SkillNet is the first paper I've seen that treats agent skills as a network, a three-layer ontology that turns isolated skill files into a structured, composable network. Externalizing knowledge into files isn't enough. You also need to know how those files relate to each other. Layer 1 is a Skill Taxonomy. Ten top-level categories (Development, AIGC, Research, Science, Business, Testing, Productivity, Security, Lifestyle, Other), each broken into fine-grained tags: frontend, python, llm, physics, biology, plotting, debugging. This is the semantic skeleton. It answers "what domain does this skill belong to?" Layer 2 is the Skill Relation Graph. This is where SkillNet diverges from other skill repositories. Tags from Layer 1 get instantiated into specific skill entities (Matplotlib, Playwright, kegg-database, gget). Then four typed relations define how skills connect: > similar_to: two skills do the same thing. Matplotlib and Seaborn both plot. Enables redundancy detection. > belong_to: a skill is a sub-component of a larger workflow. Captures hierarchy and abstraction. > compose_with: two skills chain together. One's output feeds the other's input. This is the relation that enables automatic workflow generation. > depend_on: a skill can't run without a prerequisite. Enables safe execution by resolving the dependency graph before running anything. These four relations form a directed, typed multi-relational graph. Nodes are skills, edges are typed relationships. And the graph is dynamic. As new skills enter the system, LLMs infer relations from their metadata. Layer 3 is the Skill Package Library. Individual skills bundled into deployable packages. A data-science-visualization package contains Matplotlib, Seaborn, Plotly, GeoPandas with their relations pre-configured. You install a package, you get a coherent set of skills that already know how to compose with each other. This is a good example of what comes after a flat package manager. The paper also (you can test here skillnet.openkg.cn/) has a science case on a real research workflow: identifying disease-associated genes and candidate therapeutic targets from large-scale biological data. Without encoded relations, the agent figures out the research pipeline from scratch every time. With them, it receives a pre-structured execution plan. The agent still reasons about which genes to focus on and which pathways to investigate. But the pipeline architecture is given. So the skill metadata is actually doing routing work too. The metadata encodes the judgment a domain expert would make when choosing between tools. I also like this framing from the paper: Skills are how memory becomes executable and workflows become flexible. While the network effect and layered architecture is actually useful today, they also acknowledge this: "Low-frequency or highly tacit abilities are difficult to capture, particularly when they resist explicit linguistic description." From my short research career, I'd say the hardest parts are hypothesis generation, experimental design judgment, and interpreting ambiguous results etc. SkillNet handles the structured pipeline well; fetch data → analyze → validate → report. It doesn't handle the creative work where a scientist's (not just in science but in any white-collar field) intuition drives what's worth investigating in the first place. Skills encode "how to run the analysis." They don't encode "what's worth analyzing." That gap is where domain expertise still sits.