SSL Representation: Skill Artifact Structuring Overview
Figure 1: Overview of the SSL representation. A text-heavy skill artifact is converted by a source-grounded normalizer into three layers: a scheduling record for invocation-level signals, a structural graph of execution scenes, and a logical graph of atomic actions and resource-use evidence. The structured view remains paired with the original source document and supports downstream tasks such as Skill Discovery and Risk Assessment.
Paper context
Paper title: From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills Abstract: LLM agents increasingly rely on reusable skills, capability packages that combine instructions, control flow, constraints, and tool calls. In most current agent systems, however, skills are still represented by text-heavy artifacts, including SKILL.md-style documents and structured records whose machine-usable evidence remains embedded largely in natural-language descriptions. This poses a challenge for skill-centered agent systems: managing skill collections and using skills to support agent both require reasoning over invocation interfaces, execution structure, and concrete side effects that 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 linguistic knowledge 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 and resource-use evidence: the Scheduling-Structural-Logical (SSL) representation. We instantiate SSL with an LLM-based normalizer and evaluate it on a corpus of skills in two tasks, Skill Discovery and Risk Assessment, and superiorly outperform the text-only baselines: in Skill Discovery, SSL improves MRR from 0.573 to 0.707; in Risk Assessment, Passages referencing this figure: from surface wording (Schank, 1972 ) . Together, these theories provide a reference point for disentangling skills into goal-level context, ordered execution trajectory, and primitive operations. Guided by this reference point, SSL is designed to represent machine-facing skill artifacts. An overview of the resulting representation and its role in downstream skill-centered tasks is illustrated in Figure 1 . Figure 1: Overview of the SSL representation. A text-heavy skill artifact is converted by a source-grounded normalizer into three layers: a scheduling record for invocation-level signals, a structural graph of execution scenes, and a logical graph of atomic actions and resource-use evidence. The structured view remains paired with the original source document and supports downstream tas ce wording (Schank, 1972 ) . Together, these theories provide a reference point for disentangling skills into goal-level context, ordered execution trajectory, and primitive operations. Guided by this reference point, SSL is designed to represent machine-facing skill artifacts. An overview of the resulting representation and its role in downstream skill-centered tasks is illustrated in Figure 1 . Figure 1: Overview of the SSL representation. A text-heavy skill artifact is converted by a source-grounded normalizer into three layers: a scheduling record for invocation-level signals, a structural graph of execution scenes, and a logical graph of atomic actions and resource-use evidence. The structured view remains paired with the original source document and supports downstream tasks such as
