review-paper

review-paper is a skill for Claude Code from claesbackman/AI-research-feedback. It costs 21 tokens per session (8,759 once invoked), scanned A, original, MIT.

A coordinated review process for an academic economics paper before it is submitted to a journal. It uses eight specialised reviewers and combines their reports.

In plain words
What is it for?
It is for reviewing a paper aimed at journals such as AER, QJE, JF, or Econometrica, or for receiving a general high-standard economics review.
Why use it?
It helps identify problems in a paper before submission and checks it against the standards of a chosen economics journal or research field.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit It is for reviewing a paper aimed at journals such as AER, QJE, JF, or Econometrica, or for receiving a general high-standard economics review.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/claesbackman/ai-research-feedback/review-paper
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 claesbackman/AI-research-feedback --skill review-paper
Clone the repo
git clone --depth 1 https://github.com/claesbackman/AI-research-feedback

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 review-paper

README.md
[![agentmods](https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-paper/github.svg)](https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-paper)
Your own site
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-paper"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-paper/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 review-paper

Your own site · 80×15
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-paper"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-paper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,759 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Anti-Refusal · line 170
    Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.
    Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
How audits are shown
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 $0.00021 $0.08759
Opus 5 $0.00010 $0.04380
Sonnet 5 $0.00004 $0.01752
Haiku 4.5 $0.00002 $0.00876

Measured 11d ago against content hash 78ee0fe52db6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

review-paper 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 11d 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/review-paper/SKILL.md · 664 lines

How it starts

The opening of the file, as written. The whole thing — 664 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are coordinating a rigorous pre-submission review of an academic economics paper. You will run 8 specialized review agents in parallel and consolidate their findings into a structured report.

Phase 1: Parse Arguments and Discover the Paper

Parse $ARGUMENTS as follows:

  • The recognized journal names are:
    • Top-5 economics: AER, QJE, JPE, Econometrica, REStud
    • Finance: JF, JFE, RFS, JFQA
    • Macro: AEJMacro, JME, RED
    • (case-insensitive; users can add further journals by editing this list in the skill file)
  • If the first token of $ARGUMENTS matches one of these names, treat it as the target journal and treat any remaining text as the file path.
  • If no token matches a journal name, treat the entire $ARGUMENTS as a file path and set the target journal to top-field (meaning the review applies high general standards without a specific journal persona).
  • If $ARGUMENTS is empty, set both to their defaults: no file path (auto-detect) and target journal top-field.

Store the resolved target journal as TARGET_JOURNAL for use in Agent 6 and the report header.

If a file path was provided, use it as the main LaTeX file. Otherwise, auto-detect:

  1. Use Glob with pattern **/*.tex to list all .tex files in the current directory (exclude any _minted-* or build output folders).
  2. Identify the main document among the .tex files that contain \documentclass or \begin{document}. Read each candidate briefly if needed. Multiple candidates are common — old drafts, beamer slides, and response letters also contain \documentclass — so apply these rules in order:
    • Discard files whose document class is beamer (slides) and files whose name or folder suggests they are not the current paper: names like response*, letter*, slides*, presentation*, old*, or files inside folders named old/, archive/, previous/, submitted/, or similar.
    • Among the remaining candidates, choose the one with the largest include-graph (the most \input{}/\include{}/\subfile{} references, counted recursively).
    • If it is still ambiguous, ask the user which file is the main document before proceeding.
  3. Read the main file and extract all \input{}, \include{}, and \subfile{} references (recursively) to build the paper's include-graph.
  4. Read all component .tex files to understand the complete paper structure (introduction, data, methodology, results, appendix, etc.). The .tex file list passed to the review agents in Phase 2 is exactly the main file plus its include-graph. Do not pass .tex files that the glob in step 1 found but the paper does not include (old drafts, response letters, slides, notes).
  5. Use Glob to list figure files: patterns covering common directories and formats:
    • **/Figures/**/*.pdf, **/figures/**/*.pdf, **/Figure/**/*.pdf, **/figure/**/*.pdf
    • **/Figures/**/*.png, **/figures/**/*.png, **/Figure/**/*.png, **/figure/**/*.png
    • **/Figures/**/*.eps, **/figures/**/*.eps, **/Figure/**/*.eps, **/figure/**/*.eps
    • **/Figures/**/*.jpg, **/figures/**/*.jpg, **/Figure/**/*.jpg, **/figure/**/*.jpg
    • **/Figures/**/*.jpeg, **/figures/**/*.jpeg, **/Figure/**/*.jpeg, **/figure/**/*.jpeg
    • **/Figures/**/*.svg, **/figures/**/*.svg, **/Figure/**/*.svg, **/figure/**/*.svg
    • Root-level: *.pdf, *.png, *.eps, *.jpg, *.jpeg, *.svg
    • Exclude: **/_minted-*/**, **/build/**, **/output/**, **/.git/**
  6. Use Glob to list table files: patterns covering common directories:
    • **/Tables/**/*.tex, **/tables/**/*.tex, **/Table/**/*.tex, **/table/**/*.tex
    • Root-level: *table*.tex, *Table*.tex
    • Exclude: **/_minted-*/**, **/build/**, **/output/**, **/.git/**
  7. Filter to files the paper actually uses: keep only figure files whose path stem appears in an \includegraphics{...} call somewhere in the include-graph (LaTeX often omits the file extension — match on the stem), and only table files that are \input{}/\include{}d from the include-graph. Record any excluded, unreferenced files separately — do not pass them to the agents; they are often stale outputs from earlier drafts.

Read the full file on GitHub · 664 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. 11d ago First seen · 664 lines · 0 tokens per session scan A 78ee0fe52db6

Subscribe to this mod's changes

review-paper is a skill published in the GitHub repository claesbackman/AI-research-feedback (478 stars, last pushed 14d ago), licensed MIT. It adds 21 tokens to every session and 8,759 once invoked, about $0.0001 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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