Beyond Semantic Relevance: Grounding Skill Selection in Model-Specific Execution Experience

Reusable skills equip language-model agents with procedural knowledge for reasoning and problem solving. Access to a skill library, however, does not ensure that an agent can select the procedures it will benefit from. Skill descriptions express intended applicability, whereas their value depends on the executing model's capabilities and the question at hand. This mismatch creates a selection bottleneck for small and medium-sized models. We propose SkillCredit ValueFusion, which grounds semantic skill selection in the executor's own experience. Historical rollouts provide question-dependent estimates of skill gains. A lightweight value model then learns how the agent's current semantic preferences should correct those estimates, using within-question reward contrasts rather than a single best-skill label. Independent calibration adjusts the correction strength for each backbone and task domain. The resulting selector combines historical execution evidence with current judgment while keeping the language model and skill library fixed. Experiments across question-answering benchmarks and model backbones show significant overall improvements over agent self-selection and similarity-based retrieval. Further analyses expose complementary errors between semantic choice and historical credit, and show why their balance must be calibrated to the executor rather than imposed uniformly.