Données d’exemple et modernisation du code legacy

Generate sample JSON data for 5 users.

Requirements:
- include id, name, email, role, createdAt
- roles: admin, user, viewer
- use fake data only
- do not use real personal data

[
  {
    "id": "user_001",
    "name": "Alice Martin",
    "email": "alice.martin@example.com",
    "role": "admin",
    "createdAt": "2026-01-15T10:00:00Z"
  },
  {
    "id": "user_002",
    "name": "Noah Bernard",
    "email": "noah.bernard@example.com",
    "role": "user",
    "createdAt": "2026-01-16T11:30:00Z"
  }
]

Generate SQL seed data for a products table.

Schema:
- id UUID
- name text
- price decimal
- status text
- created_at timestamp

Constraints:
- 10 rows
- realistic but fake data
- include active and inactive products

Generate pytest fixtures for User objects.

Include:
- active user
- deactivated user
- admin user
- user with missing optional profile fields

Use fake data only.

Generate sample data based on our real customer export.

Generate fake sample data with the same schema as this anonymized example.

function getUsers(callback) {
  db.query("SELECT * FROM users", function(err, result) {
    if (err) {
      callback(err);
    } else {
      callback(null, result);
    }
  });
}

Create a plan to modernize this callback-based function to async/await.
Preserve behavior.
Do not modify code yet.
Include tests to add before migration.

Refactor this function to async/await.
Preserve error behavior.
Use the existing db client.
Add Jest tests.

def get_user(id):
    query = "SELECT * FROM users WHERE id = " + id
    return db.execute(query)

Modernize this function safely.

Goals:
- prevent SQL injection
- add type hints
- preserve behavior
- use parameterized queries
- suggest tests

Do not introduce new dependencies.