Agent skill

Review Paper Checks

by claesbackman in claesbackman/AI-research-feedback

Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims.

MITAuto-check: notesDocuments & Office

Install Review Paper Checks

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

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

GitHub CLI
$ gh skill install claesbackman/AI-research-feedback review-paper-checks --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-checks .claude/skills/review-paper-checks && 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-checks
GitHub stars
495
Token cost
~4.3k tokens
SKILL.md length
2,073 words
Files
1
Skills in repo
10
Repo updated
First seen
Licence
MIT

At a glance

Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims.

  • Works in 3 steps: Discover the Paper → Launch 3 Agents in Parallel → Consolidate and Save
  • Tasks that involve LaTeX
  • SKILL.md covers Phase 1: Discover the Paper, Phase 2: Launch 3 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 Checks is an agent skill from claesbackman/AI-research-feedback. Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims. Reports fixable errors, not editorial judgment.

Its SKILL.md is about 4.3k 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-checks”

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 3 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 Checks loads about 4.3k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 2,073 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

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). 2,073 words, ~4,334 tokens.

Download SKILL.mdSave it as .claude/skills/review-paper-checks/SKILL.md (or your agent's skills folder).
name
review-paper-checks
description
Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims. Reports fixable errors, not editorial judgment.
allowed-tools
Read, Write, Edit, Glob, Grep, Bash, Agent
disable-model-invocation
true

You are coordinating a mechanical error check of an economics paper. You will run 3 agents in parallel and consolidate their output into a single list of concrete, fixable defects.

What this skill does: finds things that are wrong — misspellings, grammar errors, numbers that disagree between text and tables, terminology that drifts, cross-references that point to nothing, sentences that claim more than the evidence supports.

What this skill does not do: judge the contribution, evaluate the identification strategy, propose additional analyses, or issue a publication recommendation. Those are editorial judgments and belong to /review-paper-light (fast) or /review-paper (full). Do not let the agents drift into them, and do not add such judgments yourself when consolidating.

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, **/Table/**/*.tex, **/table/**/*.tex, root-level *table*.tex and *Table*.tex. Keep only tables that are \input{}/\include{}d from the include-graph.
  6. Use Glob to find the bibliography: **/*.bib. Keep only files named in a \bibliography{}, \addbibresource{}, or \begin{thebibliography} block within the include-graph. If the references are typed directly into a .tex file, note that instead.

Record:

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

If no table files are found, warn the user: "No table .tex files were found in standard locations. Agent B can only check consistency across the prose, not against table source files."

Phase 2: Launch 3 Agents in Parallel

In a single message, launch all 3 agents using the Agent tool with subagent_type: "general-purpose".

Scope guard — prepend the following block verbatim to all three 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 (PAPER_CHECK_*.md, 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.

Precision guard — prepend the following block verbatim to all three agents' prompts:

Every issue you report must be anchored to text that actually appears in the files. Quote the exact string from the source, and give the file name and the section or paragraph where it appears. Do not report an issue you cannot quote. Do not speculate about what a table or figure might contain — if you cannot read the value, skip it rather than flagging it. It is better to return five issues you are certain of than thirty you are not. You are checking for errors, not evaluating the paper's contribution, identification strategy, or suitability for publication — stay out of those judgments entirely.


AGENT A — Spelling, Grammar & Academic Style

You are a copy editor at a top economics journal. Read all .tex files in the following list. Ignore LaTeX commands (anything starting with \) unless they cause formatting problems. Focus on the prose.

What to check:

  1. Spelling errors: Every misspelled word. Pay special attention to proper nouns (author names, place names, institution names), technical terms, and commonly confused pairs (affect/effect, principal/principle, complement/compliment, discrete/discreet).

  2. Grammar errors: Subject-verb agreement, tense consistency (present tense for findings, past tense for what was done), article usage (a/an/the), dangling modifiers, comma splices, run-on sentences, sentence fragments.

  3. Spelling convention consistency: Is the paper consistently US or UK English? Flag mixed usage (behavior/behaviour, analyze/analyse, labor/labour) and state which convention dominates.

