MCPInstall

enterprise-user-management-ai-analytics

by reason-machinesData & Databases

Enterprise user management system with AI-powered analytics for risk detection, burnout analysis, and predictive insights

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/data-skills --skill enterprise-user-management-ai-analytics
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads enterprise-user-management-ai-analytics automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

git clone https://github.com/Nareshkumar2583/Enterprise-User-Management-System-with-AI-Analytics.git
cd Enterprise-User-Management-System-with-AI-Analytics

Backend Setup

cd backend
npm install

Create .env file in backend/:

PORT=5000
MONGODB_URI=mongodb://localhost:27017/enterprise-user-mgmt
JWT_SECRET=your_jwt_secret_key_here
JWT_EXPIRE=7d
ML_SERVICE_URL=http://localhost:8000
NODE_ENV=development

Start backend:

npm start
# Backend runs at http://localhost:5000

ML Service Setup

cd ml-service
pip install -r requirements.txt

Create .env file in ml-service/:

MONGODB_URI=mongodb://localhost:27017/enterprise-user-mgmt
MODEL_PATH=./models
LOG_LEVEL=INFO

Start ML service:

uvicorn main:app --reload --port 8000
# ML service runs at http://localhost:8000

Frontend Setup

cd frontend
npm install

Create .env file in frontend/:

REACT_APP_API_URL=http://localhost:5000/api
REACT_APP_ML_URL=http://localhost:8000

Start frontend:

npm start
# Frontend runs at http://localhost:3000

Key API Endpoints

Authentication APIs

// POST /api/auth/register
const registerUser = async (userData) => {
  const response = await fetch('http://localhost:5000/api/auth/register', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      name: userData.name,
      email: userData.email,
      password: userData.password,
      role: userData.role // 'admin' or 'user'
    })
  });
  return response.json();
};

// POST /api/auth/login
const loginUser = async (credentials) => {
  const response = await fetch('http://localhost:5000/api/auth/login', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      email: credentials.email,
      password: credentials.password
    })
  });
  const data = await response.json();
  // Returns: { token: 'jwt_token', user: {...} }
  localStorage.setItem('token', data.token);
  return data;
};

User Management APIs

// GET /api/users (Admin only)
const getAllUsers = async () => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/users', {
    headers: {
      'Authorization': `Bearer ${token}`
    }
  });
  return response.json();
};

// PUT /api/users/:id (Admin only)
const updateUser = async (userId, updates) => {
  const token = localStorage.getItem('token');
  const response = await fetch(`http://localhost:5000/api/users/${userId}`, {
    method: 'PUT',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify(updates)
  });
  return response.json();
};

// DELETE /api/users/:id (Admin only)
const deleteUser = async (userId) => {
  const token = localStorage.getItem('token');
  const response = await fetch(`http://localhost:5000/api/users/${userId}`, {
    method: 'DELETE',
    headers: {
      'Authorization': `Bearer ${token}`
    }
  });
  return response.json();
};

Task Management APIs

// POST /api/tasks
const createTask = async (taskData) => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tasks', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({
      title: taskData.title,
      description: taskData.description,
      assignedTo: taskData.userId,
      priority: taskData.priority, // 'low', 'medium', 'high'
      dueDate: taskData.dueDate,
      status: 'todo' // 'todo', 'in-progress', 'done'
    })
  });
  return response.json();
};

// GET /api/tasks/user/:userId
const getUserTasks = async (userId) => {
  const token = localStorage.getItem('token');
  const response = await fetch(`http://localhost:5000/api/tasks/user/${userId}`, {
    headers: {
      'Authorization': `Bearer ${token}`
    }
  });
  return response.json();
};

// PATCH /api/tasks/:id/status
const updateTaskStatus = async (taskId, newStatus) => {
  const token = localStorage.getItem('token');
  const response = await fetch(`http://localhost:5000/api/tasks/${taskId}/status`, {
    method: 'PATCH',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({ status: newStatus })
  });
  return response.json();
};

Support Ticket APIs

// POST /api/tickets
const createTicket = async (ticketData) => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tickets', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify({
      subject: ticketData.subject,
      description: ticketData.description,
      priority: ticketData.priority,
      category: ticketData.category
    })
  });
  return response.json();
};

// GET /api/tickets
const getAllTickets = async () => {
  const token = localStorage.getItem('token');
  const response = await fetch('http://localhost:5000/api/tickets', {
    headers: {
      'Authorization': `Bearer ${token}`
    }
  });
  return response.json();
};

