Prompting is the primary interface through which users interact with large language models. Although individual prompting strategies have been extensively studied, it remains unclear whether combining multiple strategies can yield additive benefits. We present a systematic empirical study of prompt pattern composition across multiple LLMs and composition mechanisms, evaluating 18 prompt pattern categories over 7 benchmarks spanning 6 task types. Among the three models for which we exhaustively evaluate all 306 ordered pairs of functional prompt patterns, only 6.86%, 10.46%, and 4.25% of compositions outperform their stronger constituent prompt, respectively. We further find that prompt accumulation provides only limited early-stage gains, while local additive predictions increasingly overestimate actual performance as compositions grow longer. Moreover, selection-based mechanisms generally provide a more favorable performance profile than direct prompt accumulation. Overall, our results reveal a scoped less-is-more tendency in the prompt-composition settings studied here: effective use of multiple prompt strategies depends more on selecting suitable strategies than on simply accumulating additional instructions.