  4. Awkward or convoluted phrasing: Sentences that require re-reading. Quote the sentence and suggest a clearer alternative.

  5. Style violations — flag every instance of:

    • "interestingly", "importantly", "notably", "it is worth noting", "it is important to note", "needless to say", "obviously", "clearly" — delete these; let the finding speak for itself
    • "very unique", "absolutely essential", "completely eliminate" — tautologies
    • "significant" used to mean large or important (reserve "significant" for statistical significance)
    • "This paper contributes to the literature by..." — show, don't tell
    • Passive voice where active is natural ("it is shown that" → "we show that")
    • Inconsistent first person ("we find" in some places, "the paper argues" in others)
    • Bulleted or numbered lists used in place of prose in the main text
    • Semicolons joining independent clauses that would read better as two sentences
  6. Typographic consistency:

    • Hyphenation: is "long-run" vs "long run" used consistently? Is "high-income households" (attributive) vs "households with high income" (predicative) handled correctly?
    • Em-dash vs en-dash vs hyphen (en-dash for number ranges: 2003–2019)
    • Spacing around punctuation; missing \% escapes; ~ before citations and references
  7. Number formatting: Are numbers below 10 spelled out in prose? Are percentages formatted consistently (15% vs 15 percent)? Are decimal places consistent for the same quantity across the paper?

Output format:

Tag every issue [CRITICAL], [MAJOR], or [MINOR]. Use [CRITICAL] for outright errors a reader will notice (misspellings, broken grammar), [MAJOR] for issues a referee would remark on, [MINOR] for polish.

## Agent A: Spelling, Grammar & Style

### Errors (must fix before submission)
[numbered list: [CRITICAL] File, Section | "Problematic text" → "Suggested correction" | Reason]

### Style and Typography
[numbered list: [MAJOR] or [MINOR] same format]

### Recurring Patterns to Fix Throughout
[list each recurring problem once, with one example, an approximate count, and a global fix instruction — tag each [MAJOR] or [MINOR]]

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


AGENT B — Internal Consistency & Cross-References

You are a technical reviewer checking whether an economics paper is internally coherent. Read all .tex files and all table .tex files, and verify that the paper does not contradict itself.

What to check:

  1. Numerical consistency: Every time a specific number appears in the text (coefficients, standard errors, percentages, sample sizes, years), verify it matches the number in the referenced table — read the table .tex file directly. Flag discrepancies such as "text says 1.3% but Table 2 Column 3 shows 1.2%." Numbers embedded in figures (binscatters, coefficient plots) cannot be verified from source files: skip them and do not flag them.

  2. Abstract vs. body consistency: Do the numbers, findings, and claims in the abstract match the main text and tables exactly?

  3. Introduction vs. results consistency: Where the introduction previews a result ("we find X"), verify the results section delivers exactly that, with the same sign and magnitude.

  4. Conclusion vs. results consistency: Same check for the conclusion. Flag any finding that appears in the conclusion but not in the results.

  5. Terminology consistency: Identify every key term the paper defines and flag inconsistent usage. A term defined one way in Section 2 must not mean something else in Section 5. Flag interchangeable use of terms with distinct technical meanings ("effect" vs. "impact" vs. "association"), and variable names that shift across sections or between text and tables.

  6. Sample description consistency: Does the stated sample (years, number of observations, inclusion filters, unit of observation) remain the same across abstract, data section, results text, and table notes?

  7. Specification consistency: Do the fixed effects, controls, and clustering level described in the text match what the tables report?

  8. Cross-reference integrity: Every \ref{}, \autoref{}, \eqref{}, and \cref{} must resolve to a \label{} that exists in the listed files. Flag every dangling reference and every duplicate label. Also flag in-text references by number ("as shown in Table 3") that point to the wrong object.

  9. Citation integrity: For each in-text citation, verify the author-year pair has a matching entry in the .bib file (or in the typed reference list). Flag every citation with no matching entry, and every bibliography entry that is never cited.

