charls-reproduce
CHARLS (China Health and Retirement Longitudinal Study) database-specific knowledge for reproducing published papers. Use when reproducing or analyzing papers that use CHARLS data, including variable mapping from harmonized to raw questionnaire items, cognitive function scoring (episodic memory, mental status, TICS), CESD-10 depression screening, social isolation index construction, and chronic disease coding. Also use for any CHARLS data cleaning, variable construction, or cohort selection task.
npx skills add BioTender-max/awesome-bio-agent-skills --skill charls-reproduce --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.
# CHARLS Reproduce — CHARLS 数据库复现指南 ## CHARLS 概览 | 项目 | 说明 | |------|------| | 全称 | China Health and Retirement Longitudinal Study (中国健康与养老追踪调查) | | 官网 | https://charls.pku.edu.cn/ | | 覆盖 | 全国 150 县级单位、450 社区，45 岁以上居民 | | 波次 | wave1(2011), wave2(2013), wave3(2015), wave4(2018), wave5(2020) | | 格式 | Stata (.dta), 也有 harmonized 版本 | ## Harmonized 数据集 vs 原始数据 大多数 CHARLS 论文使用**原始问卷模块数据**，变量名以 CHARLS 问卷编号命名（如 `dc009s1`）。但很多用户拿到的是 **harmonized 数据集**（合并所有波次，变量已清洗重命名）。 **识别方法**: - 如果有 `wave` 列且包含 `wave1`-`wave5` → harmonized 数据 - 如果有 `dc009s1`, `be001`, `sc004_s1` 等原始变量名 → 原始数据 - harmonized 数据通常 ~10 万行（所有波次堆叠），原始数据每波 ~2 万行 **关键差异**: 变量名完全不同，缺失模式不同，编码方式不同。 ## 核心变量映射表 ### 认知功能 | 论文常用名 | 原始变量 | Harmonized 变量 | 范围 | 说明 | |-----------|---------|----------------|------|------| | 即时回忆 | `dc009s1`–`dc009s10` | `imrc` | 0-10 | 10 个词即时回忆正确数 | | 延迟回忆 | `dc012s1`–`dc012s10` | `dlrc` | 0-10 | 延迟回忆正确数 | | 情景记忆 (EM) | 自行计算 | `recall` 或 `(imrc+dlrc)/2` | 0-10 | **注意**: `recall` = `(imrc+dlrc)/2` | | Serial 7 | `dc024` (5 次减法) | `ser7` | 0-5 | 100 连续减 7，正确次数 | | 日期定向 | `dc003`-`dc006` | `orient` | 0-4 | = dw + dy + mo + yr（4 个二分项之和） | | 画图 | `dc014` | `draw` | 0-1 | 重画展示图形是否正确 | | 心理状态 (MS) | 自行计算 | `ser7
- CHARLS 概览
- Harmonized 数据集 vs 原始数据
- 核心变量映射表
- 认知功能
- 抑郁 (CESD-10)
- 社会隔离
- 人口学与健康变量
- 社会活动变量
- 日期定向子变量
- 常见缺失率（Wave1 参考）
- 样本筛选经验
- 标准流程
- 样本量调试策略
- Wave 间数据合并
What does the charls-reproduce skill do?
CHARLS (China Health and Retirement Longitudinal Study) database-specific knowledge for reproducing published papers. Use when reproducing or analyzing papers that use CHARLS data, including variable mapping from harmonized to raw questionnaire items, cognitive function scoring (episodic memory, mental status, TICS), CESD-10 depression screening, social isolation index construction, and chronic disease coding. Also use for any CHARLS data cleaning, variable construction, or cohort selection task.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill charls-reproduce --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.