Team Skills Tracker

A web application for tracking team member skills and proficiency levels across Azure products, use cases, and soft skills.

Features

• Skills Matrix View: Visual table showing all team members and their skills
• User Profiles: Detailed view of individual skills with easy editing
• Proficiency Levels: Microsoft's L100-L400 scale with color coding
  • L100 (Red): Awareness - aware of the technology
  • L200 (Orange): Conversant - can discuss potential and use cases
  • L300 (Blue): Practitioner - hands-on experience, can speak competently
  • L400 (Green): Expert - deep expertise, can lead workshops
• Skill Categories: Organized by Azure Compute, Data, AI, Security, Networking, DevOps, Soft Skills, and Use Cases
• Easy Skill Management: Quick add/edit/delete with autocomplete
• AI Chat Assistant: Natural language queries for finding experts and analyzing team skills

Tech Stack

• Frontend: React + Vite
• Backend: Node.js + Express
• Agent: Python + Microsoft Agent Framework
• Database: PostgreSQL
• AI Model: Azure AI Services (GPT-4o)

🚀 Quick Start with Docker (Recommended)

Prerequisites: Docker Desktop (Get Docker)

# Clone the repository
git clone <your-repo-url>
cd teamskills

# Start everything
docker-compose up --build

Done! Open http://localhost:3000 in your browser.

Docker automatically:

• ✅ Starts PostgreSQL with test data
• ✅ Runs backend API (http://localhost:3001)
• ✅ Runs agent service (http://localhost:8000)
• ✅ Runs frontend dev server (http://localhost:3000)

Chat Assistant Setup

The AI chat assistant requires Azure AI Services credentials. Create a .env file:

cp .env.example .env

Edit .env with your Azure AI Services settings:

AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o

Without these credentials, the chat will show "Agent not available" but all other features work normally.

See DOCKER.md for detailed Docker commands and troubleshooting.

---

Alternative: Local Setup (Without Docker)

Prerequisites:

• Node.js 18+
• PostgreSQL 14+
• npm or yarn

Setup Instructions

1. Database Setup

Create a PostgreSQL database:

psql -U postgres
CREATE DATABASE teamskills;
\q

Initialize the schema and seed data:

psql -U postgres -d teamskills -f database/schema.sql
psql -U postgres -d teamskills -f database/seed.sql

2. Backend Setup

Create a .env file in the root directory:

cp .env.example .env

Edit .env with your database credentials:

DB_USER=postgres
DB_HOST=localhost
DB_NAME=teamskills
DB_PASSWORD=your_password
DB_PORT=5432
PORT=3001

Install dependencies and start the backend:

npm install
npm start

The API will run on http://localhost:3001

3. Frontend Setup

Install frontend dependencies:

cd frontend
npm install

Start the development server:

npm run dev

The frontend will run on http://localhost:3000

Loading Your Team Data

The repository includes demo data with fictional team members. To load your real team:

1. Create your team seed file by copying and modifying the demo:

   cp database/seed.sql database/seed-team.sql

2. Edit database/seed-team.sql with your real team members' names, emails, and skills

3. Load your team data (replaces demo data):

   # With Docker:
   docker-compose exec db psql -U postgres -d teamskills -c "TRUNCATE users, user_skills, skill_relationships RESTART IDENTITY CASCADE;"
   docker-compose exec -T db psql -U postgres -d teamskills < database/seed-team.sql
   
   # Without Docker:
   psql -U postgres -d teamskills -c "TRUNCATE users, user_skills, skill_relationships RESTART IDENTITY CASCADE;"
   psql -U postgres -d teamskills -f database/seed-team.sql

Note: seed-team.sql is gitignored to protect your team's personal information.

Usage

1. Open http://localhost:3000 in your browser
2. Matrix View: See all team members and their skills at a glance
3. Click a user name to view their detailed profile
4. Add Skills: Click "+ Add Skill" button, search for skills, and select proficiency level
5. Update Proficiency: Use dropdown in profile view to change skill levels
6. Filter Skills: Use category filter in matrix view to focus on specific skill areas
7. Chat Assistant: Click the 💬 button to ask questions like:
   • "Who knows Kubernetes?"
   • "What skill gaps do we have?"
   • "Give me a team skills summary"

API Endpoints

Users

• GET /api/users - Get all users
• GET /api/users/:id - Get single user
• POST /api/users - Create user
• PUT /api/users/:id - Update user
• DELETE /api/users/:id - Delete user

Skills

• GET /api/skills - Get all skills
• GET /api/skills/:id - Get single skill
• GET /api/skills/:id/related - Get related skills
• POST /api/skills - Create skill
• PUT /api/skills/:id - Update skill
• DELETE /api/skills/:id - Delete skill

Skill Categories

• GET /api/categories - Get all categories
• POST /api/categories - Create category

User Skills

• GET /api/user-skills/:userId - Get all skills for a user
• PUT /api/user-skills - Update or create user skill
• DELETE /api/user-skills - Remove user skill

Matrix

• GET /api/matrix - Get complete matrix data (users × skills)

Agent (Chat Assistant)

• GET /agent/status - Check agent availability and capabilities
• POST /chat - Send message and get complete response
• POST /chat/stream - Send message and get streaming SSE response

Development

The application includes 8 test users with diverse skill profiles across different Azure specializations. Use these to explore the functionality or add your own team members.

Future Enhancements

• Export/reporting features (Excel, PDF)
• Authentication and authorization
• Skill endorsements/verification
• Learning resource links
• MCP PostgreSQL integration for advanced queries

Azure Deployment

This project can be deployed to Azure using the Azure Developer CLI (azd).

Prerequisites

• Azure CLI installed and logged in
• Azure Developer CLI (azd) installed

Deploy to Azure

# Login to Azure
az login
azd auth login

# Deploy everything (infrastructure + code)
azd up

This will:

• Create a resource group with PostgreSQL, Container Registry, Container Apps, and Azure AI Services
• Build and push Docker images to the registry
• Deploy all three services (backend, frontend, agent)
• Configure RBAC for the agent to call Azure AI Services

Tear Down Azure Resources

To delete all Azure resources created by this project:

# Delete all resources (with purge for soft-deleted resources)
azd down --force --purge -e <environment-name>

The --force flag skips confirmation prompts, and --purge permanently deletes soft-deletable resources like Key Vaults and Cognitive Services accounts.

Note: If azd down fails, you can manually delete the resource group:

az group delete --name rg-<environment-name> --yes --no-wait

Useful Commands

# List deployed environments
azd env list

# Show environment details (URLs, settings)
azd env get-values -e <environment-name>

# Deploy only code changes (no infrastructure changes)
azd deploy

# Deploy only infrastructure changes
azd provision

# View deployment logs
az containerapp logs show --name <app-name> --resource-group <rg-name> --tail 50

License

ISC