Borrowing it
Nothing to install: this file belongs to Lambenthan/empiricalwiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Lambenthan/empiricalwiki/main/.claude/skills/variable-map/SKILL.mdgit clone --depth 1 https://github.com/Lambenthan/empiricalwikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lambenthan/empiricalwiki/variable-map)<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/variable-map"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/variable-map/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/variable-map"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/variable-map.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.00499 |
| Opus 5.5 | $0.00011 | $0.00200 |
| Sonnet 5.5 | $0.00006 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
variable-map scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/variable-map
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”。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 59 lines · 28 tokens per session scan A ed93d4bc167e
variable-map is a skill published in the GitHub repository Lambenthan/empiricalwiki (96 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 499 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs…