SkillSpec: Consensus‑Gated Agent Skill Evolution via Representation Specialization

Natural-language skills are textual procedural memories through which large language model (LLM) agents retain reusable task knowledge without updating model weights. Existing methods typically treat skills as either static artifacts or monolithic documents optimized using aggregate validation scores as feedback. However, representing a skill as a monolithic document restricts optimization to its textual content, without explicitly modeling the structure through which procedural knowledge is retrieved and executed. We identify a key distinction between learning what knowledge to retain and determining how to organize it: textual updates should first be validated through execution evidence, after which the retained knowledge should be structured according to its procedural dependencies and retrieval requirements. To this end, we introduce SkillSpec, a two-phase framework comprising consensus-gated evolution and representation specialization. In the consensus-gated phase, complementary editing intents generate complete candidate skills, which are accepted as updates only when paired evaluations reach consensus: an overall improvement over the current skill with no performance degradation in any individual evaluation run. In the specialization phase, signals of process and redundancy sensitivity derived from the optimization trajectory guide the selection of a flat, graph, or hybrid representation while preserving the validated backbone as an immutable lineage source. Across six benchmarks and several frontier language models, SkillSpec improves average success rate over prior state-of-the-art skill optimization methods by 4.8%. These results demonstrate that reliable skill evolution and representation specialization address complementary objectives: deciding what knowledge to retain and how to structure it for inference.