SKILL.state: Scalable Long-Horizon Agent Skills

As Large Language Models (LLMs) increasingly act as autonomous agents executing long-running agent skills. Existing agent runtimes maintain execution by appending observations, actions, and reasoning traces to an ever-growing conversation history, resulting in increasing prompt sizes, higher token costs, and degraded long-horizon performance. We present SKILL.state, a runtime architecture that replaces conversational history with an explicit execution state. At each step, the model receives only the immutable skill specification, the current execution state, and the latest observation. Intermediate reasoning is discarded after producing a structured state update, preventing prompt growth with execution history. Across multiple execution horizons, models, and runtime baselines, SKILL.state substantially reduces prompt size and token consumption while maintaining execution performance comparable to or better than history-based approaches. Our results demonstrate that explicit execution state is a simple and effective runtime abstraction for scalable long-horizon agent skills.