Ecological Skill Libraries: Co-Evolving Skills, Routers, and Library Topology

Large language model agents increasingly rely on reusable skills that encode procedural knowledge, executable operations, and task-specific guidance. As skill libraries grow, their performance depends on skill quality, routing competition, context consumption, redundancy, conflicts, and complementary interactions. Existing approaches primarily optimize skills and routing decisions separately, which can misestimate each skill's marginal contribution and reduce library performance when the candidate population changes. We introduce EcoSkill, an ecological framework that models a skill library as a co-adapting community. Given a task distribution and an execution budget, EcoSkill jointly evolves skill content, applicability triggers, routing policies, library topology, and lifecycle decisions. Its central mechanism assigns ecological credit based on each skill's marginal contribution to community performance, incorporating task utility, context and routing costs, unique coverage, and inter-skill synergy. The framework estimates this credit using sampled coalition counterfactuals and approximate Shapley-style attribution, avoiding exhaustive evaluation of skill coalitions. The resulting signal coordinates skill mutation, retention, migration, merging, and extinction while preserving specialized and complementary capabilities. We evaluate EcoSkill across libraries of increasing scale, controlled skill corruptions, and task-distribution shifts using task performance, routing accuracy, context consumption, and conflict rate. This formulation maintains adaptive skill libraries by selecting members according to their contribution to the agent ecosystem.