Assistant Base de Données

VérifiéPrudence

Expert en conception de schémas, optimisation de requêtes SQL, migrations et frameworks ORM. Aide à normaliser, indexer et optimiser vos bases de données.

Spar Skills Guide Bot
Data & IAIntermédiaire
111010/03/2026
Claude CodeCursorWindsurfCopilotCodex
#database#sql#schema-design#query-optimization#orm

Recommandé pour

Synthèse automatique

Ce skill fournit une expertise en bases de données relationnelles et NoSQL pour concevoir des schémas, écrire et optimiser des requêtes SQL, utiliser des ORM et planifier des migrations.

Points forts

  • Couvre les principes de normalisation (1NF à 3NF) avec des indications claires sur le moment de dénormaliser
  • Fournit des exemples SQL concrets pour les relations un-à-plusieurs, plusieurs-à-plusieurs et les suppressions logiques
  • Détaille une stratégie d'indexation (composite, partielle) et l'analyse via EXPLAIN ANALYZE
  • Inclut des exemples d'ORM pour Prisma et SQLAlchemy ainsi que des bonnes pratiques de migration

Limites

  • Les exemples SQL sont surtout orientés PostgreSQL (SERIAL, types) et peu adaptés à d'autres moteurs
  • La couverture NoSQL est annoncée dans l'introduction mais aucun exemple concret n'est fourni
  • Peu de contenu sur la sécurité des données, les transactions ou les stratégies de sharding à grande échelle
Quand l'utiliser

Utilisez ce skill lorsqu'il faut modéliser un schéma, écrire ou optimiser des requêtes SQL, configurer un ORM ou préparer une migration.

Quand l'éviter

Évitez-le pour des problématiques d'administration de serveur, de réplication ou d'optimisation NoSQL spécifique, peu traitées ici.

Analyse de sécurité

Prudence
Score qualité88/100

The skill is an educational database assistant and does not explicitly instruct destructive or exfiltrating actions. However, it declares Bash as an allowed tool and includes schema-modification SQL examples, which could be executed on real databases with unintended consequences. This falls under legitimate use of powerful tools, so caution is appropriate.

Points d'attention
  • •Declares Bash as an allowed tool, which permits execution of arbitrary shell commands.
  • •SQL examples include ALTER TABLE, CREATE INDEX, and migration patterns that could alter or drop data if executed on a production database without review.
  • •The skill's guidance could lead an AI agent to run database commands via Bash (e.g., psql, mysql) that have side effects.
Comment cette analyse est faite

Exemples

Optimize a slow query
My query SELECT * FROM posts WHERE user_id = 42 ORDER BY created_at DESC is slow on a large table. What index should I add and how do I verify the improvement?
Model many-to-many with Prisma
Design a Prisma schema for users and roles with a many-to-many relation, including the join table and the indexes I need.
Plan a zero-downtime migration
I need to rename a column in the users table on a Postgres database with live traffic. Write a reversible migration plan that stays safe.

name: database-assistant description: Activates when user needs help with database design, SQL queries, migrations, or ORM usage. Triggers on "database schema", "SQL query", "migration", "optimize query", "foreign key", "index", "normalize", "ORM", "Prisma", "TypeORM", "SQLAlchemy", or database-related questions. allowed-tools: Read, Write, Edit, Glob, Grep, Bash

Database Assistant

You are a database expert with deep knowledge of relational databases, NoSQL, query optimization, schema design, and ORM frameworks.

Schema Design Principles

Normalization

  • 1NF: Atomic values, no repeating groups
  • 2NF: No partial dependencies
  • 3NF: No transitive dependencies

When to Denormalize

  • Read-heavy workloads
  • Complex joins impacting performance
  • Reporting/analytics queries

Common Patterns

One-to-Many

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255) NOT NULL
);

CREATE TABLE posts (
  id SERIAL PRIMARY KEY,
  user_id INTEGER REFERENCES users(id),
  title VARCHAR(255) NOT NULL,
  content TEXT
);

Many-to-Many

CREATE TABLE users (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255)
);

CREATE TABLE roles (
  id SERIAL PRIMARY KEY,
  name VARCHAR(50)
);

CREATE TABLE user_roles (
  user_id INTEGER REFERENCES users(id),
  role_id INTEGER REFERENCES roles(id),
  PRIMARY KEY (user_id, role_id)
);

Soft Deletes

ALTER TABLE users ADD COLUMN deleted_at TIMESTAMP NULL;

-- Query active users
SELECT * FROM users WHERE deleted_at IS NULL;

Query Optimization

Indexing Strategy

-- Single column index
CREATE INDEX idx_users_email ON users(email);

-- Composite index (order matters)
CREATE INDEX idx_posts_user_created ON posts(user_id, created_at);

-- Partial index
CREATE INDEX idx_active_users ON users(email) WHERE deleted_at IS NULL;

Query Analysis

EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'test@example.com';

Common Optimizations

  • Use indexes on WHERE, JOIN, ORDER BY columns
  • Avoid SELECT *
  • Use LIMIT for large result sets
  • Batch inserts/updates
  • Use connection pooling

ORM Examples

Prisma

model User {
  id    Int     @id @default(autoincrement())
  email String  @unique
  posts Post[]
}

model Post {
  id       Int    @id @default(autoincrement())
  title    String
  author   User   @relation(fields: [authorId], references: [id])
  authorId Int
}

SQLAlchemy

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    email = Column(String, unique=True)
    posts = relationship('Post', back_populates='author')

class Post(Base):
    __tablename__ = 'posts'
    id = Column(Integer, primary_key=True)
    title = Column(String)
    author_id = Column(Integer, ForeignKey('users.id'))
    author = relationship('User', back_populates='posts')

Migration Best Practices

  1. Atomic Changes: One logical change per migration
  2. Reversibility: Always include rollback
  3. Data Safety: Backup before major changes
  4. Zero Downtime: Consider live traffic
  5. Testing: Test migrations on copy of production data

Guidelines

  • Design for current needs, but consider growth
  • Choose appropriate data types
  • Add indexes based on query patterns
  • Document schema decisions
  • Use constraints for data integrity
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