Agent skill · software engineering · aradotso
enterprise-user-management-ai-analytics
Enterprise user management system with AI-powered analytics for risk detection, burnout analysis, and predictive insights
Why this skill is useful
Adds executable scripts for setting up a full-stack enterprise user management system with AI analytics, including specific configurations and API integrations.
What it needs
Requires mongodb, node, python installed locally. About 8k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Enterprise User Management System with AI Analytics Skill by ara.so — Data Skills collection. Overview Enterprise User Management System with AI Analytics is a full-stack application that combines user administration, task management, and support ticket handling with AI-powered insights. The system uses machine learning for risk detection, anomaly detection, burnout analysis, and predictive project insights to help organizations automate workflows and improve decision-making. Architecture: Frontend: React.js dashboard for admins and users Backend: Node.js REST API with JWT authentication ML Service: FastAPI service using scikit-learn and River for online learning Database: MongoDB for data persistence Installation Prerequisites Ensure you have installed: Node.js (v14+) Python (3.8+) MongoDB (running locally or cloud instance) Clone and Setup Backend Setup Create .env file in backend/: Start backend: ML Service Setup Create .env file in ml-service/: Start ML service: Frontend Setup Create .env file in frontend/: Start frontend: Key API Endpoints Authentication APIs User Management APIs Task Management APIs Support Ticket APIs AI/ML Service Integration Risk Prediction Anomaly Detection Burnout Detection Ticket Classification Predictive Project Insights Common Usage Patterns Protected Route Component (React) Kanban Board Component Time Tracking Hook AI-Powered User Analytics Dashboard Configuration Backend Configuration (backend/config.js) ML Service Configuration Create ml-service/config.py: Troubleshooting Issue: JWT Token Expired Issue: ML Service Connection Failed Issue: MongoDB Connection Error Issue: CORS Errors Issue: Model Performance Degradation `python ml-service/main.py - Add model monitoring from datetime import datetime import logging class ModelMonitor: def init(self):
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/enterprise-user-management-ai-analytics