KEY,
  product_ids VARCHAR(255)  -- '101,102,103'
);

-- GOOD: Separate table for items
CREATE TABLE orders (
  id INT PRIMARY KEY,
  customer_id INT
);

CREATE TABLE order_items (
  id INT PRIMARY KEY,
  order_id INT REFERENCES orders(id),
  product_id INT
);

2nd Normal Form (2NF)

-- BAD: customer_name depends only on customer_id
CREATE TABLE order_items (
  order_id INT,
  product_id INT,
  customer_name VARCHAR(100),  -- Partial dependency!
  PRIMARY KEY (order_id, product_id)
);

-- GOOD: Customer data in separate table
CREATE TABLE customers (
  id INT PRIMARY KEY,
  name VARCHAR(100)
);

3rd Normal Form (3NF)

-- BAD: country depends on postal_code
CREATE TABLE customers (
  id INT PRIMARY KEY,
  postal_code VARCHAR(10),
  country VARCHAR(50)  -- Transitive dependency!
);

-- GOOD: Separate postal_codes table
CREATE TABLE postal_codes (
  code VARCHAR(10) PRIMARY KEY,
  country VARCHAR(50)
);

When to Denormalize

Scenario Denormalization Strategy
Read-heavy reporting Pre-calculated aggregates

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Вложенные файлы

README.mdassets/templates/migration-template.sqlreferences/schema-design-checklist.md

FAQ

Что делает скилл database-schema-designer?

Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.

Как установить скилл database-schema-designer?

Скопируй папку скилла в ~/.claude/skills (вкладка Claude Code выше делает это одной командой), либо поставь как плагин.

Скилл database-schema-designer запускает скрипты?

Нет, скилл состоит только из инструкций (SKILL.md), без исполняемых скриптов.

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