AI/ML Service Integration

Risk Prediction

// POST /predict/risk
const predictUserRisk = async (userId) => {
  const response = await fetch('http://localhost:8000/predict/risk', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userId,
      features: {
        login_frequency: 45,
        failed_logins: 3,
        task_completion_rate: 0.75,
        avg_response_time: 120,
        ticket_count: 5
      }
    })
  });
  const data = await response.json();
  // Returns: { risk_score: 0.23, risk_level: 'low', factors: [...] }
  return data;
};

Anomaly Detection

// POST /detect/anomaly
const detectAnomaly = async (userActivity) => {
  const response = await fetch('http://localhost:8000/detect/anomaly', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userActivity.userId,
      activity_data: {
        login_time: userActivity.loginTime,
        ip_address: userActivity.ipAddress,
        location: userActivity.location,
        device: userActivity.device,
        actions: userActivity.actions
      }
    })
  });
  const data = await response.json();
  // Returns: { is_anomaly: false, anomaly_score: 0.12, details: {...} }
  return data;
};

Burnout Detection

// POST /predict/burnout
const detectBurnout = async (userId) => {
  const response = await fetch('http://localhost:8000/predict/burnout', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      user_id: userId,
      metrics: {
        tasks_assigned: 25,
        tasks_completed: 18,
        avg_task_duration: 180, // minutes
        overtime_hours: 15,
        weekend_work_frequency: 3,
        break_frequency: 2
      }
    })
  });
  const data = await response.json();
  // Returns: { burnout_risk: 'medium', score: 0.65, recommendations: [...] }
  return data;
};

Ticket Classification

// POST /classify/ticket
const classifyTicket = async (ticketContent) => {
  const response = await fetch('http://localhost:8000/classify/ticket', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      subject: ticketContent.subject,
      description: ticketContent.description
    })
  });
  const data = await response.json();
  // Returns: { category: 'technical', priority: 'high', suggested_assignee: 'team-a' }
  return data;
};

Predictive Project Insights

// POST /predict/project-delay
const predictProjectDelay = async (projectData) => {
  const response = await fetch('http://localhost:8000/predict/project-delay', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      project_id: projectData.projectId,
      total_tasks: projectData.totalTasks,
      completed_tasks: projectData.completedTasks,
      days_remaining: projectData.daysRemaining,
      team_size: projectData.teamSize,
      avg_velocity: projectData.avgVelocity
    })
  });
  const data = await response.json();
  // Returns: { delay_probability: 0.45, estimated_delay_days: 3, risk_factors: [...] }
  return data;
};

Common Usage Patterns

Protected Route Component (React)

import React from 'react';
import { Navigate } from 'react-router-dom';

const ProtectedRoute = ({ children, requiredRole }) => {
  const token = localStorage.getItem('token');
  const user = JSON.parse(localStorage.getItem('user') || '{}');

  if (!token) {
    return <Navigate to="/login" />;
  }

  if (requiredRole && user.role !== requiredRole) {
    return <Navigate to="/unauthorized" />;
  }

  return children;
};

export default ProtectedRoute;

// Usage in App.js
import { BrowserRouter, Routes, Route } from 'react-router-dom';

function App() {
  return (
    <BrowserRouter>
      <Routes>
        <Route path="/login" element={<Login />} />
        <Route path="/dashboard" element={
          <ProtectedRoute>
            <UserDashboard />
          </ProtectedRoute>
        } />
        <Route path="/admin" element={
          <ProtectedRoute requiredRole="admin">
            <AdminDashboard />
          </ProtectedRoute>
        } />
      </Routes>
    </BrowserRouter>
  );
}

Kanban Board Component

import React, { useState, useEffect } from 'react';

const KanbanBoard = ({ userId }) => {
  const [tasks, setTasks] = useState({ todo: [], inProgress: [], done: [] });

  useEffect(() => {
    fetchTasks();
  }, [userId]);

  const fetchTasks = async () => {
    const token = localStorage.getItem('token');
    const response = await fetch(`http://localhost:5000/api/tasks/user/${userId}`, {
      headers: { 'Authorization': `Bearer ${token}` }
    });
    const data = await response.json();
    
