skill-issue is an AI-powered platform that helps college students stop getting ghosted by employers. Students upload their resume and paste job posting links, then our system analyzes exactly where they stack up against requirements, predicting their likelihood of landing the role and identifying specific skill gaps. Instead of blindly applying to hundreds of jobs and wondering why they're getting rejected, students get actionable insights on which skills to develop and a clear roadmap to become competitive for their dream positions. It transforms the anxiety-inducing job hunt into a strategic, data-driven process.
- Nathaniel Davis (nad239@pitt.edu)
- Noah Kim (nrk81@pitt.edu)
- Models from Anthropic, OpenAI, and Google
So as a team of a computer scientist and computer engineer, we know that the job market is becoming quite difficult for people in our position. And so we really need every advantage we can get when it comes to screening and writing resumes. And so we built this to help us detect what gaps we had in our skills. And our chances of of getting through screenings.
The app. Accepts the content of a job posting either by scraping the URL or from manually pasted content and then has an AI and machine learning algorithms come up with an analysis to calculate your percentage of getting screened your strengths, weaknesses, and a road map of how to improve.
Nathaniel coded the app combination with Claude code and Noah used Figma to design and style the app.
I would say that coming up with an idea that was feasible to complete in twenty four hours while still being interesting and fitting our track of predicting the unpredictable. Was a major challenge for us. Another major issue was scraping at job board sites is often hard to do. So we ended up going through a scraping service And if all else fails, we allow the user to manually paste the content.
When the algorithm is run on the exact same data, we get very similar results, which means the percentage calculated is not willy nilly. It actually has some standing. Clean graphical user interface. Responsive design, and like, clean intuitive navigation and usage. Simplistic design. Not made with AI.
In terms of the hackathon process in itself, Noah and Nathaniel both learned to take frequent breaks as well as to rest and get a good night sleep so we can code and still work very well the next day. We learned that far more than coming into the hackathon with an impressive idea It's the cofounders and team that really makes up like, how well you're going to do rather than just idea and execution.
Next up for scale issue is having better integration for nontech related jobs. As it's somewhat suited for that right now. Second is to work on some optimizations to get it running faster and then improving the algorithms and prompts for even better output.