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Get Started Free →Use when targeting 《公共管理评论》(China Public Administration Review — 教育部主管、清华大学主办的公共管理学术季刊, 2004 年创刊, 匿名评审) or deciding whether a Chinese public-administration/governance manuscript fits this journal. Encodes the journal's fit, framing, abstract/keyword house style, anonymous-review rules, official-submission re-check, and desk-reject heuristics.
.claude/skills/brycewang-stanford-china-public-administration-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -3% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 16% | 0% |
《公共管理评论》由教育部主管、清华大学主办，2004 年创刊，是公共管理领域的学术季刊（CSSCI 来源）。设原创论文、实践视野、主题述评、当季英文佳作评介、评论往来等栏目，覆盖公共治理理论、政策过程、行政改革、基层治理、社会组织与公共价值、数字政府、城市治理与应急管理等。偏好理论对话清楚、机制扎实、有方法规范的研究，兼收规范实证、案例比较与高质量述评。
这个 skill 是定位 / 选刊 / 改写框架工具，不替代该刊最新官方投稿须知。正式投稿前必须重新核对官网、采编系统或编辑部发布的最新模板。
china-economic-studies（《中国经济问题》） / china-industrial-economics（《中国工业经济》） / china-rural-economy（《中国农村经济》） / china-rural-survey（《中国农村观察》）。若这些刊物的读者对象更贴近，不要因为名称、地区或变量相似而强行投本刊。../../resources/source-basis.md 和 ../../resources/official-source-map.md，从其中的官方来源锚点或同一主办/出版体系入口开始核验，并在回答中说明核验日期。方法更厚、识别更干净的公共管理实证转 journal-of-public-management（《公共管理学报》）；行政管理制度与政策实务转 chinese-public-administration（《中国行政管理》）；治理实践取向转 governance-studies（《治理研究》）。
text【匹配度】高 / 中 / 低（一句话理由） 【目标期刊】《公共管理评论》 【选题标签】<最贴近的 2-3 个主题> 【方法证据】<当前方法是否够本刊标准> 【最大风险】<最可能导致退稿的一点> 【需核验官方要求】<栏目字数/匿名/摘要250字/关键词5-7/英文长摘要等> 【改投建议】<若不匹配，给出更合适期刊>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 27,139 | 16,641 | -39% | 1 | 1 | 0% | 3,385 | 3,456 | +2% | 0 | 0 | — |
case-02 | fail→pass | 25,052 | 23,632 | -6% | 1 | 1 | 0% | 3,743 | 4,023 | +7% | 0 | 0 | — |
case-03 | fail→fail | 33,397 | 22,500 | -33% | 1 | 1 | 0% | 3,373 | 3,766 | +12% | 0 | 0 | — |
case-04 | fail→pass | 31,774 | 19,891 | -37% | 1 | 1 | 0% | 3,299 | 3,204 | -3% | 0 | 0 | — |
case-05 | fail→pass | 32,136 | 22,344 | -30% | 1 | 1 | 0% | 3,077 | 3,767 | +22% | 0 | 0 | — |
case-06 | fail→pass | 29,400 | 18,147 | -38% | 1 | 1 | 0% | 3,117 | 3,631 | +16% | 0 | 0 | — |
case-07 | fail→pass | 27,836 | 16,697 | -40% | 1 | 1 | 0% | 3,053 | 3,396 | +11% | 0 | 0 | — |
case-08 | fail→pass | 27,400 | 23,279 | -15% | 1 | 1 | 0% | 2,921 | 4,203 | +44% | 0 | 0 | — |
case-09 | fail→pass | 30,074 | 24,395 | -19% | 1 | 1 | 0% | 3,115 | 3,917 | +26% | 0 | 0 | — |
case-10 | pass→pass | 28,806 | 22,974 | -20% | 1 | 1 | 0% | 2,945 | 3,923 | +33% | 0 | 0 | — |
case-11 | fail→pass | 26,432 | 17,319 | -34% | 1 | 1 | 0% | 2,528 | 3,406 | +35% | 0 | 0 | — |
case-12 | fail→pass | 25,741 | 27,136 | +5% | 1 | 1 | 0% | 3,221 | 4,161 | +29% | 0 | 0 | — |
case-13 | fail→pass | 26,039 | 25,902 | -1% | 1 | 1 | 0% | 2,804 | 4,290 | +53% | 0 | 0 | — |
case-14 | pass→pass | 26,247 | 21,857 | -17% | 1 | 1 | 0% | 2,787 | 3,851 | +38% | 0 | 0 | — |
case-15 | fail→pass | 22,001 | 18,350 | -17% | 1 | 1 | 0% | 2,200 | 3,469 | +58% | 0 | 0 | — |
case-16 | fail→fail | 27,076 | 24,843 | -8% | 1 | 1 | 0% | 2,790 | 4,205 | +51% | 0 | 0 | — |
case-17 | fail→pass | 27,688 | 23,572 | -15% | 1 | 1 | 0% | 3,085 | 3,811 | +24% | 0 | 0 | — |
case-18 | fail→fail | 26,723 | 21,362 | -20% | 1 | 1 | 0% | 2,916 | 3,677 | +26% | 0 | 0 | — |
case-19 | fail→pass | 25,877 | 16,222 | -37% | 1 | 1 | 0% | 2,830 | 3,303 | +17% | 0 | 0 | — |
case-20 | fail→fail | 22,847 | 16,945 | -26% | 1 | 1 | 0% | 2,118 | 3,242 | +53% | 0 | 0 | — |
case-21 | fail→pass | 25,842 | 21,260 | -18% | 1 | 1 | 0% | 2,673 | 3,723 | +39% | 0 | 0 | — |
case-22 | fail→pass | 67,188 | 32,459 | -52% | 1 | 1 | 0% | 8,238 | 5,497 | -33% | 0 | 0 | — |
case-23 | fail→fail | 36,471 | 27,794 | -24% | 1 | 1 | 0% | 5,693 | 5,479 | -4% | 0 | 0 | — |
case-24 | pass→pass | 20,922 | 17,048 | -19% | 1 | 1 | 0% | 1,934 | 3,130 | +62% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +67 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.