enterprise-user-management-ai-system — Claude Code skill: what it does

What enterprise-user-management-ai-system does

The skill covers a three-part stack: a Node.js backend, a Python ML service and a React frontend. It lays out role-based access with JWT auth, tasks on a Kanban board with time tracking, ticket creation and classification, admin user operations and audit logs. The analytics side handles risk prediction, anomaly detection, burnout analysis and project delay forecasting.

You need Node.js 14+, Python 3.8+ and MongoDB, local or remote. The backend listens on http://localhost:5000, the ML service on http://localhost:8000, the frontend on http://localhost:3000. Each part reads its own .env: the backend takes MONGODB_URI, JWT_SECRET, JWT_EXPIRE and ML_SERVICE_URL; the ML service takes MODEL_PATH and LOG_LEVEL; the frontend takes REACT_APP_API_URL and REACT_APP_ML_URL. Skip the ML service and the analytics endpoints stay silent, since the backend reaches them through ML_SERVICE_URL.

Endpoint examples are included for registration and login, user CRUD, tasks, tickets and the four AI calls. On the frontend side there is a useAuth hook and a KanbanBoard component to start from.