Even with complete knowledge of the underlying wiring diagram based on electron microscopy and of neuronal activity through in vivo recordings, we can only ever correlate neuron function with the structure of connectivity. Causation, however, can only be proven by manipulations of connectivity, which can experimentally only be pursued on coarse-grained scales or with low specificity. Higher resolution and specificity can, to date, only be reached using simulation techniques. Here, we present a novel framework for rapid connectome manipulations of detailed, biologically realistic network models in SONATA format, the standard for large-scale network models. Manipulations can be targeted to entire models, specific sub-networks, or even single neurons, ranging from insertion or removal of specific motifs to complete rewiring based on stochastic connectivity models at various levels of complexity. Important use cases include wiring a model in a biologically realistic way based on given features of connectivity, rewiring an existing connectome while preserving certain aspects of connectivity, and transplanting connectivity characteristics from one connectome to another. The resulting connectomes can be readily simulated using any simulator supporting SONATA, allowing systematic and reproducible characterization of causal effects of manipulations on network activity. We employed the framework to manipulate the connectome of a detailed model of the rat somatosensory cortex in two ways: first, we increased specific interneuron connectivity based on trends found in MICrONS data; second, we progressively removed higher-order features of E-to-E connectivity. We ran a series of network simulations and found specific changes in activity causally linked to these manipulations.