Conciseness

Dense expert guidance that assumes competence (never explains LoRA, Docker, or basic agent concepts) and every section carries non-obvious distilled rules. Not 5: several very long multi-clause bullets (e.g. 安全边界三条, the browser workflow lifecycle) could be tightened without losing content; not 3: there is little padding or explanation of known concepts.

Actionability

Concrete, executable guidance throughout for an instruction-only skill: enumerated pipelines (五步提炼管道, 六步浏览器生命周期, 五步离线整合), a concrete diff format (old_str → new_str), an explicit release gate (补丁非空、来源可追溯、保留集不退化、边界集确有改善；通过才灰度 release_to_canary), and a specified change-contract field list. Not 5: some sections are principle statements (核心原则) with no command or artifact-level detail, leaving minor gaps.

Workflow Clarity

Multi-step processes are clearly sequenced with explicit validation checkpoints and feedback loops: 发布门槛 with pass/fail branching, 独立回放验证 before publish, 失效与重学 fallback to full-agent mode, and checklists (实验 9-3's gate criteria, 开放式任务 evidence structure). Not 4: validation is explicit at every risky step rather than present with minor gaps, which matters given the self-modification/destructive nature of the domain.

Progressive Disclosure

Good section structure (何时使用 / 核心原则 / 实践模式 / 常见陷阱 / 配套代码 / 深度阅读) with references one level deep and clearly signaled in dedicated sections. Not 5: no bundle files exist (references/, scripts/, assets/ are absent), so the referenced paths (chapter9/…, book/chapter9.md) do not resolve inside the skill, and some dense patterns (轨迹验证器, 浏览器工作流) could arguably live in separate reference files; not 3: references are clearly signaled and organization is consistent.

Specificity

Lists multiple concrete capabilities — 三层轨迹验证, 四种进化方式（经验知识库、Prompt/Skill、程序/Harness、模型参数）, Prompt 自动优化, 轨迹编译为程序, 自我修改与安全门禁, 睡眠学习与长期分层评估 — giving comprehensive coverage of the skill's scope, matching the 'comprehensive coverage' anchor. Not 4: coverage is broad and each capability is named concretely, not just several with minor gaps.

Completeness

Explicitly answers both: what (从运行轨迹提取学习信号…四种进化方式…睡眠学习与长期分层评估) and when (「让 Agent 从运行经验中持续学习、构建自进化闭环时使用」 plus 触发词 list). Not 4: the when-clause is explicit with concrete trigger phrases, not merely present.

Trigger Term Quality

Explicit trigger list (持续进化、自进化、经验学习、轨迹验证器、自我修改、进化闭环) covers natural phrasings a user would say, but a few common variants like 持续学习 or 从错误中学习 are missing. Not 5 for the missing synonyms; not 3 because coverage is genuinely good and domain-appropriate rather than partial.

Distinctiveness Conflict Risk

A clear niche — agent self-evolution from run experience — with distinct triggers (轨迹验证器, 自我修改, 进化闭环) unlikely to fire for adjacent skills like generic memory or RPA skills. Not 4: the trigger terms are specific enough that overlap risk is minimal.