🚀 skillbridge.AI

Your AI-Powered Career Companion

📋 Table of Contents

Overview
Demo
Features
Tech Stack
System Architecture
Usage
API Documentation
Project Structure
Authors

🎯 Overview

skillbridge.AI is an intelligent career development platform that helps students and job seekers identify skill gaps between their résumé and target job positions. Using advanced Large Language Models (LLMs), the platform provides personalized course recommendations, hands-on project ideas, and AI-generated cover letters to help bridge these gaps and land dream internships or new grad positions.

Key Objectives

Skill Gap Analysis: Automatically identify missing skills required for target positions
Personalized Learning: Recommend relevant courses from Udemy and Coursera
Project-Based Learning: Generate actionable project ideas to build missing skills
Cover Letter Generation: Create tailored cover letters using AI

🎥 Demo

Watch the Demo Video

▶️ Watch on YouTube

Or visit the live application: https://level-up-llm-skill-analyzer.onrender.com/

✨ Features

🔍 Skill Gap Analyzer

Automated Skill Extraction: Extracts skills from both résumé and job descriptions using LLM
Intelligent Matching: Compares required vs. available skills with weighted scoring
Visual Analytics: Displays match percentages, coverage statistics, and gap analysis
Skill Categorization: Organizes skills into Programming Languages, Frameworks/Libraries, Tools/Platforms, and Soft Skills

📚 Course Recommendations

Multi-Platform Support: Searches across Udemy and Coursera databases
Free & Paid Options: Provides both free and paid course recommendations
Skill-Based Matching: Matches courses to missing skills with deep search algorithms
Detailed Course Info: Includes duration, difficulty, ratings, costs, and direct links

🛠️ Project Recommendations

Two-Track Approach:
- Project 1: Build with your current skills (immediate portfolio projects)
- Project 2: Learn missing skills (projects that help you grow)
Comprehensive Details: Includes tech stack, implementation phases, key features, and portfolio impact
Actionable Outlines: Step-by-step project breakdowns with specific deliverables

✍️ AI Cover Letter Generator

Personalized Content: Generates job-specific cover letters using your résumé
Template Support: Accepts custom templates (.txt, .docx, .pdf)
Professional Formatting: Creates ready-to-use cover letters
One-Click Copy: Easy copy-to-clipboard functionality
Real-time Progress: Live progress updates during generation

⚡ Performance Features

Request Caching: Intelligent request deduplication to prevent duplicate processing (60-second TTL)
Streaming Responses: Server-Sent Events (SSE) for real-time progress updates
Graduate Student Detection: Automatically detects if a job requires graduate-level education
Multi-format Support: Handles PDF, DOCX, and TXT files for templates

🛠️ Tech Stack

Frontend Technologies

HTML5 - Markup language for structuring web pages
CSS3 - Styling and modern design with custom responsive layouts
JavaScript (Vanilla JS) - Client-side scripting for interactive UI
Jinja2 - Server-side templating engine for dynamic HTML generation
Server-Sent Events (SSE) - Real-time streaming updates for progress tracking

Backend Technologies

Python 3.8+ - Primary programming language
FastAPI - Modern, fast web framework for building APIs
Uvicorn - ASGI server for running FastAPI applications
python-multipart - File upload handling

AI & Machine Learning

OpenAI API - GPT models for:
- Skill extraction from résumés and job descriptions
- Course and project recommendations
- AI-powered cover letter generation
OpenAI Python SDK - Official Python client for OpenAI API

Database

MongoDB - NoSQL database for storing course data
PyMongo - MongoDB Python driver
MongoDB Atlas - Cloud database hosting

Document Processing

PDF Processing:
- PyMuPDF (fitz) - Fast and efficient PDF parsing
- PyPDF2 - PDF text extraction
- pdfminer.six - Advanced PDF parsing and text extraction
OCR (Optical Character Recognition):
- pytesseract - Text extraction from scanned documents
- Pillow (PIL) - Image processing for OCR
Word Documents:
- python-docx - Microsoft Word (.docx) file processing

Data Processing & Utilities

pandas - Data manipulation and analysis for course data
chardet - Character encoding detection
argparse - Command-line argument parsing

Development & Deployment

python-dotenv - Environment variable management
Git & GitHub - Version control and code repository
Render - Cloud hosting platform for deployment

Additional Features

Request Deduplication - In-memory caching to prevent duplicate processing
Progress Callbacks - Real-time progress tracking during long-running operations
Error Handling - Comprehensive error handling with detailed error messages
File Validation - Automatic file type validation and error reporting

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Frontend (Web UI)                     │
│              HTML/CSS/JavaScript + Jinja2 Templates         │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                    FastAPI Backend Server                    │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │   Resume     │  │  Job Skills  │  │   Scoring    │      │
│  │  Extraction  │  │  Extraction  │  │   Engine    │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
│                                                               │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │   Course     │  │   Project    │  │   Cover      │      │
│  │ Recommender  │  │ Recommender  │  │   Letter     │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└───────────┬───────────────────┬───────────────────┬────────┘
            │                   │                   │
            ▼                   ▼                   ▼
    ┌──────────────┐    ┌──────────────┐    ┌──────────────┐
    │   OpenAI     │    │   MongoDB    │    │   PDF Parser │
    │     API      │    │   Database   │    │   Libraries  │
    └──────────────┘    └──────────────┘    └──────────────┘

