$\textsf{SKILL.nb}$: Selective Formalization and Gated Execution for Durable Agent Workflows

AI agents increasingly convert past experience into reusable artifacts such as code, workflows, and procedural memories. Reuse improves efficiency, but these artifacts can also carry obsolete assumptions across interface drift, repeated repairs, or changing task distributions, especially in web automation. We introduce SKILL.nb, a framework for governing reusable agent workflows through evidence-calibrated lifecycle policies. Its key mechanism is selective formalization: execution evidence decides which workflow steps should become executable code, which should remain natural-language-guided, and when those choices should be revised. SKILL.nb stores workflows as auditable, versioned notebooks that interleave natural-language guidance, multi-language executable cells, validation gates, fallback paths, and multimodal evidence such as outputs, screenshots, and error traces. At runtime, SKILL.nb performs gate-conditioned execution: unlike all-or-nothing scripts, each step can execute code when its gates validate, or fall back locally to an NL procedure or step intent when drift invalidates the executable realization. Cell-level records of attempted realizations, gate outcomes, outputs, screenshots, and fallbacks make both workflow updates and executions auditable. On WebArena-Verified, SKILL.nb achieves 53.7% single-round success, improving over the strongest baseline by 3.9 percentage points. Across three re-executions, it retains 91.7% of initially successful tasks, 15.5 points above the next best method. Under bounded repair, it recovers 72.9% of subsequent failures while limiting post-repair regressions to 4.2%, compared with 15.0-17.0% regression rates for persistent baselines. It also leads the compared methods on Mind2Web cross-website and cross-domain splits. In a realistic GitLab migration test, SKILL.nb preserves performance when reusing frozen state learned on GitLab 15.7, with frozen-versus-fresh target-version gaps of only -1.7 points on GitLab 16.11 and +0.6 points on GitLab 18.9; the least-degraded persistent baseline drops by 10.6-11.1 points. These results identify lifecycle governance and gate-conditioned execution as reliability axes beyond one-shot task success. Code, data, and evaluation scripts will be released after review.