Snakemake is a popular open-source tool to create reproducible and scalable data analyses. Workflows are described via a Python-based language that defines steps in the workflow as rules, and these are then used by Snakemake to construct and execute a work plan to yield the desired output. Re-calculation of existing results is avoided where possible, so you can add or update input data, then efficiently generate an updated result. Workflows can be seamlessly scaled to server, cluster, grid and cloud environments without the need to modify the workflow definition. A key appeal of workflow systems like Snakemake is that our workflows can be re-used, published and re-mixed as open-source code. We look at WorkflowHub.eu and other on-line resources for workflow sharing, and how we can best prepare our own workflows to be most effectively re-usable. Attendees must have a working knowledge of how to use the Linux BASH command line – our 1-day “Linux for bioinformatics” course is a suitable background.