SkillBridge

An academia–industry collaboration portal for student skill profiles, portfolio evidence, opportunity matching and faculty support.

Originally developed by Varun, Pranay and Ananya for Code2Web BUILD_A_THON at SRM Ramapuram, this repository continues from the shared team checkpoint as Varun's personal development version. The prototype was developed with AI assistance.

Project status

Personal development starts from team checkpoint f320478. This is a student prototype under active development.

Personal deployment: not yet available. A separate deployment will be linked here when ready.

The existing shared team demo runs from a separate repository and deployment branch. Changes in this repository do not automatically update that demo.

Features

Workspace	Capabilities
Student	Skill assessments, skill passport, projects and certifications, opportunity applications, faculty feedback and mentorship tasks
Industry	Company profile, job and internship postings, required skills and weights, applicant skill matches and gaps, shortlisting
Faculty	Development opportunities, project reviews, evidence verification, student skill-gap inspection and mentorship allocation
Institution	Aggregate skill-gap and industry-demand reports, placement readiness summaries and evidence auditing

Feature details are documented in backend/README.md and FACULTY_FEATURES.md.

How matching and AI work

Skill and opportunity matching uses deterministic scoring based on skill requirements, weights and available evidence. These scores are descriptive matches, not predictions of hiring outcomes.

An optional local Ollama integration can explain results without changing numeric scores. It requires a separately running local model and is not required for the core application. AI-generated explanations are not verified as available in the shared hosted demo.

Tech stack

Layer	Technologies
Frontend	React, Vite, JavaScript, React Router and CSS
Backend	Python, Flask, SQLAlchemy, Flask-Migrate and JWT authentication
Database	SQLite for default local development; PostgreSQL through DATABASE_URL for hosted deployments
Testing	pytest, Vitest and React Testing Library
Optional AI	Ollama with a locally installed model

The shared team deployment uses Render for the frontend and backend, and Neon PostgreSQL for its database.

Run locally

Install Python 3, Node.js and npm before starting. Use a Python version compatible with the dependencies in backend/requirements.txt.

1. Clone the repository

git clone https://github.com/StackVarun/skillbridge.git
cd skillbridge

2. Set up the backend

cd backend
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
cp .env.example .env

On Windows, activate the virtual environment with .venv\Scripts\activate instead.

Edit backend/.env and replace SECRET_KEY and JWT_SECRET_KEY with separate secret values. Leave DATABASE_URL unset to use the local database/skillbridge.db. Use the same database configuration for migrations, seeds and the running server.

python -m flask --app run.py db upgrade
python seed_reference_data.py
python run.py

The backend runs on http://localhost:5001 by default. Its health endpoint is http://localhost:5001/health.

3. Start the frontend

Open a second terminal from the repository root:

cd frontend
npm install
npm run dev

Open the URL printed by Vite, normally http://localhost:5173. The development server proxies /api to the local backend. Keep VITE_API_BASE_URL=/api for local development; if you use another backend port, configure API_PROXY_TARGET accordingly.

Optional demo data

From backend/, with the virtual environment active:

DEMO_PASSWORD='choose-a-local-demo-password' python seed_demo.py

The script seeds demo role accounts and synthetic portfolio/application data. Read its output for account details and posting ownership. Run it against a development database; it is not part of normal server startup. See backend/README.md for institution account provisioning and optional AI configuration.

Project structure

• frontend/ — React pages, routing, API client and frontend tests
• backend/ — Flask application, models, routes, services, migrations and tests
• database/ — default local SQLite database
• FACULTY_FEATURES.md — faculty support setup and behavior

Validation commands

Backend, with its virtual environment active:

cd backend
python -m pytest -q

Frontend:

cd frontend
npm test
npm run build

These are the project's validation commands; this README update does not claim a new successful test run.

Development direction

• Improve the usability of each role's workspace.
• Refine application tracking and opportunity discovery.
• Improve validation, error handling and test coverage.
• Configure a separate deployment for this personal version.

These are planned improvements, not completed features.

Prototype limitations

• Demo portfolios and applications are synthetic; self-reported evidence is not a verified credential.
• Self-registered industry and faculty accounts are not independently verified.
• Placement readiness and demand reports summarize available data; they are not predictive hiring models.
• The optional local AI runtime needs separate setup and hosting to work outside local development.

Credits

Originally developed for Code2Web BUILD_A_THON at SRM Ramapuram by:

• Varun
• Pranay
• Ananya

This repository continues as my personal development version, preserving the original team foundation and commit history.