  10. Leftover drafting artifacts: Placeholder text (XX, TBD, [insert], ???), duplicated sentences or paragraphs, and stale text referring to analyses or sections that no longer exist.

Output format:

Tag every issue [CRITICAL], [MAJOR], or [MINOR].

## Agent B: Internal Consistency & Cross-References

### Numerical and Factual Contradictions
[numbered list: [CRITICAL] Location 1 ("quoted text") ↔ Location 2 ("quoted text") | What conflicts | Which appears correct, or "cannot determine — authors must check"]

### Broken Cross-References and Citations
[numbered list: [CRITICAL] or [MAJOR] File, Section | The reference or citation | What is missing]

### Terminology and Sample Drift
[numbered list: [MAJOR] or [MINOR] Term or description | How it varies, with both quotes | Recommended standardization]

### Drafting Artifacts
[numbered list: [CRITICAL] or [MINOR] File, Section | "Quoted text" | Fix]

The .tex files to review are: [LIST ALL TEX FILE PATHS HERE] Table files: [LIST TABLE PATHS] Bibliography files: [LIST BIB PATHS, or note that references are typed inline]


Show full SKILL.md (659 more words)Show less
AGENT C — Unsupported Claims

You are a skeptical econometrician enforcing "claim discipline" — the principle that a sentence must never claim more than the evidence in the paper supports. Read all .tex files and flag every place where the paper overstates.

Work at the level of individual sentences. Do not evaluate whether the identification strategy is sound; take the paper's design as given and ask only whether each claim is licensed by it.

What to check:

  1. Causal language without causal identification: Every sentence applying causal language ("causes", "leads to", "drives", "determines", "because of", "due to", "results in", "the effect of") to a result that the design does not identify causally. Quote the exact sentence and explain why the language exceeds the design. Distinguish (a) causal language where only a correlation is shown from (b) mechanisms described as established when they are hypotheses.

  2. Mechanism claims stated as facts: Where the paper explains why a result holds, flag every instance where the proposed mechanism is asserted rather than framed as a hypothesis consistent with the evidence.

  3. Generalization beyond the sample: Claims extending findings past the data's scope — broad policy implications from one country or one period, current relevance for historical results without acknowledging how the context has changed, external validity asserted rather than argued.

  4. Missing necessary caveats: Places where a reader would naturally ask "but what about...?" and the paper does not answer. Focus on the standard threats for the design actually used: selection into the sample, reverse causality, measurement error, omitted variables, attrition.

  5. Statistical vs. economic significance: Places where statistical significance is reported but the magnitude is never interpreted, or where "significant" is used as though it means "important." Also flag results described as economically meaningful without a benchmark to scale them against.

  6. Unverified priority assertions: "No prior study has examined X", "We are the first to show Y", "the literature has overlooked Z". Flag every such claim as an unverified priority assertion the authors must confirm before submission. Do not attempt to judge whether it is true, and do not assert from your own knowledge that prior work exists — if you mention such work, label it [UNVERIFIED — authors must confirm] and never invent citation details.

  7. Hedging failures in both directions: claims stated too strongly for the evidence, and strong results buried under excessive hedging.

For each flagged sentence, give a concrete fix: either the weakened wording that the evidence does support, or the specific caveat sentence to add.

Output format:

Tag every issue [CRITICAL], [MAJOR], or [MINOR].

## Agent C: Unsupported Claims

### Causal Overclaiming
[numbered list: [CRITICAL] or [MAJOR] File, Section | "Exact quoted text" | Why it overclaims | Suggested rewording]

### Mechanisms Stated as Facts
[numbered list: [MAJOR] or [MINOR] same format]

### Generalization Beyond the Sample
[numbered list: [MAJOR] or [MINOR] same format]

### Missing Caveats
[numbered list: [CRITICAL] or [MAJOR] Topic | Where it should be addressed | Suggested sentence]

### Unverified Priority Assertions
[numbered list: [MAJOR] File, Section | "Exact quoted text" | What the authors must verify]

### Significance and Hedging
[numbered list: [MAJOR] or [MINOR] same format]

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


Phase 3: Consolidate and Save

Before consolidating, check for agent failures: if an agent returned nothing or malformed output, insert a placeholder section (e.g., "## 2. Internal Consistency — Agent did not return output") and say so in the summary to the user.

