jmsc-fit-positioning
Use when judging whether a manuscript fits 《管理科学学报》 (Journal of Management Sciences in China) — a 数理/定量 (mathematical / quantitative) management-science journal whose deliverable is a model plus provable properties plus an algorithm. Use when the draft is regression coefficients + policy talk, or survey-SEM, to detect OFF-FIT and re-route to 管理世界 / 南开管理评论 / 金融研究. Use before drafting and whenever a reviewer asks whether the contribution is in method or in conclusion.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmsc-fit-positioning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 匹配度与赛道定位（jmsc-fit-positioning） ## 触发时机 - 不确定该投《管理科学学报》还是别的刊 - 手头是回归结果 + 政策建议，想"包装成模型"硬投 - 问卷-SEM / 案例研究，纠结要不要数理刊 - reviewer 反复问"贡献在方法还是在结论" ## 核心：看交付物形态，不看话题 本刊认的是**方法贡献与数理严谨性**。判断只看一件事——**去掉经验数据后还剩什么**： - 剩下**一个模型 + 可证明的性质 + 算法** → 对口（数理型）。 - 剩下**一个因果断言 + 政策建议** → 不对口（经验型），改投。 ## 对照表：对口 vs 改投 | 交付物 | 是否对口 | 去向 | |--------|----------|------| | 决策模型 + 命题/定理 + 证明 | 高度对口 | 本刊主线 | | 优化/博弈/机制设计 + 算法复
What does the jmsc-fit-positioning skill do?
Use when judging whether a manuscript fits 《管理科学学报》 (Journal of Management Sciences in China) — a 数理/定量 (mathematical / quantitative) management-science journal whose deliverable is a model plus provable properties plus an algorithm. Use when the draft is regression coefficients + policy talk, or survey-SEM, to detect OFF-FIT and re-route to 管理世界 / 南开管理评论 / 金融研究. Use before drafting and whenever a reviewer asks whether the contribution is in method or in conclusion.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmsc-fit-positioning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.