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Slash command/database-designer
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Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
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LanguagePython
LicenseMIT
📦 Ships with claude-skills
</> SKILL.md
database-designer.SKILL.md
---name: "database-designer"
description: "Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships."
---# Database Designer - POWERFUL Tier Skill
## Overview
A comprehensive database design skill that provides expert-level analysis, optimization, and migration capabilities for modern database systems. This skill combines theoretical principles with practical tools to help architects and developers create scalable, performant, and maintainable database schemas.
## Core Competencies
### Schema Design & Analysis
- **Normalization Analysis**: Automated detection of normalization levels (1NF through BCNF)
- **Denormalization Strategy**: Smart recommendations for performance optimization
- **Data Type Optimization**: Identification of inappropriate types and size issues
- **Constraint Analysis**: Missing foreign keys, unique constraints, and null checks
- **Naming Convention Validation**: Consistent table and column naming patterns
- **ERD Generation**: Automatic Mermaid diagram creation from DDL
### Index Optimization
- **Index Gap Analysis**: Identification of missing indexes on foreign keys and query patterns
- **Composite Index Strategy**: Optimal column ordering for multi-column indexes
- **Index Redundancy Detection**: Elimination of overlapping and unused indexes
Accepts SQL DDL or JSON schema (`assets/sample_schema.sql` / `sample_schema.json`). Output includes normalization findings, missing constraints, naming issues, and a Mermaid ERD — show the ERD to the user and fix flagged issues before optimizing.
### 2. Optimize indexes against real query patterns
Write the user's hot queries into a query-patterns JSON first (copy `assets/sample_query_patterns.json`). Output is a priority-ordered list of CREATE INDEX recommendations plus redundant-index removals.
`--zero-downtime` emits an expand-contract plan; `--validate-only` checks feasibility without generating SQL.
### 4. Verification loop
Re-run step 1 on the *target* schema and assert the issues found in the first pass are gone; run `migration_generator.py --validate-only` before handing over the migration.
## Database Design Principles
→ See references/database-design-reference.md for details
| **DynamoDB** | Wide-column | Serverless AWS apps, single-digit-ms latency at any scale |
> Use SQL as default. Reach for NoSQL only when the access pattern clearly benefits from it.
---
## Sharding & Replication
### Horizontal vs Vertical Partitioning
- **Vertical partitioning**: Split columns across tables (e.g., separate BLOB columns). Reduces I/O for narrow queries.
- **Horizontal partitioning (sharding)**: Split rows across databases/servers. Required when a single node cannot hold the dataset or handle the throughput.
### Sharding Strategies
| Strategy | How It Works | Pros | Cons |
|----------|-------------|------|------|
| **Hash** | `shard = hash(key) % N` | Even distribution | Resharding is expensive |
| **Range** | Shard by date or ID range | Simple, good for time-series | Hot spots on latest shard |
| **Geographic** | Shard by user region | Data locality, compliance | Cross-region queries are hard |
