Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Use to judge whether a manuscript fits 《南开管理评论》 (Nankai Business Review) before investing in revision, and to re-route off-fit papers — math models to 管理科学学报, macro policy-evaluation to 管理世界 / 中国工业经济, capital-market governance to 会计研究 / 金融研究. Use when the topic, method, or framing might be a mismatch for a theory-building management journal.
.claude/skills/bilal140202-nbr-fit-positioning/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 3% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 96% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 3% | 0% |
战略管理、组织行为与人力资源、营销、公司治理、创新创业、运营管理均在收稿范围。两条细口径：
具体栏目设置以期刊最新投稿指南为准。
本刊并非只收问卷/实验/案例：用 CSMAR、Wind 等数据库做企业层面治理、创新、战略实证同样对口，但有三个前提：
| 稿子特征 | 更对口的刊 | 为什么 | |----------|-----------|--------| | 数理模型、定理证明、算法/最优化、运筹 | 管理科学学报 | 本刊偏行为/理论建构，非数理优化 | | 宏观/产业政策评估、政策含义为落点、准实验识别 | 管理世界 / 中国工业经济 | 本刊不以因果识别"干净"为评判 | | 公司治理偏资本市场反应、盈余、审计、财务后果 | 会计研究 / 金融研究 | 本刊治理偏管理学视角而非财务实证 | | 单学科细分、就事论事、无理论命题 | 学科专门刊 / 重做选题 | 本刊要理论贡献 | | 纯综述/思辨无经验证据 | 视主题另投 | 本刊以规范实证/案例为主 |
> 治理类要分流：董事会过程、高管认知、激励的行为机制留本刊；股价反应、盈余管理、审计费用偏财务，转会计/金融刊。
设想稿件：用 CSMAR 数据检验"董事会非正式层级→企业创新投入"。判断：构念关系明确（非正式层级、创新投入）、对话高阶梯队理论、可用工具变量与 PSM 处理内生性——对口本刊，进 nbr-theory-gap。若同一数据改问"某治理新规实施后创新是否提升"，以 DID 政策评估为主线，则改投管理世界更合适——数据相同，叙事主线决定去向。
【匹配度】高 / 中 / 低
【判定依据】构念关系□ 方法□ 理论落点□ 情境□
【若不对口】建议改投 <管理科学学报 / 管理世界 / 会计研究 / 金融研究> 因 <理由>
【下一步】对口 → nbr-theory-gap；不对口 → 改投| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,006 | 9,390 | -45% | 1 | 1 | 0% | 2,711 | 2,782 | +3% | 0 | 0 | — |
case-02 | fail→pass | 15,956 | 9,999 | -37% | 1 | 1 | 0% | 2,819 | 3,022 | +7% | 0 | 0 | — |
case-03 | fail→pass | 8,135 | 8,244 | +1% | 1 | 1 | 0% | 1,302 | 2,553 | +96% | 0 | 0 | — |
case-04 | fail→pass | 15,828 | 7,058 | -55% | 1 | 1 | 0% | 2,481 | 2,313 | -7% | 0 | 0 | — |
case-05 | fail→pass | 15,923 | 8,443 | -47% | 1 | 1 | 0% | 2,604 | 2,673 | +3% | 0 | 0 | — |
case-06 | fail→pass | 20,058 | 9,558 | -52% | 1 | 1 | 0% | 3,280 | 2,707 | -17% | 0 | 0 | — |
case-07 | fail→pass | 11,343 | 6,272 | -45% | 1 | 1 | 0% | 2,380 | 2,208 | -7% | 0 | 0 | — |
case-08 | pass→pass | 12,319 | 6,312 | -49% | 1 | 1 | 0% | 2,205 | 2,250 | +2% | 0 | 0 | — |
case-09 | fail→pass | 15,477 | 6,554 | -58% | 1 | 1 | 0% | 2,433 | 2,242 | -8% | 0 | 0 | — |
case-10 | fail→pass | 14,369 | 6,712 | -53% | 1 | 1 | 0% | 2,329 | 2,271 | -2% | 0 | 0 | — |
case-11 | fail→pass | 17,604 | 5,958 | -66% | 1 | 1 | 0% | 2,802 | 2,145 | -23% | 0 | 0 | — |
case-12 | fail→pass | 17,622 | 7,079 | -60% | 1 | 1 | 0% | 2,843 | 2,362 | -17% | 0 | 0 | — |
case-13 | fail→pass | 16,110 | 6,105 | -62% | 1 | 1 | 0% | 2,499 | 2,250 | -10% | 0 | 0 | — |
case-14 | fail→pass | 15,538 | 6,327 | -59% | 1 | 1 | 0% | 2,488 | 2,348 | -6% | 0 | 0 | — |
case-15 | fail→pass | 16,151 | 6,610 | -59% | 1 | 1 | 0% | 2,647 | 2,204 | -17% | 0 | 0 | — |
case-16 | fail→pass | 14,359 | 7,494 | -48% | 1 | 1 | 0% | 2,280 | 2,452 | +8% | 0 | 0 | — |
case-17 | fail→pass | 19,960 | 7,386 | -63% | 1 | 1 | 0% | 3,237 | 2,357 | -27% | 0 | 0 | — |
case-18 | pass→pass | 18,920 | 7,837 | -59% | 1 | 1 | 0% | 2,936 | 2,390 | -19% | 0 | 0 | — |
case-19 | pass→fail | 15,715 | 14,793 | -6% | 1 | 1 | 0% | 2,303 | 3,315 | +44% | 0 | 0 | — |
case-20 | pass→pass | 22,549 | 19,898 | -12% | 1 | 1 | 0% | 3,954 | 4,784 | +21% | 0 | 0 | — |
case-21 | fail→fail | 5,327 | 4,740 | -11% | 1 | 1 | 0% | 887 | 1,909 | +115% | 0 | 0 | — |
case-22 | fail→pass | 15,305 | 8,767 | -43% | 1 | 1 | 0% | 2,718 | 2,653 | -2% | 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 +73 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.