Agent skill

Review Paper Light

by claesbackman in claesbackman/AI-research-feedback

Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming.

MITAuto-check: notesDocuments & Office

Install Review Paper Light

skills CLI
$ npx skills add claesbackman/AI-research-feedback --skill review-paper-light -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install claesbackman/AI-research-feedback review-paper-light --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/claesbackman/AI-research-feedback.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Skills/review-paper-light .claude/skills/review-paper-light && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-paper-light
GitHub stars
495
Used in
1 other repo
Token cost
~2.3k tokens
SKILL.md length
1,050 words
Files
1
Skills in repo
10
Repo updated
First seen
Licence
MIT

At a glance

Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming.

  • Works in 3 steps: Discover the Paper → Launch 2 Agents in Parallel → Consolidate and Save
  • Tasks that involve LaTeX
  • SKILL.md covers Phase 1: Discover the Paper, Phase 2: Launch 2 Agents in… and Phase 3: Consolidate and Save
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Paper Light is an agent skill from claesbackman/AI-research-feedback. Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Completes in ~1 minute.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering LaTeX. The repository describes itself as: A collection of Claude Code skills for academic research review. These tools were developed by Claes Bäckman. The licence is MIT.

When your agent uses it

  • Tasks that involve LaTeX

Example prompts

  • “/review-paper-light”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, Bash, Agent

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Discover the Paper
  2. Launch 2 Agents in Parallel
  3. Consolidate and Save

What it can do on your machine

Read from SKILL.md and the folder at commit d129756. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • Bash
    • Agent

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Paper Light loads about 2.3k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 1,050 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Glob, Grep, Bash, Agent

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from claesbackman/AI-research-feedback at commit d129756, republished under its MIT licence (© claesbackman). 1,050 words, ~2,315 tokens.

Download SKILL.mdSave it as .claude/skills/review-paper-light/SKILL.md (or your agent's skills folder).
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.
allowed-tools
Read, Write, Edit, Glob, Grep, Bash, Agent
disable-model-invocation
true

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.

Phase 1: Discover the Paper

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 among the .tex files containing \documentclass or \begin{document}. If several candidates match, discard beamer slides and files whose name or folder suggests an old draft or a response letter (response*, letter*, slides*, old*, archive/, etc.), then choose the candidate with the largest include-graph. If still ambiguous, ask the user.
  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. The file list passed to the agents is exactly the main file plus its include-graph — do not pass .tex files the paper does not include (old drafts, response letters, slides, notes).
  5. Use Glob to find table files: **/Tables/**/*.tex, **/tables/**/*.tex, root-level *table*.tex. Keep only tables that are \input{}/\include{}d from the include-graph.

Record:

  • Full path of each .tex file
  • Paper title, authors, and abstract

Phase 2: Launch 2 Agents in Parallel

In a single message, launch both agents using the Agent tool with subagent_type: "general-purpose".

Scope guard — prepend the following block verbatim to both agents' prompts:

Review ONLY the files listed at the end of this prompt. Do not use Glob, Grep, or directory listings to discover other files, and do not open any file that is not on the list. In particular, ignore any previous review reports (QUICK_REVIEW_*.md, PRE_SUBMISSION_REVIEW_*.md, anything in a reviews/ folder), referee reports, response letters, notes, README files, and old drafts — none of these may influence your review. Within the listed .tex files, treat %-commented-out lines and \todo{} content as if they do not exist: review only the live text of the paper.


AGENT A — Contribution, Identification & Required Analyses

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

  • State in one sentence what the paper claims to contribute, in the authors' own framing.
  • Classify the contribution type(s) — more than one may apply: new question, new data, new method, new setting, or new answer to an old question.
  • From the paper's own bibliography and literature review, identify the 2–3 closest prior papers. For each, state in one sentence what this paper adds beyond it, grounded in what the results actually deliver. Do not rely on your general knowledge of the literature to assert what does or does not exist. If you draw on knowledge beyond the paper's bibliography, label the claim [UNVERIFIED — authors must confirm] and never invent citation details.
  • Does the framing overstate? Does the introduction promise more than the results deliver?
  • Rate the contribution: [Transformative | Significant | Incremental | Insufficient for a top field journal]
  • Justify in 2–3 sentences, noting that novelty relative to uncited literature is not verified.

Part 2 — Identification and Credibility

  • What variation does the paper use to identify its main result?
  • Is this variation plausibly exogenous? What are the main threats?
  • Does the paper adequately address these threats?
  • Is the main finding causal, correlational, or descriptive? Does the paper claim the right thing?
  • What would a skeptical econometrician at a seminar say?