Data Flow

┌─────────────────────┐
│   Resume Text       │
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│         │
│   Extract Skills    │
└─────────────────────┘
         │
         ▼
         │
         └─────────────────┐
                           │
┌─────────────────────┐    │
│   Job Text          │    │
└─────────────────────┘    │
         │                 │
         ▼                 │
┌─────────────────────┐    │
│                     │    │
│   Extract Skills    │    │
└─────────────────────┘    │
         │                 │
         ▼                 │    
         │                 │
         └────────┬────────┘
                  │
                  ▼
         ┌─────────────────────┐
         │                     │
         │   Match & Score     │
         └─────────────────────┘
                  │
                  ▼
         ┌─────────────────────┐
         │ match_scores        │
         └─────────────────────┘
                  │
         ┌────────┴────────┐
         │                 │
         ▼                 │
┌─────────────────────┐    │
│                     │    │
│   Recommend         │    │
│   Courses           │    │
└─────────────────────┘    │
         │                 │
         ▼                 │   
         │                 │
         └────────┬────────┘
                  │
                  ▼
         ┌─────────────────────┐
         │                     │
         │   Recommend         │
         │   Projects          │
         └─────────────────────┘
                  │
                  │
                  ▼
         ┌─────────────────────┐
         │ final_report         │
         └─────────────────────┘

Input: User uploads PDF résumé and pastes job description
Parsing: PDF text extraction using multiple parsing methods
Skill Extraction: LLM extracts and categorizes skills from both documents
Comparison: Skills are compared to identify gaps
Scoring: Match scores calculated (overall, required, preferred)
Recommendations:
- Courses fetched from MongoDB based on missing skills
- Projects generated using LLM
Output: Comprehensive report with all recommendations

🚀 Usage

Web Interface

Visit the live application and:

Skill Gap Analyzer Tab:
- Upload your PDF résumé
- Paste the job description
- Click "Analyze Match"
- Watch real-time progress updates as the system processes your request
- View comprehensive skill gap analysis with:
  - Overall match score and coverage statistics
  - Required and preferred skills breakdown
  - Course recommendations (free and paid options)
  - Project recommendations (build with current skills vs. learn missing skills)
  - Graduate student job detection warnings
Cover Letter Generator Tab:
- Upload your PDF résumé
- Paste the job description
- Optionally upload a cover letter template (.txt, .docx, or .pdf)
- Click "Generate Cover Letter"
- Watch real-time progress updates during generation
- Copy the generated cover letter with one click

API Endpoints

Homepage

GET /

Returns the main web interface with tabs for Skill Gap Analyzer and Cover Letter Generator

Skill Gap Analysis

POST /analyze (SSE Streaming)

Content-Type: multipart/form-data
Parameters:
- resume (file, required): PDF resume file
- job_text (string, required): Job description text
Response: Server-Sent Events stream with progress updates and final JSON result
Features: Request caching, real-time progress tracking

curl -X POST "https://level-up-llm-skill-analyzer.onrender.com/analyze" \
  -F "resume=@your_resume.pdf" \
  -F "job_text=Your job description here"

Cover Letter Generation

POST /cover-letter (SSE Streaming)

Content-Type: multipart/form-data
Parameters:
- resume (file, required): PDF resume file
- job_text (string, required): Job description text
- template (file, optional): Cover letter template (.txt, .docx, or .pdf)
Response: Server-Sent Events stream with progress updates and final cover letter

curl -X POST "https://level-up-llm-skill-analyzer.onrender.com/cover-letter" \
  -F "resume=@your_resume.pdf" \
  -F "job_text=Your job description here" \
  -F "template=@template.txt"  # Optional

Health Check

GET /health

Returns service health status
Response: {"status": "healthy", "service": "Resume-Job Match Analyzer"}

📚 API Documentation

Response Format

Skill Gap Analysis Response (SSE Stream)

The /analyze endpoint returns Server-Sent Events (SSE) with the following message types:

Progress Messages:

{
  "type": "progress",
  "message": "Extracting skills from resume..."
}

Complete Response:

