From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills
Introduces SSL, a three-layer structured skill representation, improving skill retrieval and risk assessment with 15-20% performance gains.
Key Findings
Methodology
This work draws on cognitive linguistic models—Memory Organization Pack, Script Theory, and Conceptual Dependency—to decompose skill texts into three layers: scheduling (interface signals), scene (execution phases), and logical (atomic actions/resources). An LLM-based normalizer maps SKILL.md files into this schema, ensuring source-grounded, verifiable structures. Experiments on 6,184 skills, 431 intent queries, and 252 risk-labeled skills evaluate SSL's effectiveness in retrieval (MRR@50) and risk detection (macro F1). Results show significant improvements over text-only baselines, demonstrating the value of explicit structure for downstream tasks.
Key Results
- In skill discovery, MRR@50 rose from 0.649 to 0.729, indicating better matching accuracy with structured representations.
- In risk assessment, macro F1 increased from 0.409 to 0.509, showing enhanced ability to identify operational risks.
- Structured SSL features, especially scene and logic layers, contributed most to performance gains, validating the design.
Significance
This research addresses the core bottleneck of unstructured skill descriptions, enabling more transparent, reusable, and secure skill management. By transforming natural language into interpretable, source-grounded structures, it facilitates automated skill discovery, validation, and safety analysis, crucial for deploying reliable AI agents at scale.
Technical Contribution
The paper introduces SSL, a novel three-layer schema inspired by cognitive theories, combined with an LLM-based normalization pipeline. It advances the state-of-the-art by providing a source-supported, disentangled representation that improves retrieval and risk detection. The approach bridges linguistic models and practical AI systems, offering a scalable, verifiable framework for skill management.
Novelty
This is the first systematic effort to decompose agent skills into explicit, source-grounded, three-layer structures, moving beyond flat text or embedding-based methods. It uniquely integrates cognitive linguistics with modern LLM techniques, enabling interpretable and operationally actionable skill representations.
Limitations
- SSL relies heavily on the quality of the LLM normalizer; complex or ambiguous skills may produce unsupported or incomplete structures, affecting downstream tasks.
- The schema's generalizability to multi-modal or highly dynamic skills remains untested, requiring further extension.
- Real-world deployment faces challenges like data privacy, computational costs, and robustness against adversarial inputs.
Future Work
Future directions include extending SSL to multi-modal skill descriptions, enabling dynamic updates, and integrating reasoning capabilities. Additionally, exploring automated validation and security mechanisms will be critical for real-world applications, aiming to build comprehensive, trustworthy skill ecosystems.
AI Executive Summary
The rapid evolution of large language models (LLMs) has transformed AI agents from simple tool-callers into complex, multi-step reasoning systems. However, a persistent challenge remains: how to effectively represent and manage reusable skills that encompass instructions, control flow, and tool calls. Existing approaches rely heavily on natural language descriptions, such as SKILL.md files, which are inherently unstructured and difficult for machines to analyze reliably. This hampers efficient skill discovery, reuse, and security assessment.
To address this, the authors propose SSL, a structured, three-layer representation inspired by cognitive linguistic theories. The scheduling layer captures invocation signals like intent and input/output contracts. The scene layer models execution phases, such as preparation and verification, forming a directed graph of scenes. The logical layer decomposes actions into primitive steps with resource and effect evidence. An LLM-based normalizer converts unstructured skill texts into this schema, ensuring source-grounded, verifiable structures.
Experiments on a large corpus of 6,184 skills, with 431 intent queries and 252 risk-labeled skills, demonstrate SSL’s effectiveness. In skill discovery, structured representations improved retrieval metrics (MRR@50) from 0.649 to 0.729. In risk assessment, macro F1 scores increased from 0.409 to 0.509, indicating better detection of operational risks. These results confirm that explicit, source-grounded structures facilitate better search, review, and safety analysis.
This work significantly advances the field by providing a practical framework for managing complex agent skills. It bridges linguistic theories and modern AI, offering a scalable, interpretable, and verifiable approach. The authors plan to extend SSL to multi-modal descriptions, dynamic updates, and reasoning tasks, aiming to build more trustworthy and autonomous AI systems. Despite current limitations, such as reliance on LLM normalization quality, this research lays a foundation for more transparent, reusable, and secure skill ecosystems in AI agents.
Deep Dive
Plain Language Accessible to non-experts
想象你在一家大型工厂工作，工厂里有许多不同的生产线，每条生产线负责不同的任务，比如装配、包装或检验。每条线都有自己的流程和步骤，但如果只用一张纸写着流程说明，工人们很难快速找到需要的步骤，也难以检查每个环节是否安全或高效。现在，假设你用一种特殊的标签，把每个生产线的任务拆成三个部分：第一部分是任务的目标和入口（调度层），告诉工人这条线的主要任务；第二部分是生产的不同阶段（场景层），比如准备、装配、检验；第三部分是每个具体操作（逻辑层），比如拧螺丝、贴标签、检查质量。这样一来，工人可以更快找到需要的流程，也能更容易发现潜在的问题。这种拆分就像本文提出的SSL，把复杂的技能变成清晰、易检的三层结构，帮助机器人和系统更智能、更安全地工作。
ELI14 Explained like you're 14
想象你在学校参加各种活动，比如足球、音乐和科学实验。如果只用一张纸写着活动说明，老师和同学们都觉得很难理解和检查。现在，假设我们用一种特别的方法，把每个活动拆成三个部分：第一部分是活动的目标和主要内容（调度层），告诉你这个活动是为了锻炼身体还是培养兴趣；第二部分是活动的不同阶段（场景层），比如准备、进行、总结；第三部分是每个具体的动作（逻辑层），比如踢球、弹钢琴、做实验。这样一来，老师和同学们都能更清楚每个活动的结构，也能更容易发现问题或改进。这就像论文中提出的SSL，把复杂的技能拆成三个容易理解的层次，让机器人和电脑更聪明、更安全地帮我们工作和学习。
Abstract
Large language model (LLM) agents increasingly rely on reusable skills: capability packages that combine instructions, control flow, constraints, and tool calls. In current agent systems, however, skills are still represented by text-heavy artifacts, mainly SKILL{.}md-style documents whose machine-usable evidence remains embedded largely in natural-language descriptions. As a result, skill-centered agent 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 SSL-derived representations in two tasks, Skill Discovery and Risk Assessment. The experiment shows that SSL significantly outperforms the text-only baselines: in Skill Discovery, MRR@50 improves from 0.649 to 0.729; in Risk Assessment, macro F1 improves from 0.409 to 0.509. These findings suggest that an explicit, source-grounded structure can make agent skills easier to search and review, positioning SSL as a practical step toward more inspectable, reusable, and operationally actionable skill representations, rather than a finished standard or end-to-end skill-management mechanism.