In recent years, GitHub Actions (GHA) has emerged as the leading platform for Continuous Integration and Continuous Deployment (CI/CD) within the GitHub ecosystem, offering developers seamless workflow automation. However, as with other CI/CD tools, GHA workflows are susceptible to ”smells” which are suboptimal practices that can lead to technical debt, reduced maintainability, and performance issues. This thesis investigates the prevalence and nature of these workflow smells in GHA configurations. Through an extensive analysis of commit histories from 83 projects, we identify common patterns of frequent changes in GHA workflows that may indicate the presence of smells. We propose a set of potential GHA-specific smells, develop a tool to automatically detect these smells, and validate our findings through a contribution study involving 40 pull requests to open-source projects. After qualitatively analysing the comments on 32 pull requests we settle on 7 confirmed GHA workflows smells, including one novel smell previously unrecognised in the literature, This work contributes to improving the quality of GHA workflows and offers insights for developers to optimise their CI/CD processes. Finally, this research was also accepted as a paper to the SCAM 2024 conference.