Detalle del Skill

sql-database-assistant

SQL analysis is useful to data scientists, though primarily database-focused.

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

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SKILL.md

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---
name: "sql-database-assistant"
description: "Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy."
---

# SQL Database Assistant - POWERFUL Tier Skill

## Overview

The operational companion to database design. While **database-designer** focuses on schema architecture and **database-schema-designer** handles ERD modeling, this skill covers the day-to-day: writing queries, optimizing performance, generating migrations, and bridging the gap between application code and database engines.

### Core Capabilities

- **Natural Language to SQL** — translate requirements into correct, performant queries
- **Schema Exploration** — introspect live databases across PostgreSQL, MySQL, SQLite, SQL Server
- **Query Optimization** — EXPLAIN analysis, index recommendations, N+1 detection, rewrite patterns
- **Migration Generation** — up/down scripts, zero-downtime strategies, rollback plans
- **ORM Integration** — Prisma, Drizzle, TypeORM, SQLAlchemy patterns and escape hatches
- **Multi-Database Support** — dialect-aware SQL with compatibility guidance

### Tools

| Script | Purpose |
|--------|---------|
| `scripts/query_optimizer.py` | Static analysis of SQL queries for performance issues |
| `scripts/migration_generator.py` | Generate migration file templates from change descriptions |
| `scripts/schema_explorer.py` | Generate schema documentation from introspection queries |

---

## Natural Language to SQL

### Translation Patterns

When converting requirements to SQL, follow this sequence:

1. **Identify entities** — map nouns to tables
2. **Identify relationships** — map verbs to JOINs or subqueries
3. **Identify filters** — map adjectives/conditions to WHERE clauses
4. **Identify aggregations** — map "total", "average", "count" to GROUP BY
5. **Identify ordering** — map "top", "latest", "highest" to ORDER BY + LIMIT

### Common Query Templates

**Top-N per group (window function)**
```sql
SELECT * FROM (
  SELECT *, ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rn
  FROM employees
) ranked WHERE rn <= 3;
```

**Running totals**
```sql
SELECT date, amount,
  SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total
FROM transactions;
```

**Gap detection**
```sql
SELECT curr.id, curr.seq_num, prev.seq_num AS prev_seq
FROM records curr
LEFT JOIN records prev ON prev.seq_num = curr.seq_num - 1
WHERE prev.id IS NULL AND curr.seq_num > 1;
```

**UPSERT (PostgreSQL)**
```sql
INSERT INTO settings (key, value, updated_at)
VALUES ('theme', 'dark', NOW())
ON CONFLICT (key) DO UPDATE SET value = EXCLUDED.value, updated_at = EXCLUDED.updated_at;
```

**UPSERT (MySQL)**
```sql
INSERT INTO settings (key_name, value, updated_at)
VALUES ('theme', 'dark', NOW())
ON DUPLICATE KEY UPDATE value = VALUES(value), updated_at = VALUES(updated_at);
```

> See references/query_patterns.md for JOINs, CTEs, window functions, JSON operations, and more.

---

## Schema Exploration

### Introspection Queries

**PostgreSQL — list tables and columns**
```sql
SELECT table_name, column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_schema = 'public'
ORDER BY table_name, ordinal_position;
```

**PostgreSQL — foreign keys**
```sql
SELECT tc.table_name, kcu.column_name,
  ccu.table_name AS foreign_table, ccu.column_name AS foreign_column
FROM information_schema.table_constraints tc
JOIN information_schema.key_column_usage kcu ON tc.constraint_name = kcu.constraint_name
JOIN information_schema.constraint_column_usage ccu ON tc.constraint_name = ccu.constraint_name
WHERE tc.constraint_type = 'FOREIGN KEY';
```

**MySQL — table sizes**
```sql
SELECT table_name, table_rows,
  ROUND(data_length / 1024 / 1024, 2) AS data_mb,
  ROUND(index_length / 1024 / 1024, 2) AS index_mb
FROM information_schema.tables
WHERE table_schema = DATABASE(
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