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Get Started Free →Use when targeting 《中国管理科学》(Chinese Journal of Management Science — 中国优选法统筹法与经济数学研究会与中科院科技战略咨询研究院主办的管理科学与工程类月刊, 中科院主管) or deciding whether a Chinese management-science manuscript fits this venue. Encodes the journal's fit, framing, abstract/citation house style, fee note, official-submission re-check, and desk-reject heuristics.
.claude/skills/brycewang-stanford-chinese-journal-of-management-science/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 26% | 0% |
《中国管理科学》由中国科学院主管，中国优选法统筹法与经济数学研究会与中国科学院科技战略咨询研究院共同主办，为管理科学与工程类学术月刊。以运筹优化、决策分析、风险管理、金融工程、供应链与运营、数据驱动管理与复杂系统为核心，强调模型设定、命题/定理或算法层面的方法贡献，是国内管理科学与工程方向的重要 CSSCI / 中文核心刊物。
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-soft-science（《中国软科学》） / chinese-journal-of-management（《管理学报》） / chinese-public-administration（《中国行政管理》） / chinese-review-of-financial-studies（《金融评论》）。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，从官方来源锚点开始核验，并在回答中说明核验日期。更数理旗舰、定理推演更厚 → journal-of-management-sciences-china（《管理科学学报》）；系统工程/复杂系统方法 → systems-engineering-theory-and-practice（《系统工程理论与实践》）；工业工程与工程管理落点 → journal-of-industrial-engineering-and-engineering-management（《管理工程学报》）。
text【匹配度】高 / 中 / 低（一句话理由） 【目标期刊】《中国管理科学》 【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】<投稿系统/匿名/摘要100-200字/关键词/顺序编码参考文献/版面费等> 【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 | 30,908 | 20,898 | -32% | 1 | 1 | 0% | 3,501 | 3,637 | +4% | 0 | 0 | — |
case-02 | fail→pass | 24,482 | 21,188 | -13% | 1 | 1 | 0% | 3,135 | 3,850 | +23% | 0 | 0 | — |
case-03 | fail→pass | 30,340 | 23,391 | -23% | 1 | 1 | 0% | 3,200 | 3,907 | +22% | 0 | 0 | — |
case-04 | fail→pass | 27,490 | 20,898 | -24% | 1 | 1 | 0% | 3,254 | 3,912 | +20% | 0 | 0 | — |
case-05 | pass→pass | 28,228 | 25,094 | -11% | 1 | 1 | 0% | 2,982 | 4,332 | +45% | 0 | 0 | — |
case-06 | pass→pass | 32,912 | 32,077 | -3% | 1 | 1 | 0% | 3,397 | 4,951 | +46% | 0 | 0 | — |
case-07 | fail→pass | 25,251 | 20,071 | -21% | 1 | 1 | 0% | 3,096 | 3,900 | +26% | 0 | 0 | — |
case-08 | pass→pass | 24,729 | 17,258 | -30% | 1 | 1 | 0% | 2,573 | 3,330 | +29% | 0 | 0 | — |
case-09 | pass→pass | 30,778 | 22,356 | -27% | 1 | 1 | 0% | 3,220 | 3,678 | +14% | 0 | 0 | — |
case-10 | pass→pass | 37,062 | 23,466 | -37% | 1 | 1 | 0% | 3,313 | 4,365 | +32% | 0 | 0 | — |
case-11 | fail→pass | 12,557 | 10,406 | -17% | 1 | 1 | 0% | 1,008 | 2,213 | +120% | 0 | 0 | — |
case-12 | pass→pass | 27,712 | 14,606 | -47% | 1 | 1 | 0% | 2,895 | 3,455 | +19% | 0 | 0 | — |
case-13 | pass→pass | 22,171 | 22,929 | +3% | 1 | 1 | 0% | 3,103 | 4,739 | +53% | 0 | 0 | — |
case-14 | fail→pass | 21,444 | 17,118 | -20% | 1 | 1 | 0% | 2,848 | 3,397 | +19% | 0 | 0 | — |
case-15 | pass→pass | 27,584 | 28,880 | +5% | 1 | 1 | 0% | 3,067 | 4,452 | +45% | 0 | 0 | — |
case-16 | pass→pass | 30,637 | 35,064 | +14% | 1 | 1 | 0% | 3,211 | 5,516 | +72% | 0 | 0 | — |
case-17 | fail→fail | 27,978 | 18,876 | -33% | 1 | 1 | 0% | 3,086 | 3,680 | +19% | 0 | 0 | — |
case-18 | fail→pass | 28,417 | 17,554 | -38% | 1 | 1 | 0% | 2,989 | 3,507 | +17% | 0 | 0 | — |
case-19 | pass→pass | 25,284 | 21,917 | -13% | 1 | 1 | 0% | 2,577 | 3,707 | +44% | 0 | 0 | — |
case-20 | pass→pass | 27,896 | 33,218 | +19% | 1 | 1 | 0% | 3,025 | 4,291 | +42% | 0 | 0 | — |
case-21 | fail→fail | 42,897 | 39,923 | -7% | 1 | 1 | 0% | 6,305 | 7,817 | +24% | 0 | 0 | — |
case-22 | pass→pass | 14,932 | 18,454 | +24% | 1 | 1 | 0% | 1,422 | 3,584 | +152% | 0 | 0 | — |
case-23 | fail→fail | 19,748 | 17,121 | -13% | 1 | 1 | 0% | 1,944 | 3,210 | +65% | 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. 23 cases were attempted. The headline lift of +35 percentage points is the difference between those two pass rates over the 23 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.