    // Organize tasks by status
    const organized = {
      todo: data.filter(t => t.status === 'todo'),
      inProgress: data.filter(t => t.status === 'in-progress'),
      done: data.filter(t => t.status === 'done')
    };
    setTasks(organized);
  };

  const moveTask = async (taskId, newStatus) => {
    const token = localStorage.getItem('token');
    await fetch(`http://localhost:5000/api/tasks/${taskId}/status`, {
      method: 'PATCH',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${token}`
      },
      body: JSON.stringify({ status: newStatus })
    });
    fetchTasks(); // Refresh board
  };

  return (
    <div className="kanban-board">
      <Column title="To Do" tasks={tasks.todo} onMove={moveTask} targetStatus="in-progress" />
      <Column title="In Progress" tasks={tasks.inProgress} onMove={moveTask} targetStatus="done" />
      <Column title="Done" tasks={tasks.done} />
    </div>
  );
};

Time Tracking Hook

import { useState, useEffect } from 'react';

const useTimeTracker = (taskId) => {
  const [isTracking, setIsTracking] = useState(false);
  const [elapsedTime, setElapsedTime] = useState(0);

  useEffect(() => {
    let interval;
    if (isTracking) {
      interval = setInterval(() => {
        setElapsedTime(prev => prev + 1);
      }, 1000);
    }
    return () => clearInterval(interval);
  }, [isTracking]);

  const startTracking = () => {
    setIsTracking(true);
  };

  const stopTracking = async () => {
    setIsTracking(false);
    
    // Log time to backend
    const token = localStorage.getItem('token');
    await fetch(`http://localhost:5000/api/tasks/${taskId}/log-time`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${token}`
      },
      body: JSON.stringify({ duration: elapsedTime })
    });
  };

  const resetTracking = () => {
    setElapsedTime(0);
    setIsTracking(false);
  };

  return { isTracking, elapsedTime, startTracking, stopTracking, resetTracking };
};

export default useTimeTracker;

AI-Powered User Analytics Dashboard

import React, { useState, useEffect } from 'react';

const UserAnalyticsDashboard = ({ userId }) => {
  const [analytics, setAnalytics] = useState({
    riskScore: null,
    burnoutRisk: null,
    anomalies: []
  });

  useEffect(() => {
    fetchAnalytics();
  }, [userId]);

  const fetchAnalytics = async () => {
    try {
      // Fetch risk prediction
      const riskRes = await fetch('http://localhost:8000/predict/risk', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ user_id: userId })
      });
      const riskData = await riskRes.json();

      // Fetch burnout detection
      const burnoutRes = await fetch('http://localhost:8000/predict/burnout', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ user_id: userId })
      });
      const burnoutData = await burnoutRes.json();

      setAnalytics({
        riskScore: riskData,
        burnoutRisk: burnoutData,
        anomalies: []
      });
    } catch (error) {
      console.error('Error fetching analytics:', error);
    }
  };

  return (
    <div className="analytics-dashboard">
      <div className="risk-indicator">
        <h3>Risk Level: {analytics.riskScore?.risk_level}</h3>
        <div className="score">{analytics.riskScore?.risk_score}</div>
      </div>
      <div className="burnout-indicator">
        <h3>Burnout Risk: {analytics.burnoutRisk?.burnout_risk}</h3>
        <div className="score">{analytics.burnoutRisk?.score}</div>
        <ul>
          {analytics.burnoutRisk?.recommendations?.map((rec, idx) => (
            <li key={idx}>{rec}</li>
          ))}
        </ul>
      </div>
    </div>
  );
};

Configuration

Backend Configuration (backend/config.js)

module.exports = {
  port: process.env.PORT || 5000,
  mongoURI: process.env.MONGODB_URI,
  jwtSecret: process.env.JWT_SECRET,
  jwtExpire: process.env.JWT_EXPIRE || '7d',
  mlServiceURL: process.env.ML_SERVICE_URL || 'http://localhost:8000',
  
  // Rate limiting
  rateLimitWindowMs: 15 * 60 * 1000, // 15 minutes
  rateLimitMax: 100,
  
  // File upload
  maxFileSize: 5 * 1024 * 1024, // 5MB
  
  // Email (if configured)
  emailService: process.env.EMAIL_SERVICE,
  emailUser: process.env.EMAIL_USER,
  emailPassword: process.env.EMAIL_PASSWORD
};

ML Service Configuration

Create ml-service/config.py:

import os
from dotenv import load_dotenv

load_dotenv()

class Config:
    MONGODB_URI = os.getenv('MONGODB_URI', 'mongodb://localhost:27017/enterprise-user-mgmt')
    MODEL_PATH = os.getenv('MODEL_PATH', './models')
    LOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO')
    
