ConScribe: Repairing Learned Skills with Residual Updates for Changing Web Environments

Environment changes can leave learned skills partly useful and partly invalid. Adapting them requires deciding how new behavior should build on their inherited implementation. We introduce ConScribe, a skill-maintenance framework that generates residual updates and composes them with retained components, including those within the edited scope. Execution checks guide acceptance, wider repair, and reconstruction. On 24 public Mind2Web transfers, initial repair raises element-selection accuracy from 67.97% under static reuse to 75.78%. The last candidate outperforms information-aligned, source-preserving dual path by 9.90 percentage points. Author output decreases by 37.57% on 22 measured transfers, and online wall time decreases by 28.83% on 22 complete-record transfers. A controlled comparison across 48 Chromium transitions holds target AUC at 0.867 while source pass rises from 33.3% under local replacement to 100% under residual composition. Fixed-decomposition experiments also identify conditions where reconstruction costs less than repair. Together, these results show that the form of a skill update matters for target recovery, source retention, and adaptation cost, establishing the value of building target behavior on an inherited skill.