In Progress: Defining the next dev role. Connection: Linking code to career. Action: Analyzing the journey.
Code Review: Deep dives into the portfolio.
Resume Ready: Analyzing the path ahead.
The Blueprint: From skills to success.

Inspiration

We realized that students often spend years learning technologies without understanding whether they actually match industry requirements. Most learning platforms are generic and don’t provide personalized guidance based on real job roles. This gap between learning and employability inspired us to build SkillGap AI—a system that acts like a personal AI career mentor.

What it does

SkillGap AI analyzes a user’s resume and GitHub profile to evaluate their job readiness. It identifies missing skills for a target role, generates personalized learning roadmaps, recommends projects, and provides a career readiness score to guide improvement.

How we built it

We built SkillGap AI using a full-stack and AI-powered approach: Frontend: Next.js, React.js, Tailwind CSS Backend: Node.js, Express.js AI Engine: Google Gemini API (2.5 Flash) for skill extraction and analysis Data Sources: Resume parsing + GitHub API integration Architecture: REST API-based communication between frontend and backend Processing: In-memory file handling for secure resume analysis

Challenges we ran into

Extracting structured skills from unstructured resumes Reducing hallucinations and making AI outputs job-relevant Mapping user skills accurately to real-world job roles Designing a meaningful and consistent “career readiness score” Handling different resume formats and GitHub activity variations

Accomplishments that we're proud of

Built a fully functional AI career guidance system Successfully integrated resume + GitHub-based evaluation Created personalized learning roadmaps instead of generic suggestions Designed an intuitive career readiness scoring system Developed a privacy-first system with no data storage

What we learned

Real-world skill mapping is more complex than expected Prompt engineering significantly impacts AI accuracy Resume data is highly unstructured and needs careful parsing Combining multiple data sources improves recommendation quality Even small systems can create meaningful career impact when well-designed

What's next for SkillGap

AI-powered mock interviews Real-time job matching system LinkedIn profile optimization tools Internship and course recommendation engine Company-specific preparation modules Multi-language support for wider accessibility