SkilllsFuture Browser Extension

One thing we would like to share is that this project was more challenging than we first expected because it was not just about building an AI feature. A big part of the work was making sure the system used the right text from a job posting. For example, on MyCareersFuture, the page includes extra text such as profile prompts and job-match suggestions. At first, that kind of text could accidentally get mixed into the analysis, which would make the AI extract the wrong skills. We had to spend time improving the extraction logic so that the system focused only on the employer’s real responsibilities, requirements, qualifications, and tools. Another interesting part was learning that AI matching is not always a simple “correct or wrong” answer. Sometimes a job skill can be close to more than one official SkillsFuture skill, so we decided to show confidence scores and other possible matches instead of pretending the system is always perfectly certain. My teammate and I also learned a lot from connecting different parts together: Python backend, official datasets, AI models, embeddings, and a browser extension. Each part worked differently, so getting them to communicate smoothly was one of the most satisfying parts of the project. We would also like to acknowledge that this project was built with support from the open-source Python ecosystem, including FastAPI, pandas, NumPy, sentence-transformers, and transformers. These tools made it possible for us to build something meaningful within a short time. Overall, this project reminded us that AI is most useful when it is combined with careful human judgement, good data, and thoughtful design.