review-paper

review-paper is a skill for Claude Code from Felpix-Studios/social-science-research. It costs 36 tokens per session (2,366 once invoked), scanned A, original, MIT.

An academic-paper review process that examines a manuscript’s reasoning, research methods, citations, and likely objections from journal reviewers.

In plain words
What is it for?
Use it to review a paper in LaTeX, PDF, or Quarto format and get feedback on argument structure, econometric methods, citations, writing, and presentation.
Why use it?
Authors may miss weaknesses that a critical reader or specialist would notice. A structured review helps identify problems before submitting a paper.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the social-science-research plugin — 13 skills, 9 agents, 3 hooks shipped together

Good fit Use it to review a paper in LaTeX, PDF, or Quarto format and get feedback on argument structure, econometric methods, citations, writing, and presentation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/felpix-studios/social-science-research/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 Felpix-Studios/social-science-research --skill review-paper
Clone the repo
git clone --depth 1 https://github.com/Felpix-Studios/social-science-research

Made for: Claude Code.

Or install social-science-research, the plugin that ships this one along with the rest of its 13 skills, 9 agents, 3 hooks.

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/felpix-studios/social-science-research/review-paper/github.svg)](https://agentmods.dev/skills/felpix-studios/social-science-research/review-paper)
Your own site
<a href="https://agentmods.dev/skills/felpix-studios/social-science-research/review-paper"><img src="https://agentmods.dev/badge/skills/felpix-studios/social-science-research/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/felpix-studios/social-science-research/review-paper"><img src="https://agentmods.dev/badge/skills/felpix-studios/social-science-research/review-paper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,366 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 $0.00036 $0.02366
Opus 5 $0.00018 $0.01183
Sonnet 5 $0.00007 $0.00473
Haiku 4.5 $0.00004 $0.00237

Measured 12d ago against content hash c5b3d5045e15, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 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/review-paper/SKILL.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Manuscript Review

Produce a thorough, constructive review of an academic manuscript — the kind of report a top-journal referee would write.

Input: $ARGUMENTS — path to a paper (.tex, .pdf, or .qmd), or a filename in manuscripts/ or references/papers/.

Steps

  1. Locate and read the manuscript. Check:

    • Direct path from $ARGUMENTS
    • manuscripts/$ARGUMENTS
    • references/papers/$ARGUMENTS
    • Glob for partial matches in manuscripts/ and references/papers/
  2. Read the full paper end-to-end. For long PDFs, read in chunks (5 pages at a time).

  3. Read references/domain-profile.md for field and top journals — use these to calibrate the referee perspective (see Principles).

  4. Dispatch three reviewer agents in parallel via Task (one message, three Task calls — see below):

    • domain-reviewer — substantive correctness through 6 lenses (including design-specific diagnostic reporting)
    • adversarial-reviewer — hostile-referee attack on the paper
    • fresh-eyes-reviewer — first-time reader perspective
  5. Evaluate writing quality and presentation (dimensions 5-6) — the skill handles these directly while the agents run.

  6. After all agents complete, merge findings into the unified report:

    • fresh-eyes-reviewer output → "Fresh Eyes Read" section
    • domain-reviewer output → "Major Concerns" and "Minor Concerns" sections
    • adversarial-reviewer output → primary source for "Referee Objections" and any FATAL-severity entries in "Major Concerns"
    • Skill's own Dim 5-6 evaluation → "Writing Quality" and "Presentation" entries in concerns
  7. Produce the unified review report.

  8. Save to quality_reports/paper_review_[sanitized_name].md

Step 4: Dispatch Three Reviewer Agents in Parallel

Send one message with three Task calls so the agents run concurrently. Each agent has a distinct job and they should not see each other's output.

Task 1: domain-reviewer

Task prompt: "You are the domain-reviewer agent. Review the manuscript at [path].
Research question: [from spec if available].

Apply all 6 review lenses:
1. Assumption stress test
2. Derivation verification
3. Citation fidelity
4. Code-theory alignment
5. Backward logic check
6. Design-specific diagnostics audit — verify that the paper reports the actual numerical results of the diagnostics the claimed design demands (parallel-trends event-study coefficients, McCrary t-stat, Olea-Pflueger first-stage F, AR weak-IV CI, in-space placebo permutation, balance table, etc., depending on design). Treat a missing required diagnostic as CRITICAL.

Also check cross-document consistency.
Follow the domain-reviewer agent instructions and return your full substance review report."

Read the full file on GitHub · 263 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 · 263 lines · 36 tokens per session scan A c5b3d5045e15

Subscribe to this mod's changes

review-paper is a skill published in the GitHub repository Felpix-Studios/social-science-research (8 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 2,366 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 2026-08-31.

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