From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills
- Published
- Source
- arXiv
- Paper number
- 165
- Field
- Agents / Skills
- arXiv ID
- 2604.24026
Key points
- Agent skills are mostly represented as text-heavy artifacts that mix the invocation interface, execution structure, and tool use inside natural-language descriptions.
- The implicit nature of skill information makes it difficult for automated systems to parse, validate, reliably reuse, and efficiently discover relevant capabilities in large skill repositories.
- Security risk assessment before execution is hard for third-party skills because the unstructured text format makes risk evidence difficult to inspect and audit.
- To make skill information explicit from unstructured SKILL.md documents, we propose Scheduling-Structural-Logical (SSL), a typed three-layer JSON graph representation.
- SSL separates skill properties into a Scheduling Layer for skill-level interfaces, a Structural Layer for scene-level execution steps, and a Logical Layer for atomic actions and resource evidence.
- We developed an LLM-based normalizer, using DeepSeek-V3.2, that converts SKILL.md files into the SSL schema and achieves high fidelity through strict grounding on the source artifact plus validation and retries.
Paper links
External research summaries. These are not HDATF publications or measured product results.