Beyond the Final Skill: Revaluing Skill Evolution History After Executor Replacement

Skill evolution produces a sequence of reusable Skill versions, but deployment usually keeps only the version selected by the source model. When that model is replaced, an earlier version may work better with the new executor. Existing transfer approaches typically reuse the source-selected Skill or optimize a new target-specific Skill, leaving this historical choice unexplored. We propose HEIRS (History-preserving Executor-aware Inheritance through Revaluation and Selection), which preserves the Skill history, keeps a compact set of distinct candidates, and selects one unchanged archived Skill using separate target-side screening and confirmation sets. Across Qwen3.5 migrations and a DeepSeek-to-GLM cross-family transfer, HEIRS improves selected migrations: mean Qwen ALFWorld success rises by 4.5 percentage points over endpoint transfer, and DeepSeek-to-GLM Spreadsheet success rises by 11.5 points. Across Qwen Spreadsheet and ALFWorld migrations, HEIRS uses 52.4% fewer logical target evaluations than fresh SkillOpt while avoiding target-side Skill optimization. The code implementation is available at https://anonymous.4open.science/r/HEIRSanonymous.4open.science/r/HEIRS.