empiricalwiki: Skill for Claude Code

.claude/skills/empirical-ingest/SKILL.md

empirical-ingest is a skill for Claude Code from Lambenthan/empiricalwiki. It costs 49 tokens per session (1,336 once invoked), scanned A, original, MIT.

A workflow for turning an economics or management research paper into a reusable research wiki entry focused on its study design.

In plain words
What is it for?
Use it with a local PDF, LaTeX source, or prepared paper to create or update pages for the paper and, when needed, its variables, datasets, models, mechanisms, hypotheses, identification methods, and robustness checks.
Why use it?
It makes the important evidence easier to reuse by recording variables, data, models, mechanisms, causal identification, robustness checks, and differences between groups.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

This is Lambenthan/empiricalwiki's own configuration. It tells Claude Code how to work on empiricalwiki itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything empiricalwiki configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Lambenthan/empiricalwiki/main/.claude/skills/empirical-ingest/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Lambenthan/empiricalwiki

Made for: Claude Code.

Wrote 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.

agentmods badge for empirical-ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/empirical-ingest/github.svg)](https://agentmods.dev/skills/lambenthan/empiricalwiki/empirical-ingest)
Your own site
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/empirical-ingest"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/empirical-ingest/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.

agentmods 80×15 button for empirical-ingest

Your own site · 80×15
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/empirical-ingest"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/empirical-ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,336 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01336
Opus 5 $0.00024 $0.00668
Sonnet 5 $0.00010 $0.00267
Haiku 4.5 $0.00005 $0.00134

Measured 12d ago against content hash 12e4c8302a59, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

empirical-ingest 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 12d 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.

.claude/skills/empirical-ingest/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/empirical-ingest

把一篇实证论文拆成可复用的研究设计资产。它不是普通摘要工具;优先抽取变量、数据、模型、机制、识别和稳健性,再写通用概念。

Inputs

  • source: 本地 .pdf.tex,或 /init 预处理后的 raw/tmp/... 路径。
  • --topic 可选:当前项目主题,如“耐心资本与 ESG”。

Outputs

  • wiki/papers/{slug}.md
  • 按需新建或更新:
    • wiki/variables/*.md
    • wiki/datasets/*.md
    • wiki/models/*.md
    • wiki/mechanisms/*.md
    • wiki/hypotheses/*.md
    • wiki/identification/*.md
    • wiki/robustness/*.md
    • wiki/heterogeneity/*.md
    • wiki/tables/*.md(仅当用户明确要求逐表复现时创建;默认把关键表格及结论写进论文卡,不单独建页)
  • wiki/index.mdwiki/log.md
  • wiki/graph/edges.jsonl

Workflow

Step 1: Resolve Source

确认工作目录是项目根目录,包含 wiki/raw/tools/

优先使用 .venv

if [ -x .venv/bin/python ]; then PYTHON_BIN=.venv/bin/python; else PYTHON_BIN=python3; fi

如果输入是 PDF,先人工读取第一页标题。中文 PDF 可以用 PyMuPDF 快速抽取:

"$PYTHON_BIN" - "<source>" <<'PY'
import sys, fitz
path = sys.argv[1]
doc = fitz.open(path)
print(doc[0].get_text("text")[:2000])
PY

然后运行:

"$PYTHON_BIN" tools/prepare_paper_source.py --raw-root raw --source <source> --title "<confident-title>"

把返回的 prepared_path 作为正文读取入口。若标题不确定,不传 --title

Step 2: Extract Empirical Facts

从论文中抽取以下字段。没有明确证据时写“未报告”,不要猜:

  • 研究问题
  • 理论机制
  • 研究假设
  • 样本区间、样本范围、样本筛选规则
  • 数据来源和数据库表
  • 被解释变量、核心解释变量、中介变量、调节变量、控制变量、工具变量
  • 变量测算公式、分组规则、缩尾规则
  • 主模型、固定效应、标准误聚类方式
  • 内生性处理
  • 机制检验、异质性检验、稳健性检验
  • 关键表格及结论
  • 可复现线索:变量名、数据库、Stata 处理步骤

Step 3: Write Pages

打开 docs/runtime-page-templates.zh.md,按模板写页面。

papers/{slug}.md 时,正文必须包含:

## 研究问题
## 理论机制
## 研究假设
## 数据与样本
## 变量设定
## 模型设定
## 主要结果
## 机制检验
## 异质性检验
## 稳健性检验
## 内生性处理
## 可复现线索
## 对我当前选题的启发
## Related

对每个核心变量、数据源、模型、机制和检验方法,先查重再创建新页面:

"$PYTHON_BIN" tools/research_wiki.py slug "<title>"

已有等价页面时更新,不重复造近义页面。

Step 4: Add Graph Edges

用工具写图谱关系,不手动编辑 wiki/graph/edges.jsonl

"$PYTHON_BIN" tools/research_wiki.py add-edge wiki --from papers/<paper> --to variables/<variable> --type operationalizes --confidence high --evidence "<evidence>"
"$PYTHON_BIN" tools/research_wiki.py add-edge wiki --from papers/<paper> --to datasets/<dataset> --type uses_dataset --confidence high --evidence "<evidence>"
"$PYTHON_BIN" tools/research_wiki.py add-edge wiki --from papers/<paper> --to models/<model> --type estimates_model --confidence high --evidence "<evidence>"

Read the full file on GitHub · 143 lines

Changes

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.

  1. 12d ago First seen · 143 lines · 49 tokens per session scan A 12e4c8302a59

Subscribe to this mod's changes

empirical-ingest is a skill published in the GitHub repository Lambenthan/empiricalwiki (84 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 1,336 once invoked, about $0.0002 per session on Opus 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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