Given the critical role of specialty contractors in the construction industry, as the part who is involved in manual construction tasks and therefore is faced with hazardous attributes more than any others, finding innovative ways to enhance the safety performance of these types of contractors is promising. To address this challenge, the objective of this study is to utilize recently developed attribute-based risk assessment method and create a safety risk database, which then will be used to develop a safety-risk assessment tool (a mobile application) that can be operated by designers, project managers, and particularly specialty contractors to identify and assess risk of construction activities. The study focuses on electrical works, as this sector is one of the most hazardous trades in the construction industry (e.g. contact with electricity is the fourth main cause of fatalities in this industry). To build upon a reliable dataset, we obtained 323 accident reports from the OSHA IMIS database to create attribute-based safety risk models. Then, the attributes of construction objects and work tasks that cause injuries for electrical contractors are identified through a content analysis of injury reports and the relative risks associated with each attribute is quantified by recording the outcome of injuries. To predict potential outcome of injuries, a principal component analysis (PCA) and generalized linear model (GLM) conducted on the attribute based database and the predictive power of developed models is calculated using a rank probability skill score (RPSS). Ultimately, a mobile application has been generated to issue the findings.