We present TacticCraft, a natural language-driven approach for tactical conditioning of StarCraft II AI agents. While current state-of-the-art agents achieve impressive win rates, they lack the ability to adapt their gameplay styles based on human tactical preferences. Our method bridges this gap by freezing a pre-trained policy network (DI-Star) and attaching lightweight adapter modules to each action head, conditioned on a tactical tensor derived from natural language directives. This tensor encodes strategic preferences across multiple dimensions, enabling intuitive control over agent behavior. By training these adapters with KL divergence constraints, we ensure the policy maintains its original competitive strength while exhibiting diverse tactical styles. Empirical evaluations demonstrate that TacticCraft successfully modulates agent behavior across tactical dimensions—including aggression levels, expansion patterns, and technology preferences—while preserving up to 95\% of the base model's win rate against strong opponents. Most importantly, our approach enables non-technical users to customize agent behavior through simple language commands like "play aggressively" or "focus on economic growth," offering practical strategy customization with minimal computational overhead (less than 3\% parameter increase). TacticCraft represents a significant advancement toward AI agents that can be strategically directed through natural language while maintaining high-performance gameplay in complex real-time strategy environments.