From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills

Large language model (LLM) agents increasingly rely on reusable skills: capability packages that combine instructions, control flow, constraints, and tool calls. In current agentic systems, however, skills are still represented as text, mainly SKILL.md-style documents whose machine-usable evidence remains embedded largely in natural-language descriptions. As a result, skill-centered agentic systems face a representation problem: both managing skill collections and using skills during agent execution require reasoning over invocation interfaces, execution structure, and concrete side effects, but these signals are often entangled in a single textual surface. An explicit representation of skill knowledge may therefore help make these artifacts easier for machines to acquire and leverage. Drawing on Memory Organization Packets, Script Theory, and Conceptual Dependency from Schank and Abelson's classical work on cognitive linguistic representation, we introduce what is, to our knowledge, the first structured representation for agent skill artifacts that disentangles skill-level scheduling signals, scene-level execution structure, and logic-level action/resource-use evidence: the Scheduling-Structural-Logical (SSL) representation. We instantiate SSL with an LLM-based normalizer and evaluate it along two aspects: Skill Routing before a task, and Skill Execution during it. In Skill Routing, indexing candidate skills by their SSL views improves nDCG@10 over the raw view ($+2.69$ pp hard, $+2.95$ pp easy; 6.6% and 6.9% relative); In Skill Execution, an action card that annotates the task's source passages with SSL relations improves verified task success over raw execution ($+5.00$ pp on DeepSeek-V4.1-Flash, $+7.89$ pp on GLM-5.3-Flash; 6.8% and 13.8% relative) at no additional model calls. Together, the two aspects show that an explicit, source-grounded SSL representation helps both when structure selects what the agent sees and when it governs how the agent acts. We position SSL as a practical step toward more inspectable, reusable, and operationally actionable skill representations, and as a building block for future skill registries and agent runtimes.