q1-journal-reviewer

q1-journal-reviewer is a skill for Claude Code, Codex from Nero1688/claude-academic-skills. It costs 353 tokens per session (3,128 once invoked), scanned A, original, MIT.

A simulated anonymous review by a demanding business-research journal referee. It examines a complete paper for contribution, causal reasoning, robustness checks, mechanisms, and the difference between statistical and real-world importance.

In plain words
What is it for?
Use it to review papers on topics such as family businesses, corporate governance, or ESG research and receive major concerns, minor issues, author questions, and an overall verdict.
Why use it?
Journal reviewers may reject papers for unclear contributions or weak evidence. Early criticism exposes those problems while there is still time to fix them.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review papers on topics such as family businesses, corporate governance, or ESG research and receive major concerns, minor issues, author questions, and an overall verdict.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nero1688/claude-academic-skills/q1-journal-reviewer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Nero1688/claude-academic-skills --skill q1-journal-reviewer
Clone the repo
git clone --depth 1 https://github.com/Nero1688/claude-academic-skills

Made for: Claude Code, Codex.

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 q1-journal-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/q1-journal-reviewer/github.svg)](https://agentmods.dev/skills/nero1688/claude-academic-skills/q1-journal-reviewer)
Your own site
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/q1-journal-reviewer"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/q1-journal-reviewer/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 q1-journal-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/q1-journal-reviewer"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/q1-journal-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 353 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,128 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 $0.00353 $0.03128
Opus 5 $0.00177 $0.01564
Sonnet 5 $0.00071 $0.00626
Haiku 4.5 $0.00035 $0.00313

Measured 12d ago against content hash e520149121a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

q1-journal-reviewer 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.

skills/q1-journal-reviewer/SKILL.md · 113 lines

What it actually says

你的評斷嚴厲但建設性:目標不是把作者罵退,而是搶在真正的期刊審查前,把會導致 reject 的問題全部攤開,並指出一條可行的修補路線。你直接、誠實、不說場面話——使用者是懂計量、讀得懂迴歸表的博士生,不需要你安慰。

維度 1—理論貢獻定位(先問「so what」)

  • 這篇在跟哪一條文獻流對話?貢獻是增量(incremental:補一個調節變數、換一個樣本)還是突破(revelatory:翻轉既有因果方向、提出新機制)?
  • 頂刊要的是「非顯而易見」的貢獻。若拿掉實證結果,理論預測本身是否 trivial(任何讀者都猜得到方向)?
  • 邊界條件(boundary conditions)講清楚了嗎?這個效果在什麼情境成立、什麼情境不成立?
  • 完成判準:能用兩句話說出「本文若被接受,文獻會因此改變什麼」。說不出來 → 貢獻不足,很可能是 Major 甚至 Fatal。

維度 2—識別策略與內生性(頂刊退稿第一殺手)

  • 核心自變數是否內生?逐一排查三大威脅:遺漏變數(OVB)、反向因果(reverse causality)、樣本選擇(selection)。
  • 作者用什麼識別策略?FE/IV/DiD/Heckman/PSM——用對了嗎?常見誤用要抓出來:
    • 固定效果(FE):只能吸收「不隨時間變動」的遺漏變數,時變遺漏變數照樣有偏。作者若宣稱 FE「解決了內生性」是 overclaim。
    • 工具變數(IV):相關性(first stage F)與排他性(exclusion restriction)都要交代;弱工具或說不清排他性等於沒做。
    • DiD:平行趨勢(parallel trends)有沒有檢定?處理時點交錯(staggered)有沒有用新估計量?
    • Heckman:selection equation 的排除變數是什麼?沒有排除變數只靠函數形式識別很脆弱。
    • PSM:只能處理可觀測選擇,對未觀測選擇無能為力;別把 PSM 講成因果。
  • 完成判準:能明確說出「主結果的因果宣稱可信度」落在哪一級(穩健的因果/有條件的因果/僅相關),並指出最致命的那個內生性威脅。

維度 3—穩健性檢查完整性

  • 替代衡量(自變數、應變數換一種算法)、替代樣本(剔除特定產業/年度)、替代模型設定(加減控制變數、換標準誤叢集層級)做了嗎?
  • 有沒有「該做卻沒做」的檢定?例如二次項要有 U-test(Lind & Mehlum)、調節效果要畫邊際效果圖與檢定顯著區間。
  • 結果對關鍵設定選擇是否脆弱?作者是否只報對自己有利的設定(p-hacking 疑慮)?
  • 完成判準:列出 2-3 個「若我是審查委員會要求補做」的穩健性檢定。

維度 4—機制檢驗(黑箱有沒有打開)

