SkillWorld: World-Indexed Skill Graphs with Governed Self-Evolution

Composing skills from growing libraries for task execution requires reasoning about dependencies, alternatives, and conflicts among them. The information needed to infer these relations is distributed across skill descriptions, and their applicability may vary with the task and execution context. This calls for a reusable skill graph that supports context-dependent interpretation while remaining consistent as the underlying skill knowledge evolves. We present SkillWorld, which derives skill relations from typed skill descriptions and a shared ontology to provide a relational knowledge layer over large skill libraries. At runtime, it evaluates relation applicability in the current task and execution context. As the skill library grows, SkillWorld evolves the shared ontology to incorporate new skill knowledge and re-derives relations to maintain consistency with the updated model. On SkillsBench, SkillWorld with evolved static graphs outperforms graph-based and retrieval baselines, achieving gains of 11.1–15.3 percentage points in task success rate over the strongest baseline for each evaluated model. On ALFWorld, the full system produces 43.5–72.4% fewer invalid environment actions than BM25 skill retrieval, while achieving 85.1–85.8% task success within 15 environment actions across two models. We also deploy and evaluate SkillWorld in a large company's internal services. From the deployment traces, we select 36 tasks that are difficult to complete without skills. SkillWorld achieves a mean success rate of 72.2% over three runs.