    # ML Model Parameters
    RISK_THRESHOLD = 0.7
    ANOMALY_THRESHOLD = 0.5
    BURNOUT_THRESHOLD = 0.6
    
    # Online learning
    RETRAIN_INTERVAL = 86400  # 24 hours in seconds
    MIN_SAMPLES_RETRAIN = 100

config = Config()

Troubleshooting

Issue: JWT Token Expired

// Add token refresh logic
const refreshToken = async () => {
  const refreshToken = localStorage.getItem('refreshToken');
  const response = await fetch('http://localhost:5000/api/auth/refresh', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ refreshToken })
  });
  const data = await response.json();
  localStorage.setItem('token', data.token);
  return data.token;
};

// Axios interceptor for auto-refresh
import axios from 'axios';

axios.interceptors.response.use(
  response => response,
  async error => {
    if (error.response?.status === 401) {
      try {
        const newToken = await refreshToken();
        error.config.headers.Authorization = `Bearer ${newToken}`;
        return axios(error.config);
      } catch (refreshError) {
        // Redirect to login
        window.location.href = '/login';
      }
    }
    return Promise.reject(error);
  }
);

Issue: ML Service Connection Failed

// Add fallback when ML service is unavailable
const getPredictionWithFallback = async (endpoint, data) => {
  try {
    const response = await fetch(`http://localhost:8000${endpoint}`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(data),
      timeout: 5000
    });
    
    if (!response.ok) throw new Error('ML service error');
    return await response.json();
  } catch (error) {
    console.warn('ML service unavailable, using fallback:', error);
    // Return default/cached predictions
    return { 
      available: false, 
      message: 'AI analytics temporarily unavailable' 
    };
  }
};

Issue: MongoDB Connection Error

// backend/db.js - Add retry logic
const mongoose = require('mongoose');

const connectDB = async (retries = 5) => {
  try {
    await mongoose.connect(process.env.MONGODB_URI, {
      useNewUrlParser: true,
      useUnifiedTopology: true,
      serverSelectionTimeoutMS: 5000
    });
    console.log('MongoDB Connected');
  } catch (error) {
    console.error('MongoDB connection error:', error);
    
    if (retries > 0) {
      console.log(`Retrying connection... (${retries} attempts left)`);
      setTimeout(() => connectDB(retries - 1), 5000);
    } else {
      process.exit(1);
    }
  }
};

module.exports = connectDB;

Issue: CORS Errors

// backend/server.js - Configure CORS properly
const express = require('express');
const cors = require('cors');

const app = express();

app.use(cors({
  origin: process.env.FRONTEND_URL || 'http://localhost:3000',
  credentials: true,
  methods: ['GET', 'POST', 'PUT', 'DELETE', 'PATCH'],
  allowedHeaders: ['Content-Type', 'Authorization']
}));

Issue: Model Performance Degradation

# ml-service/main.py - Add model monitoring
from datetime import datetime
import logging

class ModelMonitor:
    def __init__(self):
        self.predictions = []
        self.performance_log = []
    
    def log_prediction(self, input_data, prediction, actual=None):
        entry = {
            'timestamp': datetime.now(),
            'input': input_data,
            'prediction': prediction,
            'actual': actual
        }
        self.predictions.append(entry)
        
        # Check if model needs retraining
        if len(self.predictions) >= 100:
            self.evaluate_performance()
    
    def evaluate_performance(self):
        # Calculate accuracy, drift, etc.
        if self.should_retrain():
            logging.warning('Model performance degraded, triggering retrain')
            # Trigger retraining pipeline

monitor = ModelMonitor()

This skill provides comprehensive coverage for developers to use the Enterprise User Management System with AI Analytics, including API integration, ML service usage, and common implementation patterns.

FAQ

What does the enterprise-user-management-ai-analytics skill do?

Enterprise user management system with AI-powered analytics for risk detection, burnout analysis, and predictive insights

How do I install enterprise-user-management-ai-analytics?

Run npx skills add https://github.com/reason-machines/data-skills --skill enterprise-user-management-ai-analytics in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/enterprise-user-management-ai-analytics/ (personal) or .claude/skills/enterprise-user-management-ai-analytics/ (project).

Which AI agents can use enterprise-user-management-ai-analytics?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is enterprise-user-management-ai-analytics safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains enterprise-user-management-ai-analytics?

It is published by reason-machines in reason-machines/data-skills. It has 663 recorded installs.

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