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Get Started Free →Use when targeting 《中国农村经济》(China Rural Economy — 中国社会科学院农村发展研究所主办、CASS 主管的三农经济旗舰月刊, 1985 年创刊, 双向匿名、零审稿/版面费) or deciding whether a Chinese 三农 econ manuscript fits this venue. Encodes the journal's fit, framing, fee-free anonymous-review policy, abstract/keyword/citation house style, official-submission re-check, and desk-reject heuristics.
.claude/skills/brycewang-stanford-china-rural-economy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 12% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 57% | 0% |
《中国农村经济》由中国社会科学院农村发展研究所主办、中国社会科学院主管，1985 年创刊，是三农经济领域的旗舰月刊。主发表关于农业、农村和农民问题的经济学学术论文，重视土地制度、农业经营、粮食安全、农民收入与消费、农村金融、乡村治理、集体经济、农业绿色转型与数字乡村等议题。它与同所主办的姊妹刊《中国农村观察》(china-rural-survey) 形成分工：本刊更偏经济学实证与政策机制，《观察》更偏制度调查、农村社会与多学科观察。
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-industrial-economics（《中国工业经济》） / china-public-administration-review（《公共管理评论》） / china-rural-survey（《中国农村观察》） / china-soft-science（《中国软科学》）。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。偏农村社会观察、制度田野与多学科材料可转 china-rural-survey（《中国农村观察》）；偏农业生产技术效率可转 journal-of-agrotechnical-economics（《农业技术经济》）；综合农业政策可转 issues-in-agricultural-economy（《农业经济问题》）。
text【匹配度】高 / 中 / 低（一句话理由） 【目标期刊】《中国农村经济》 【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/数据代码公开/参考文献等> 【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 | 32,142 | 25,782 | -20% | 1 | 1 | 0% | 3,969 | 4,939 | +24% | 0 | 0 | — |
case-02 | fail→pass | 26,858 | 19,501 | -27% | 1 | 1 | 0% | 3,564 | 4,006 | +12% | 0 | 0 | — |
case-03 | fail→pass | 30,201 | 15,349 | -49% | 1 | 1 | 0% | 3,744 | 3,942 | +5% | 0 | 0 | — |
case-04 | fail→pass | 27,962 | 20,134 | -28% | 1 | 1 | 0% | 3,069 | 3,639 | +19% | 0 | 0 | — |
case-13 | fail→pass | 28,226 | 24,337 | -14% | 1 | 1 | 0% | 2,987 | 4,704 | +57% | 0 | 0 | — |
case-11 | fail→pass | 24,824 | 23,785 | -4% | 1 | 1 | 0% | 3,084 | 4,220 | +37% | 0 | 0 | — |
case-12 | fail→pass | 27,065 | 20,319 | -25% | 1 | 1 | 0% | 2,683 | 3,734 | +39% | 0 | 0 | — |
case-05 | fail→pass | 30,440 | 22,385 | -26% | 1 | 1 | 0% | 3,012 | 4,565 | +52% | 0 | 0 | — |
case-06 | pass→pass | 26,098 | 19,194 | -26% | 1 | 1 | 0% | 2,587 | 4,010 | +55% | 0 | 0 | — |
case-07 | fail→pass | 22,543 | 17,315 | -23% | 1 | 1 | 0% | 2,820 | 3,661 | +30% | 0 | 0 | — |
case-08 | fail→pass | 31,127 | 21,752 | -30% | 1 | 1 | 0% | 3,379 | 4,007 | +19% | 0 | 0 | — |
case-09 | fail→pass | 29,985 | 12,533 | -58% | 1 | 1 | 0% | 3,126 | 3,541 | +13% | 0 | 0 | — |
case-10 | pass→pass | 26,604 | 16,741 | -37% | 1 | 1 | 0% | 2,848 | 3,419 | +20% | 0 | 0 | — |
case-14 | fail→pass | 25,473 | 22,913 | -10% | 1 | 1 | 0% | 3,325 | 4,433 | +33% | 0 | 0 | — |
case-15 | fail→pass | 30,606 | 24,739 | -19% | 1 | 1 | 0% | 3,302 | 4,089 | +24% | 0 | 0 | — |
case-16 | fail→pass | 21,088 | 13,769 | -35% | 1 | 1 | 0% | 1,768 | 3,229 | +83% | 0 | 0 | — |
case-17 | fail→pass | 31,401 | 32,048 | +2% | 1 | 1 | 0% | 3,197 | 4,108 | +28% | 0 | 0 | — |
case-18 | fail→pass | 29,756 | 16,686 | -44% | 1 | 1 | 0% | 3,071 | 3,498 | +14% | 0 | 0 | — |
case-19 | fail→pass | 26,889 | 24,783 | -8% | 1 | 1 | 0% | 3,547 | 4,298 | +21% | 0 | 0 | — |
case-20 | fail→fail | 30,866 | 32,872 | +6% | 1 | 1 | 0% | 4,356 | 5,963 | +37% | 0 | 0 | — |
case-21 | fail→fail | 10,969 | 15,403 | +40% | 1 | 1 | 0% | 1,296 | 3,155 | +143% | 0 | 0 | — |
case-22 | fail→fail | 36,246 | 41,818 | +15% | 1 | 1 | 0% | 5,126 | 6,886 | +34% | 0 | 0 | — |
case-23 | pass→pass | 29,891 | 32,261 | +8% | 1 | 1 | 0% | 4,672 | 6,404 | +37% | 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 +74 percentage points is the difference between those two pass rates over the 23 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.