In curriculum development, syllabus creation is an important activity. The need for a syllabus repository and data extraction is essential for creating a new syllabus. Faculties do this job manually by adding topics from their intelligence as well as analyzing syllabuses of the same course available on the search engine. Because it is a time-consuming job, this research aims to propose and implement a text-analyzing tool. The methodology is used, first to create a syllabus repository of computer science courses, do data extraction & normalization, analyzes the contents of different syllabuses, and suggests the contents to the faculties. This tool is experimented on the sample for creating the syllabus of C Programming. The result of the tool is that it suggests the topics that can be included in the syllabus to the faculties. The time and effort required to create a syllabus are reduced by using this tool. In the future weightage to the syllabuses can be given for classification that will give more accurate results while analyzing syllabuses. This tool can be improved by using machine learning algorithms for creating syllabus repositories and data extraction. Semantic comparison can give more accurate results by creating a specific model for computer terminologies.