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🪜 Skill Ladder

AI-Powered Career Development & Job Recommendation Platform

Live Demo IEEE Published License

React FastAPI Python spaCy Firebase Tailwind CSS

Helping job seekers climb the career ladder — one AI-powered match at a time.


📌 What Is Skill Ladder?

Skill Ladder is a full-stack AI platform that bridges the gap between job seekers and opportunities. It goes beyond keyword matching — it understands your resume, identifies your skill gaps, and recommends jobs and learning resources tailored to your actual profile.

Built as an IEEE research project and deployed live at skillladder.vercel.app.


✨ Key Features

Feature Description
📋 ATS Resume Evaluation Parses resumes using NLP and scores them against job descriptions with detailed skill gap analysis
🤖 AI Job Recommendations Matches candidates to roles using vector similarity — shows why each job fits, not just a score
📚 Learning Resource Engine Recommends courses and resources to close identified skill gaps
📊 Application Tracker Real-time dashboard to track job applications, statuses, and deadlines
🏢 Recruiter Pipeline Hiring analytics and candidate ranking for recruiters
🔐 Auth & Profiles Secure Firebase Authentication with persistent user profiles

🛠️ Tech Stack

Frontend          → React.js, Tailwind CSS
Backend API       → FastAPI (Python)
NLP / AI          → spaCy, PyPDF2, pdfplumber
Database          → Firebase Firestore
Authentication    → Firebase Auth
Deployment        → Vercel (Frontend), Render (Backend)

🗂️ Project Structure

skillladder/
├── frontend/               # React.js application
│   ├── src/
│   │   ├── components/     # Reusable UI components
│   │   ├── pages/          # Route-level page components
│   │   └── services/       # API calls and Firebase hooks
│   └── package.json
│
├── backend/                # FastAPI Python backend
│   ├── main.py             # Entry point & route definitions
│   ├── resume_parser.py    # spaCy NLP resume extraction
│   ├── job_matcher.py      # Similarity scoring engine
│   └── requirements.txt
│
├── code-runner/            # Isolated code execution service
├── firestore.rules         # Firebase security rules
└── README.md

⚙️ Local Setup

Prerequisites

  • Node.js 18+
  • Python 3.10+
  • Firebase project (for Auth + Firestore)

1. Clone the repo

git clone https://github.com/linga-1221/skillladder.git
cd skillladder

2. Backend setup

cd backend
pip install -r requirements.txt
python -m spacy download en_core_web_sm
uvicorn main:app --reload --port 8000

3. Frontend setup

cd frontend
npm install

Create a .env file in frontend/:

VITE_API_URL=http://localhost:8000
VITE_FIREBASE_API_KEY=your_api_key
VITE_FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
VITE_FIREBASE_PROJECT_ID=your_project_id
npm run dev

4. Open in browser

http://localhost:5173

🧠 How It Works

User uploads resume (PDF)
        ↓
spaCy + pdfplumber extract:
  skills, experience, education, keywords
        ↓
FastAPI computes vector similarity score
against job descriptions
        ↓
Top matches returned with:
  ✅ Match %
  🔴 Missing skills
  📚 Recommended courses
  📄 ATS improvement tips
        ↓
Results displayed in React dashboard

📸 Screenshots

Coming soon — Live demo available at skillladder.vercel.app


📄 Research

This project was developed as part of an IEEE-published research paper exploring AI-driven career development systems and NLP-based resume evaluation at scale.


🚀 Roadmap

  • LangChain RAG integration for contextual job matching
  • Cover letter generator (LLM-powered)
  • Interview question predictor based on job description
  • Multi-language resume support
  • Chrome extension for one-click job application analysis

🤝 Contributing

Contributions are welcome!

# Fork the repo
git checkout -b feature/your-feature
git commit -m "Add: your feature description"
git push origin feature/your-feature
# Open a Pull Request

📬 Contact

Nagalinga K.


Star this repo if Skill Ladder helped or inspired you!

Made with 🧠 and too much coffee by Nagalinga K.

About

AI-powered job recommendation platform — NLP-based resume parsing, vector similarity matching, and role explainability. Built with React, FastAPI, LangChain, spaCy & MongoDB.

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