Detalle del Skill

database-designer

Useful adjacent database-design capability, but not data engineering broadly.

CoincidenciaPosibleRevisado para ingenieros de datos
Fuentealirezarezvani/claude-skillsFuente externa
Instalaciones reportadasNo reportadoSolo señal de popularidad

Revisar antes de usar

La revisión automática comprueba relevancia, no seguridad ni respaldo. Lee las instrucciones de la fuente antes de usar este Skill.

Vista previa guardada

SKILL.md

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
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
- **Performance Impact Modeling**: Selectivity estimation and query cost analysis
- **Index Type Selection**: B-tree, hash, partial, covering, and specialized indexes

### Migration Management
- **Zero-Downtime Migrations**: Expand-contract pattern implementation
- **Schema Evolution**: Safe column additions, deletions, and type changes
- **Data Migration Scripts**: Automated data transformation and validation
- **Rollback Strategy**: Complete reversal capabilities with validation
- **Execution Planning**: Ordered migration steps with dependency resolution

## Tool Workflow (run these — do not analyze schemas by hand)

All paths relative to this skill folder; sample inputs in `assets/`.

### 1. Analyze the schema

```bash
python3 schema_analyzer.py --input schema.sql --generate-erd --output-format json -o analysis.json
```

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

```bash
python3 index_optimizer.py --schema assets/sample_schema.json --queries assets/sample_query_patterns.json --analyze-existing --format json -o indexes.json
```

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.

### 3. Generate the migration

```bash
python3 migration_generator.py --current current_schema.json --target target_schema.json --zero-downtime --format sql -o migration.sql
```

`--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

## Best Practices

### Schema Design
1. **Use meaningful names**: Clear, consistent naming conventions
2. **Choose appropriate data types**: Right-sized columns for storage efficiency
3. **Define proper constraints**: Foreign keys, check constraints, unique indexes
4. **Consider future growth**: Plan for scale from the beginning
5. **Document relationships**: Clear foreign key relationships and business rules

### Performance Optimization
1. **Index strategically**: Cover common query patterns without over-indexing
2. **
Leer la fuente completa en GitHub (abre una página externa)
Contexto

Trabajo relacionado