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.

Part 5 — Preliminary Recommendation

Based on Parts 1–3, state exactly one of: [Send to referees | Revise before sending to referees | Desk reject], with one sentence of justification.

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]

### Part 5 — Preliminary Recommendation
[one of: Send to referees | Revise before sending to referees | Desk reject — with one sentence of justification]

The .tex files to review are: [LIST ALL TEX FILE PATHS HERE]


Show full SKILL.md (353 more words)Show less
AGENT B — Causal Overclaiming & Unsupported Claims

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]


Phase 3: Consolidate and Save

After both agents return, consolidate into a single report.

Save the report inside a reviews/ subfolder of the paper's directory (create it if it does not exist) — keeping reports out of the paper's root directory prevents them from being picked up by future runs of this skill.

Check whether reviews/QUICK_REVIEW_[YYYY-MM-DD].md already exists. If so, append -v2 (or -v3, etc.).

Save to: reviews/QUICK_REVIEW_[YYYY-MM-DD].md

Report structure:

markdown
# 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 Priority Items below.]

**Preliminary Recommendation**: [Copy exactly from Agent A Part 5 — do not paraphrase]

---

## 1. Contribution & Identification

[Agent A output]

---

## 2. Causal Overclaiming & Unsupported Claims

[Agent B output]

---

## Priority Action Items

Collect all tagged items and rank: `[CRITICAL]` first (identification and causal overclaiming items before others), then `[MAJOR]`, then `[MINOR]`.

**CRITICAL** (could cause desk rejection or major objections):
1. ...

**MAJOR** (will likely be raised by referees):
4. ...

**MINOR** (polish):
8. ...

After saving, report to the user:

  1. Path to the saved report
  2. Preliminary recommendation
  3. Top 3 priority action items
  4. Issue counts by severity

© claesbackman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in Skills/review-paper-light of claesbackman/AI-research-feedback.

Open the folder on GitHubat commit d129756

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in claesbackman/AI-research-feedback, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Review Paper Light next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Paper Light compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Paper Light this skillclaesbackman/AI-research-feedback4951 repos~2.3kAutomated safety check: NotesMIT
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Mathmodel SkillhandsomeZR-netizen/mathmodel-skill292—~2.5kAutomated safety check: PassMIT
Newbookscunning1975/MixtapeTools474—~1.3kAutomated safety check: PassNone
Paper Auditbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~3.6kAutomated safety check: PassCustom licence
Paper WritingMLNLP-World/Paper-Writing-Tips4.7k—~630Automated safety check: PassNone

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Questions about Review Paper Light

What does Review Paper Light do?

Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming. Review Paper Light is an agent skill from claesbackman/AI-research-feedback. Run a fast 2-agent pre-submission check for an economics paper — focuses on contribution, identification, and causal overclaiming.

When should I use Review Paper Light?

Review Paper Light fits situations like: tasks that involve LaTeX.

How do I install Review Paper Light in Claude Code?

Run `npx skills add claesbackman/AI-research-feedback --skill review-paper-light -a claude-code`. Or copy the skill folder (Skills/review-paper-light in claesbackman/AI-research-feedback) into .claude/skills/review-paper-light in your project. Claude Code loads it when a task matches its description.

How do I install Review Paper Light in Codex?

Run `npx skills add claesbackman/AI-research-feedback --skill review-paper-light -a codex`. Or copy the skill folder (Skills/review-paper-light in claesbackman/AI-research-feedback) into .agents/skills/review-paper-light in your project. Codex loads it when a task matches its description.

Can I use Review Paper Light in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add claesbackman/AI-research-feedback --skill review-paper-light -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-paper-light, .gemini/skills/review-paper-light, .github/skills/review-paper-light and .opencode/skills/review-paper-light in your project.

What does Review Paper Light need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Paper Light is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Bash, Agent.

Does Review Paper Light access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Paper Light safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Review Paper Light use?

Review Paper Light is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Paper Light use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Paper Light?

Skills that share tags, products or a category with Review Paper Light: Research Writing (alfonso0512/research-writing-skill, 490 stars), Mathmodel Skill (handsomeZR-netizen/mathmodel-skill, 292 stars), Newbook (scunning1975/MixtapeTools, 474 stars) and Paper Audit (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Paper Light?

claesbackman (a GitHub user) maintains it in claesbackman/AI-research-feedback, which has 495 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on September 25, 2026.

Source: claesbackman/AI-research-feedback on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.