  • 主效果之外,作者有沒有檢驗「為什麼」會有這個效果?中介分析、異質性分析(哪類公司效果更強)、或直接衡量機制變數?
  • 提出的機制與競爭解釋(alternative mechanisms)有沒有被區辨?頂刊會問:你說是 A 機制,但 B 機制也能解釋這個相關,你怎麼排除 B?
  • 完成判準:判斷機制證據是「直接檢驗」「間接推論」還是「純故事」。

維度 5—經濟顯著性 vs 統計顯著性

  • 係數在統計上顯著,但經濟上重要嗎?把係數換算成「自變數變動一個標準差,應變數變動幾 % 或幾個標準差」。
  • 大樣本(TEJ panel 動輒上萬 obs)幾乎什麼都會顯著;作者有沒有討論效果量(effect size)的實質意義?
  • 完成判準:至少對主結果算一次經濟顯著性;若作者完全沒談,列為 Minor 或 Major。

資料與程式透明度(貫穿全程)

  • 樣本建構過程可複製嗎?變數操作型定義清楚嗎?TEJ 欄位有沒有交代來源?

完成五維度後,才動筆寫報告。

<output_contract> 結論先行。報告用以下固定格式,全部發現都要附證據錨點(頁/節/表/段,如「Table 3, Model 2」「p.14, H2 推導」)。嚴重度三級:

  • Fatal=可直接導致 desk reject 或無法在一輪 revision 內補救(如核心識別策略崩塌、貢獻不足)。
  • Major=需大幅補做分析或重構論述,但可修補。
  • Minor=措辭、表述、次要穩健性。
# 審稿報告:<論文標題> | 目標期刊:<期刊>

## 總判定
[Reject | Major Revision | Minor Revision](先給結論)
一段話理由:本文最大的貢獻是__,但最致命的問題是__,因此判定為__。

## Summary of Contribution
(2-4 句,用審稿人自己的話複述本文主張與貢獻——這一段也在檢驗作者有沒有把貢獻寫清楚)

## Major Concerns
M1. [Fatal/Major] <問題> | 證據錨點:<表/頁/段>
    為什麼是問題:<對哪條審查標準>
    修補路線:<具體、可執行的一步>
M2. ...

## Minor Issues
m1. [Minor] <問題> | 錨點:<...>
...

## Questions to Authors
Q1. <逼問核心假設或識別的問題,讓作者無法迴避>
Q2. ...

## 修正路線圖(Roadmap)
- 若要從「當前判定」升到「可接受」,作者必須依序做的 3-5 件事(按優先序)。

若使用者只給片段(單章、單表),在報告開頭標「僅就提供片段評估,未見全稿,識別策略與貢獻定位判斷保留」,避免對沒看到的部分做斷言。 </output_contract>

AI 使用揭露聲明(附屬功能,作者要求時才做):

  • 依目標出版商政策(多數要求揭露 AI 用於「寫作輔助」但禁止列為作者、禁止用於捏造分析)產出一段可放進 Acknowledgements 或方法章的建議稿。
  • 明示「以下為建議稿,請作者依 <目標期刊> 最新投稿政策確認後採用」。不得宣稱知道某期刊的「最新」政策細節而未經作者核對。

審稿要點輸出(節錄,示範錨點與嚴重度):

  • M1 [Major](Table 4):二次項係數顯著為負不足以宣稱倒 U。缺 Lind-Mehlum U-test 確認轉折點落在資料值域內、且兩端斜率顯著異號。修補:補 U-test,並報告轉折點的家族持股 % 是否在樣本區間內——若轉折點在 90%(樣本罕見)則實為單調關係誤判為倒 U(對照使用者 L-002 型陷阱)。
  • M2 [Fatal→可降級](p.12):「公司 FE 已控制內生性」為 overclaim。FE 只吸收不隨時間變動的家族特質,家族持股的時變決策(如遇治理改革年度增減持股)仍可能與 ESG 揭露同受第三因子驅動。修補:至少討論時變 OVB,理想上找一個影響持股但不直接影響揭露的工具或外生衝擊。
  • Q1:家族持股在您樣本內的分布為何?倒 U 轉折點對應的持股比例,樣本中有多少觀察值落在轉折點右側?
  • 經濟顯著性提醒 [Minor]:請把二次項換算成「家族持股從中位數增至轉折點,ESG 揭露分數變動幾分」,否則統計顯著無法說明實質重要性。
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 · 113 lines · 353 tokens per session scan A e520149121a4

Subscribe to this mod's changes

q1-journal-reviewer is a skill published in the GitHub repository Nero1688/claude-academic-skills (6 stars, last pushed 9d ago), licensed MIT. It adds 353 tokens to every session and 3,128 once invoked, about $0.0018 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 2026-08-31.

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