Developed and released iprophit v1.0.0 within the bioconda/bioconda-recipes repository, delivering a deep learning package for inducible prophage activity detection in DNA sequences. The work involved establishing a robust package structure with comprehensive metadata, build instructions, and automated testing commands to ensure reproducibility and ease of onboarding. Integrated essential dependencies such as PyTorch and Biopython to support model deployment within Conda environments, enabling seamless adoption by the bioinformatics community. Focused on Python and YAML for implementation and configuration, the contribution expanded the available toolkit for DNA sequence analysis by providing machine learning-driven prophage detection capabilities in research workflows.