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Get Started Free →Use when targeting 《管理学报》(Chinese Journal of Management — 华中科技大学主办、教育部主管的全国性管理学月刊, 2004 年创刊, 双向匿名审稿) or deciding whether a Chinese management manuscript fits this venue. Encodes the journal's fit, framing, double-blind review, house style, official-submission re-check, and desk-reject heuristics.
.claude/skills/brycewang-stanford-chinese-journal-of-management/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 41% | 0% |
《管理学报》由华中科技大学主办、教育部主管，是全国性管理学学术刊物，2004 年创刊。覆盖工商管理、组织行为、人力资源与领导力、战略、创新创业、公司治理、营销与服务、运营与数字化转型，方法上兼收问卷、档案数据、实验、多案例与混合方法，并以"中国·实践·管理"取向著称、鼓励直面中国管理实践的理论建构。注意与新乡学院《管理学刊》（slug：journal-of-management）、《中国管理科学》（slug：chinese-journal-of-management-science）区分。与偏数理优化的《管理科学学报》相比，本刊更看重理论构念 + 机制推演 + 测量规范 + 中国情境解释。
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-rural-survey（《中国农村观察》） / china-soft-science（《中国软科学》） / chinese-journal-of-management-science（《中国管理科学》） / chinese-public-administration（《中国行政管理》）。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。量化模型 / 定理 / 算法是核心贡献 → journal-of-management-sciences-china（《管理科学学报》）；问卷/案例的管理理论建构更厚 → nankai-business-review（《南开管理评论》）/ management-review（《管理评论》）；偏政策评估的经管实证 → management-world（《管理世界》）。
text【匹配度】高 / 中 / 低（一句话理由） 【目标期刊】《管理学报》（华中科技大学主办） 【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】<匿名/摘要/关键词/参考文献/收费等> 【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-20 | fail→fail | 9,182 | 6,207 | -32% | 1 | 1 | 0% | 620 | 2,600 | +319% | 0 | 0 | — |
case-01 | fail→fail | 44,754 | 21,690 | -52% | 1 | 1 | 0% | 3,445 | 4,173 | +21% | 0 | 0 | — |
case-02 | fail→pass | 30,471 | 15,535 | -49% | 1 | 1 | 0% | 3,528 | 3,338 | -5% | 0 | 0 | — |
case-03 | fail→fail | 23,852 | 22,109 | -7% | 1 | 1 | 0% | 3,232 | 3,863 | +20% | 0 | 0 | — |
case-04 | pass→pass | 24,316 | 20,852 | -14% | 1 | 1 | 0% | 2,764 | 3,662 | +32% | 0 | 0 | — |
case-05 | fail→pass | 25,501 | 20,080 | -21% | 1 | 1 | 0% | 2,823 | 4,222 | +50% | 0 | 0 | — |
case-06 | pass→pass | 16,637 | 17,718 | +6% | 1 | 1 | 0% | 2,664 | 3,835 | +44% | 0 | 0 | — |
case-07 | pass→pass | 36,602 | 19,784 | -46% | 1 | 1 | 0% | 2,762 | 3,969 | +44% | 0 | 0 | — |
case-08 | pass→pass | 34,528 | 24,741 | -28% | 1 | 1 | 0% | 3,831 | 5,210 | +36% | 0 | 0 | — |
case-09 | pass→pass | 32,088 | 29,696 | -7% | 1 | 1 | 0% | 3,123 | 5,316 | +70% | 0 | 0 | — |
case-10 | fail→pass | 23,315 | 13,406 | -43% | 1 | 1 | 0% | 3,090 | 2,850 | -8% | 0 | 0 | — |
case-11 | fail→pass | 17,831 | 21,711 | +22% | 1 | 1 | 0% | 2,576 | 3,953 | +53% | 0 | 0 | — |
case-12 | fail→fail | 26,896 | 16,615 | -38% | 1 | 1 | 0% | 2,531 | 3,232 | +28% | 0 | 0 | — |
case-13 | fail→pass | 15,765 | 17,248 | +9% | 1 | 1 | 0% | 2,443 | 3,435 | +41% | 0 | 0 | — |
case-14 | pass→pass | 26,533 | 19,564 | -26% | 1 | 1 | 0% | 3,157 | 3,868 | +23% | 0 | 0 | — |
case-15 | fail→pass | 11,725 | 17,695 | +51% | 1 | 1 | 0% | 1,952 | 3,417 | +75% | 0 | 0 | — |
case-16 | fail→pass | 24,140 | 25,405 | +5% | 1 | 1 | 0% | 3,521 | 4,720 | +34% | 0 | 0 | — |
case-17 | fail→pass | 20,075 | 18,016 | -10% | 1 | 1 | 0% | 2,932 | 3,728 | +27% | 0 | 0 | — |
case-18 | pass→pass | 20,885 | 18,649 | -11% | 1 | 1 | 0% | 2,248 | 3,614 | +61% | 0 | 0 | — |
case-19 | fail→pass | 22,493 | 17,581 | -22% | 1 | 1 | 0% | 3,373 | 3,504 | +4% | 0 | 0 | — |
case-21 | pass→pass | 23,744 | 24,446 | +3% | 1 | 1 | 0% | 3,142 | 4,953 | +58% | 0 | 0 | — |
case-22 | fail→fail | 12,815 | 15,887 | +24% | 1 | 1 | 0% | 1,186 | 3,180 | +168% | 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. 22 cases were attempted. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.