The four Ls of systemic risk are well-known to macroprudential regulators: loss, leverage, liquidity and linkages. Adding three more aspects of the job of a systemic risk engineer to the list: lore, likelihood and light - corresponding to data collection issues, probabilistic approaches and insights to guide policy - brings the total to seven. Through the lens of these alliterative seven pillars we take a high-level tour of the modelling tools we use to monitor and measure systemic risk. In each of the stages of a financial crisis: the build-up of imbalances, the resilience of the system when a shock occurs, and the aftermath in which interconnections can transmit ]and amplify the initial shock, different models provide different perspectives and insights. Interactive Jupyter notebooks coded in python and R, hosted on Azure, demonstrate each model. Examples include: back-testing early warning indicators, network construction and analysis, statistical stress tests, stock flow consistent and agent-based economic models, sentiment analysis and the role of machine learning. A cloud-based repository of executable notebooks will be made available for experimentation in a web browser.