Modeling of command-line interface (CLI) interaction has enabled flexible, execution-free output presentation. However, current approaches struggle to model inputs with complex compositions and inputs whose execution behavior depends on system characteristics. This is due to a lack of shell input-output (ShIO) data in the training distributions used by the models in these approaches. To address this data gap, we present ShIOEnv, a Gymnasium-compatible Bash shell environment for command synthesis and system-grounded execution behavior capturing. To concentrate synthesis on productive regions of the state-action space, we temporally abstract argument construction into grammar-derived options, thereby constraining synthesis to syntactically valid arguments. We introduce a self-supervised irreducibility signal to approximate the proportion of arguments that contribute to the observed execution behavior, serving as a measure of information density for each input. Using ShIOEnv, we curate and release 2.1M input-output pairs for modeling feedback from Bash command execution. We find that models trained on grammar-constrained datasets with higher maximum irreducibility achieve greater accuracy when modeling the execution behavior of user-sourced inputs than prior execution-free baselines.