Even with complete knowledge of the underlying wiring diagram using electron microscopy and the neuronal activity through $in~vivo$ recordings, we can only ever correlate neuron function with participation in the various non-random motifs that have been characterized [1, 2]. Causation, however, can only be proven by manipulations of connectivity, which can experimentally only be pursued on coarse-grained scales or low specificity. Higher resolution and specificity can to date only be reached using simulation techniques. Here, we present a novel framework [3] for rapid connectome manipulations of detailed, biologically realistic network models in SONATA format [4], an open standard for representing large-scale networks of any complexity on the level of individual synapses. 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 (Fig. 1A). The resulting connectomes can be readily simulated using any simulator supporting SONATA, allowing the systematic and reproducible characterization of causal effects of manipulations on the network activity. We employed the framework to manipulate the connectome of a detailed anatomical [5] and physiological [6] model of the rat somatosensory cortex (Fig. 1B) in two particular ways. First, we increased specific VIP+ interneuron connectivity based on trends found in MICrONS data [7]. Second, we created simplified [8] but otherwise equivalent connectomes by progressively removing 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. Taken together, our framework serves as a flexible starting point for manipulating connectomes in a systematic and reproducible way, which can be easily extended and adapted to individual use cases. Together with the ability to actually simulate such manipulated connectomes, this represents a powerful tool for fully understanding the role of connectivity in shaping network function.