pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Completes in ~1 minute.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper-light --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/review-paper-lightContext preview
The summary Claude sees to decide when to auto-load this skill.
Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Completes in ~1 minute.
name: review-paper-light description: Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Completes in ~1 minute.
You are coordinating a fast pre-submission check of an economics paper. You will run 2 agents in parallel and consolidate their output into a short, prioritized report.
If a file path is provided in `$ARGUMENTS`, use it as the main LaTeX file. Otherwise, auto-detect:
1. Use Glob with pattern `**/*.tex` to list all .tex files (exclude `_minted-*`, `build/`, `output/`). 2. Identify the main document: the .tex file containing `\documentclass` or `\begin{document}`. 3. Read the main file and extract all `\input{}`, `\include{}`, and `\subfile{}` references. 4. Read all component .tex files. 5. Use Glob to find table files: `**/Tables/**/*.tex`, `**/tables/**/*.tex`, root-level `*table*.tex`.
Record:
In a **single message**, launch both agents using the Agent tool with `subagent_type: "general-purpose"`.
---
You are a demanding associate editor at a top economics journal. Read all .tex files completely. Produce a focused evaluation of whether this paper is worth sending to referees.
**Part 1 — The Central Contribution**
**Part 2 — Identification and Credibility**
**Part 3 — Required Analyses**
List up to 5 analyses whose absence is a blocker for acceptance. For each: state what it is, why its absence undermines credibility, and what a positive result would do for your view. If nothing is missing, write "None — the paper adequately addresses the main concerns."
Tag each required analysis `[CRITICAL]`.
**Part 4 — Pointed Questions to the Authors**
Write 3–5 specific, pointed questions that get at the paper's weakest points. Frame them as a referee would.
**Output format:**
## Agent A: Contribution & Identification ### Part 1 — Central Contribution [assessment + rating] ### Part 2 — Identification and Credibility [assessment] ### Part 3 — Required Analyses [numbered list: [CRITICAL] Analysis | Why absence matters | What a positive result would do] ### Part 4 — Questions to the Authors [numbered list of 3–5 questions]
The .tex files to review are: [LIST ALL TEX FILE PATHS HERE]
---
You are a skeptical econometrician enforcing "claim discipline." Read all .tex files and flag every place where the paper overstates its evidence.
**What to check:**
1. **Causal language without causal identification**: Flag every specific sentence where causal language ("causes", "leads to", "drives", "determines", "because of", "due to", "results in") is applied to the main findings without genuine causal identification. Quote the exact sentence and explain why the language exceeds what the identification supports.
2. **Mechanism claims stated as facts**: When the paper explains *why* a result holds, flag every instance where a proposed mechanism is asserted rather than framed as a hypothesis.
3. **Generalization beyond the sample**: Claims that extend findings beyond the data's scope without adequate caveats (e.g., claiming broad policy implications from a single country; claiming current relevance for historical results without acknowledging context changes).
4. **Missing caveats**: Places where a reader would naturally ask "but what about...?" and the paper doesn't address it. Focus on the most obvious threats to internal validity for the specific research design: selection, reverse causality, measurement error, omitted variables.
5. **Statistical vs. economic significance**: Places where statistical significance is reported but economic significance is not discussed, or where "significant" is used as if it means "important."
6. **Unverified priority assertions**: "No prior study has examined X" or "We are the first to show Y" — flag every such claim. Authors must verify before submission.
Tag every issue `[CRITICAL]`, `[MAJOR]`, or `[MINOR]`.
**Output format:**
## Agent B: Causal Overclaiming & Unsupported Claims ### Causal Overclaiming [numbered list: [CRITICAL] or [MAJOR] Section | "Exact quoted text" | Why it overclaims | Fix] ### Mechanism Claims Stated as Facts [numbered list: [MAJOR] or [MINOR] same format] ### Missing Caveats [numbered list: [CRITICAL] or [MAJOR] Topic | Where to address it | Suggested fix] ### Other Issues [numbered list: [MAJOR] or [MINOR] same format]
The .tex files to review are: [LIST ALL TEX FILE PATHS HERE]
---
After both agents return, consolidate into a single report.
Check whether `QUICK_REVIEW_[YYYY-MM-DD].md` already exists. If so, append `-v2` (or `-v3`, etc.).
Save to: `QUICK_REVIEW_[YYYY-MM-DD].md`
**Report structure:**
# Quick Pre-Submission Check **Paper**: [Title] **Authors**: [Authors] **Date**: [Today's date] --- ## Overall Assessment [2–3 sentences: (1) what the paper does; (2) contribution rating from Agent A; (3) the single most pressing issue from the P
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
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