SkillAlignAI 🚀

An intelligent resume–job description alignment engine that evaluates candidate fit using semantic skill matching, domain awareness, and explainable weighted scoring.

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🎯 Problem

Most hiring systems:

• ❌ Provide no feedback after rejection
• ❌ Use naive keyword matching
• ❌ Fail to capture real skill relationships
• ❌ Do not explain why a candidate is rejected

Result → Candidates cannot improve effectively.

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💡 Solution

SkillAlignAI builds a transparent ATS-style evaluation system that:

• Separates skills, domain, and scoring logic
• Uses semantic relationships (parent–child–sibling skills)
• Applies confidence-based weighted scoring
• Generates clear, structured rejection insights

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⚙️ Core Capabilities

• Resume + Job Description analysis (text or PDF)
• Semantic skill matching (not just keywords)
• Multi-layer scoring:
  • Required Match %
  • Preferred Match %
  • Domain Alignment %
• Confidence-based scoring (direct, inferred, fuzzy)
• Structured rejection analysis + suggestions

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🧠 What Makes It Different

Unlike basic ATS clones, this system:

• ✅ Handles skill hierarchies (React → JavaScript)
• ✅ Supports sibling inference (TensorFlow ↔ PyTorch)
• ✅ Uses confidence scoring (1.0 / 0.5 / 0.3)
• ✅ Separates domain vs skill matching
• ✅ Prevents false positives (Java ≠ JavaScript)
• ✅ Handles typos using fuzzy matching

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🧮 Scoring Logic

Base Score = 0.8 × Required Match + 0.2 × Preferred Match
Final Score = Base × (0.7 + 0.3 × Domain Match)

Interpretation:

• Required Match → Core competency
• Preferred Match → Competitive advantage
• Domain Match → Contextual alignment

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⚡ Example Evaluation

Input:

Resume: html, css, ml
JD: machine learning 

Output:

• Required Match: 0%
• Domain Match: 100%
• Final Score: 0%

Interpretation:
Candidate shows interest in ML domain but lacks required technical skills.

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🏗️ System Architecture

Pipeline

Frontend → API → Parsing → Normalization → Matching → Scoring → Explanation → Response

Modules

1. Resume Parser – Extracts skills, projects, and experience
2. JD Analyzer – Identifies required and preferred skills
3. Normalization Layer – Standardizes skill variations
4. Semantic Matching Engine – Handles parent-child & sibling relationships
5. Scoring Engine – Computes weighted alignment scores
6. Domain Detection Engine – Extracts contextual domain from raw text
7. Explanation Generator – Produces structured feedback

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🛠️ Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• AI Integration: Gemini API
• Testing: Pytest
• Utilities: python-dotenv

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📁 Project Structure

SkillAlignAI/
│
├── backend/
│   ├── .env
│   ├── agent_controller.py
│   ├── agents.py
│   ├── app.py
│   ├── contracts.py
│   ├── fallback_explainer.py
│   ├── gemini_client.py
│   ├── jd_analyzer.py
│   ├── rejection_engine.py
│   ├── rejection_report.py
│   ├── report_generator.py
│   ├── resume_parser.py
│   │
│   ├── utils/
│   │    ├── normalizer.py
│   │    ├── pdf_parser.py
│   │    ├── role_map.py
│   │    ├── skill_categories.py
│   │    ├── skill_map.py
│   │    └── skill_weights.py      
│   │
│   └── tests/
│       └── test_analyze_application.py
│
├── frontend/
│   ├── index.html              # User interface
│   ├── script.js               # API calls + UI logic
│   └── style.css               # Basic styling
│
├── docs/
│   ├── api_contract.md         # API request/response specification
│   ├── problem.md              # Problem definition
│   ├── user_flow.md            # User interaction flow
│   └── solution.md             # System design explanation
│
├── Procfile                    # Render deployment configuration
├── README.md
├── .gitignore
└── requirements.txt

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🚀 Installation

git clone https://github.com/tanuluthra4/SkillAlignAI.git
cd SkillAlignAI
pip install -r requirements.txt

Create a .env file inside backend/ and configure required API keys.

▶️ Usage

pip install -r requirements.txt
python -m backend.app 

Then open frontend/index.html in your browser

🔌 API Endpoint

POST/analyze

Request Body

{
    "resume_text": "string",
    "job_description_text": "string"
}

Example Response

{
  "match_score": 76,
  "required_match": 70,
  "preferred_match": "N/A",
  "domain_match_percentage": "N/A",
  "matched_skills": ["python"],
  "missing_skills": [],
  "score_explanation": {
    "formula": "Base: 0.8 × required + 0.2 × preferred\nFinal: Base × (0.7 + 0.3 × domain)",
    "final_score": 76
  }
}

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🌐 Live Demo

Frontend: https://skillalignai-1.onrender.com
Backend API: https://skillalignai.onrender.com/analyze

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📈 Roadmap

• 🔥 Embedding-based semantic matching (next upgrade)
• 📊 Multi-candidate comparison dashboard
• 📄 Advanced resume parsing (structured sections)
• 🤖 AI-generated interview insights
• 🔐 Authentication & recruiter panel

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🧠 Key Learnings

• Importance of separating skills vs domain vs scoring
• Handling ambiguity in NLP pipelines
• Designing explainable AI systems
• Preventing bias and score inflation

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🤝 Contribution

1. Fork the repository.
2. Create a feature branch:

git checkout -b feature-name

3. Commit your changes:

git commit -m "Add feature"

4. Push to the branch:

git push origin feature-name

5. Open a pull request

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📌 Why This Project Matters

Most job application systems provide little to no feedback after rejection. SkillAlignAI bridges this gap by:

• Explaining why a candidate may be rejected
• Providing actionable improvement insights
• Simulating how ATS systems evaluate resumes

This makes the system useful not only as a tool, but also as a learning aid for job seekers.