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学术引用核查Skill。要求每条引用必须定位到PDF原页，区分直接引用/间接引用，找不到原文则标注"待核"。触发词：引用核查/检查引用/citation check/核实文献/引用 fidelity
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, manuscript writing, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaf
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/
调查数据清洗Skill。处理调查数据（CGSS/CHIP/CSS等）时的标准化清洗流程，包括缺失值处理、变量编码统一、数据异常值检测。触发词：数据清洗/调查数据/codebook/数据清洗流程/问卷数据处理
双重差分(DID)实证审查Skill。做DID分析前必须检查平行趋势假设、画图可视化、报告违背情况。触发词：DID审查/双重差分检查/平行趋势/DiD reviewer/difference-in-differences
| name | citation-fidelity |
| description | 学术引用核查Skill。要求每条引用必须定位到PDF原页，区分直接引用/间接引用，找不到原文则标注"待核"。触发词：引用核查/检查引用/citation check/核实文献/引用 fidelity |
| version | 1.0 |
| metadata | {"openclaw":{"emoji":"📚","homepage":"https://github.com/SiyaoZheng/ai4ss-skills"}} |
本 Skill 确保每一条学术引用都能追溯到原始文献页面，区分直接引用与间接引用，对无法核实的引用标注**"待核"**，拒绝引用未经核实的二手文献。是构建可信研究记录的核心工具。
引用不是装饰，是证据。学术论文中的每条引用都应该是：
三类引用错误（必须避免）：
| 错误类型 | 描述 | 风险 |
|---|---|---|
| 幽灵引用 | 引用了但原文根本不存在 | 学术声誉灾难 |
| 转引错误 | 引用了二手文献而非原始来源 | 信息衰减失真 |
| 断章取义 | 只引用符合自己观点的局部表述 | 学术不端 |
收集引用列表 → 溯源每一项 → 分类标注 → 输出核查报告
从论文草稿或笔记中提取所有引用，整理为结构化列表：
| # | 引用 | 作者 | 年份 | 期刊/出版社 |
|---|------|------|------|-------------|
| 1 | Acemoglu et al. (2001) | | | |
| 2 | "技术扩散存在倒U型效应" (Wang, 2020, p.15) | | | |
对每条引用执行以下核查步骤：
① 找到原始文献
② 定位具体页码
③ 区分引用类型
| 类型 | 判断标准 | 标注方式 |
|---|---|---|
| 直接引用 | 逐字引用原文，超过40字须加引号 | 「直接引用,p.XX」 |
| 间接引用 | 用自己的话转述原文核心观点 | 「间接引用,p.XX」 |
| 待核 | 无法找到原文或无法确认准确性 | 「待核」 |
找不到原文时：
「待核：原文未找到，可能为转引」引用原文与论文表述不符时：
数据引用核查：
「世界银行WDI数据库，GDP per capita (NY.GDP.PCAP.CD)，1990-2020」最终输出结构化报告：
## 引用核查报告
**论文标题**：[待填]
**核查时间**：[日期]
**引用总数**：XX 条
### 核查结果汇总
| 状态 | 数量 | 说明 |
|------|------|------|
| ✅ 已核实（直接引用） | X | 附原文页码 |
| ✅ 已核实（间接引用） | X | 附原文页码 |
| ⚠️ 待核 | X | 需进一步溯源 |
| ❌ 无法核实 | X | 建议删除或替换 |
### 逐条核查明细
**1. Acemoglu et al. (2001) — ✅ 直接引用, p.12**
> "原始引文内容..."
> 原文位置：第X段第Y行
**2. Wang (2020) — ⚠️ 待核**
> 无法找到原始文献高度疑似二手转引，建议追溯原文或替换为可直接获取的文献。
### 建议措施
- 替换 X 条无法核实的引用
- 补充 X 条直接来源
- 确认 X 条间接引用是否忠实于原文
在 Claude Code 对话窗口输入：
/citation-fidelity
或完整 Prompt：
核查本论文/文献笔记中的所有引用，确保每条引用都定位到PDF原页，区分直接/间接引用，找不到原文标注"待核"，输出核查报告。
grillme → 确定选题后，用 citation-fidelity 核查文献综述的引用质量econ-reviewer → 审稿时配合使用，确保引用准确did-reviewer → DID 相关文献引用需特别核查机制描述的准确性