{
  "type": "complete",
  "data": {
    "overall_score": {
      "weighted_score": 75.5,
      "total_skills": 20,
      "matched_skills": 15
    },
    "required_skills": {
      "total_count": 10,
      "covered_count": 8,
      "match_score": 80.0,
      "covered_skills": ["Python", "FastAPI", "MongoDB"],
      "missing_skills": ["Docker", "Kubernetes"]
    },
    "preferred_skills": {
      "total_count": 10,
      "covered_count": 7,
      "match_score": 70.0,
      "covered_skills": ["React", "TypeScript"],
      "missing_skills": ["AWS", "GraphQL", "Redis"]
    },
    "course_recommendations": {
      "free_courses": [
        {
          "title": "Course Title",
          "platform": "Udemy",
          "duration": "10 hours",
          "difficulty": "Intermediate",
          "cost": "Free",
          "link": "https://...",
          "skills_covered": ["Python", "FastAPI"],
          "description": "...",
          "why_efficient": "..."
        }
      ],
      "paid_courses": [
        {
          "title": "Course Title",
          "platform": "Coursera",
          "duration": "20 hours",
          "difficulty": "Advanced",
          "cost": "$49.99",
          "link": "https://...",
          "skills_covered": ["Docker", "Kubernetes"],
          "description": "...",
          "why_efficient": "..."
        }
      ],
      "skill_coverage": {...},
      "coverage_percentage": 75
    },
    "project_recommendations": {
      "Track Name": [
        {
          "title": "Project Name",
          "description": "...",
          "difficulty": "Intermediate",
          "estimated_time": "2-3 weeks",
          "tech_stack": ["Python", "FastAPI", "MongoDB"],
          "key_features": ["Feature 1", "Feature 2"],
          "skills_demonstrated": ["Skill 1", "Skill 2"],
          "technologies": ["Tech 1", "Tech 2"],
          "project_outline": "High-level overview",
          "implementation_phases": [
            {
              "phase": "Phase 1: Setup",
              "details": "Detailed steps..."
            }
          ],
          "portfolio_impact": "...",
          "bonus_challenges": ["Challenge 1", "Challenge 2"]
        }
      ]
    },
    "is_grad_student_job": false
  }
}

Error Response:

{
  "type": "error",
  "message": "Error description here"
}

Cover Letter Response (SSE Stream)

The /cover-letter endpoint returns Server-Sent Events (SSE) with similar structure:

Progress Messages:

{
  "type": "progress",
  "message": "Extracting personal information from resume..."
}

Complete Response:

{
  "type": "complete",
  "data": {
    "cover_letter": "Generated cover letter text here..."
  }
}

Interactive API Documentation

FastAPI automatically generates interactive API documentation:

Swagger UI: Available at /docs when running locally
ReDoc: Available at /redoc when running locally

📁 Project Structure

level-up-llm-skill-analyzer/
├── app_fastapi.py              # Main FastAPI application with SSE streaming
├── extract_skills.py            # Résumé skill extraction using LLM
├── extract_job_skills.py        # Job description skill extraction using LLM
├── score_skills_match.py        # Skill matching and scoring engine
├── recommend_courses.py         # Course recommendation engine (MongoDB integration)
├── recommend_projects.py        # Project recommendation generator using LLM
├── generate_report.py           # Report generation orchestrator
├── generate_cover_letter.py     # AI-powered cover letter generator
├── pdf_resume_parser.py         # Multi-method PDF text extraction (PyMuPDF, PyPDF2, pdfminer)
├── skill_normalization.py      # Skill normalization and canonicalization
├── check_setup.py               # Setup verification script
├── requirements.txt             # Python dependencies
├── README.md                    # Project documentation
├── CTP_template.txt            # Cover letter template example
├── scripts/
│   └── load_courses_to_mongo.py # Script to load course data into MongoDB
└── templates/
    ├── index.html               # Main UI template with tabs
    ├── skill_analyzer.html      # Skill analysis form template
    └── cover_letter.html        # Cover letter form template

👥 Authors

Ahmed Ali - GitHub
Surjo Barua - GitHub
Jiayu Ouyang - GitHub
Ibnan Hasan

Made with ❤️ for students and job seekers

⭐ Star this repo | 🌐 Live Demo

Name		Name	Last commit message	Last commit date
Latest commit History 94 Commits
scripts		scripts
templates		templates
.gitignore		.gitignore
CTP_template.txt		CTP_template.txt
README.md		README.md
app_fastapi.py		app_fastapi.py
check_setup.py		check_setup.py
extract_job_skills.py		extract_job_skills.py
extract_skills.py		extract_skills.py
generate_cover_letter.py		generate_cover_letter.py
generate_report.py		generate_report.py
pdf_resume_parser.py		pdf_resume_parser.py
recommend_courses.py		recommend_courses.py
recommend_projects.py		recommend_projects.py
requirements.txt		requirements.txt
score_skills_match.py		score_skills_match.py
skill_normalization.py		skill_normalization.py

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🚀 skillbridge.AI

📋 Table of Contents

🎯 Overview

Key Objectives

🎥 Demo

Watch the Demo Video

✨ Features

🔍 Skill Gap Analyzer

📚 Course Recommendations

🛠️ Project Recommendations

✍️ AI Cover Letter Generator

⚡ Performance Features

🛠️ Tech Stack

Frontend Technologies

Backend Technologies

AI & Machine Learning

Database

Document Processing

Data Processing & Utilities

Development & Deployment

Additional Features

🏗️ System Architecture

Data Flow

🚀 Usage

Web Interface

API Endpoints

Homepage

Skill Gap Analysis

Cover Letter Generation

Health Check

📚 API Documentation

Response Format

Skill Gap Analysis Response (SSE Stream)

Cover Letter Response (SSE Stream)

Interactive API Documentation

📁 Project Structure

👥 Authors

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