RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources

Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving tutorial videos and other multimodal human resources largely underused. We present Resource2Skill, a framework that distills multimodal resources—tutorial videos, repositories, articles, and reference artifacts—into executable skills for software agents. Resource2Skill organizes these skills as a hierarchical multimodal Skill Wiki, where each entry combines structured text, code, visual examples, metadata, and provenance. This design preserves complementary signals from different resources: videos capture temporal operations and visual effects, code captures executable tool patterns, and articles or artifacts provide conceptual and stylistic grounding. At inference time, agents retrieve and compose relevant skills from the wiki; when coverage is insufficient, the same construction operator can acquire new skills online. Across seven practical authoring domains, Resource2Skill improves average overall score by +11.9 percentage points over no-skill agents and outperforms strong harness baselines in 26 of 28 main-aggregate model–domain cells.

Introduction. Large language model agents [17] are increasingly expected to do more than answer questions: they must operate software, call tools, inspect intermediate results, and produce high-quality artifacts such as slide decks, spreadsheets, web pages, 3D scenes, CAD designs, and audio projects. In these settings, success often depends less on isolated factual knowledge than on reusable procedural know-how: how to decompose a goal, which tool or API pattern to use, what intermediate state to inspect, and how to recover when an operation fails. We refer to such reusable procedural knowledge as skills. In the agent era, skills provide a natural abstraction from experience to reusable expertise: they convert observations of how tasks are solved into compact instructions, code fragments, visual references, and execution recipes that can be repeatedly invoked by future agents. Recent skill-based agent systems [34] have already shown that such abstractions can be highly valuable.

Discussion / Conclusion. Resource2Skill distills multimodal human references into a structured, executable Skill Wiki shared by offline construction and controlled online gap filling. Across seven authoring domains and four backends, skill access improves artifact quality by +11.9 points over no-skill agents and beats two agentic-harness baselines in 26 of 28 main-aggregate cells. Our results show that distilling skills from human-created resources gives software agents reusable procedural knowledge that improves over both no-skill agents and strong agentic harnesses across diverse authoring domains.