/variable-map
SkillDocs & knowledgeSummarizes how an economics/management empirical variable is measured in the literature, including its calculation methods, data sources, role in models, and availability in the project.
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the /variable-map skill
What this skill tells your AI
The instructions your AI receives, as published by lambenthan/empiricalwiki in .claude/skills/variable-map/SKILL.md and read by ahel’s review.
把分散在文献卡片里的变量信息整理成可执行的变量字典。适合回答“耐心资本怎么测算”“管理者短视有哪些口径”“ESG 用哪个数据源”。
Workflow
Step 1: Locate Relevant Pages
读取:
wiki/variables/*.mdwiki/papers/*.mdwiki/datasets/*.mdwiki/models/*.md- 当前项目的
README.md、raw/notes/research-intent.md(如果存在)
按变量名称、别名、构念、角色、标签和 source_papers 匹配。
Step 2: Build the Comparison Table
输出表格至少包含:
| 字段 | 含义 |
|---|---|
| 变量/构念 | 如耐心资本、ESG、管理者短视 |
| 模型角色 | 被解释变量、核心解释变量、中介、调节、控制等 |
| 测算口径 | 公式或文本描述 |
| 数据来源 | 数据库、表名、项目路径 |
| 样本频率 | firm-year、quarter 等 |
| 来源文献 | wiki paper slug |
| 优点 | 为什么可用 |
| 风险 | 内生性、口径争议、缺失值、复现难点 |
| 项目可用性 | 已有 / 缺失 / 需要手工整理 |
Step 3: Archive
生成:
wiki/outputs/variable-map-{slug}-{YYYY-MM-DD}.md
并追加日志:
python3 tools/research_wiki.py log wiki "variable-map | <variable> | output: outputs/<file>"
Constraints
- 只比较 wiki 或本地项目中已有证据,不凭常识补数据库表名。
- 不把不同口径强行合并;口径不同就分行。
- 如果信息来自论文但本地数据没有,明确写“项目暂缺”。
Signals
- GitHub stars
- 83
- Forks
- 17
- Last commit
- Jun 2026
Advanced
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variable-map- Source
- github.com/lambenthan/empiricalwiki