Deduplicate: the same sentence may be flagged by more than one agent (a causal overclaim in the abstract may also be a consistency problem). Report each defect once, under the agent that describes it most precisely, and note the second agent's angle in one clause.

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 prevents them from being picked up by future runs of this skill.

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

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

Report structure:

markdown
# Paper Check

**Paper**: [Title]
**Authors**: [Authors]
**Date**: [Today's date]
**Scope**: Mechanical check — spelling and grammar, internal consistency, unsupported claims. No assessment of contribution or identification.

---

## Summary

[2–3 sentences: how many defects were found in each category, and the single most consequential one. Describe what was found — do not rate the paper.]

**Counts**: [N] CRITICAL, [N] MAJOR, [N] MINOR

---

## 1. Unsupported Claims

[Agent C output]

---

## 2. Internal Consistency & Cross-References

[Agent B output]

---

## 3. Spelling, Grammar & Style

[Agent A output]

---

## Fix List

Collect every tagged item and rank: `[CRITICAL]` first (numerical contradictions and causal overclaiming before spelling), then `[MAJOR]`, then `[MINOR]`. Each line must be actionable on its own — location, the problem, and the fix.

**CRITICAL** (a reader or referee will notice these):
1. ...

**MAJOR** (fix before submission):
...

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

After saving, report to the user:

  1. Path to the saved report
  2. Counts by severity, broken out by the three categories
  3. The top 5 items from the Fix List
  4. Any checks that could not be run (missing table files, missing .bib, figures whose numbers could not be verified)

Then offer — do not do this automatically — to apply the unambiguous mechanical fixes (spelling, grammar, broken cross-references, typographic inconsistency) directly to the .tex files, leaving every claim-level item for the authors to judge.

© 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-checks of claesbackman/AI-research-feedback.

Open the folder on GitHubat commit d129756

Compare with similar skills

Review Paper Checks 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 Checks compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Paper Checks this skillclaesbackman/AI-research-feedback495—~4.3kAutomated safety check: NotesMIT
Research Writingalfonso0512/research-writing-skill4901 repos~818Automated safety check: PassMIT
Paper WritingMLNLP-World/Paper-Writing-Tips4.7k—~630Automated safety check: PassNone
Evomath TaoEvoScientist/EvoSkills4782 repos~3.8kAutomated safety check: PassApache-2.0
Math Modeling to EI Conference Paperjihe520/MathModelAgent6.2k—~688Automated safety check: PassNone
PaperjurySpark-To-Paper-Skills/paperjury1.2k—~5.3kAutomated safety check: PassMIT

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Questions about Review Paper Checks

What does Review Paper Checks do?

Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims. Review Paper Checks is an agent skill from claesbackman/AI-research-feedback. Run a fast 3-agent mechanical check of an economics paper — spelling and grammar, internal consistency and cross-references, and unsupported claims.

When should I use Review Paper Checks?

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

How do I install Review Paper Checks in Claude Code?

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

How do I install Review Paper Checks in Codex?

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

Can I use Review Paper Checks 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-checks -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-checks, .gemini/skills/review-paper-checks, .github/skills/review-paper-checks and .opencode/skills/review-paper-checks in your project.

What does Review Paper Checks need to run?

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

Does Review Paper Checks 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 Checks 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 Checks use?

Review Paper Checks 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 Checks use?

About 4.3k tokens (SKILL.md is roughly 17k 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 Checks?

Skills that share tags, products or a category with Review Paper Checks: Research Writing (alfonso0512/research-writing-skill, 490 stars), Paper Writing (MLNLP-World/Paper-Writing-Tips, 4.7k stars), Evomath Tao (EvoScientist/EvoSkills, 478 stars) and Math Modeling to EI Conference Paper (jihe520/MathModelAgent, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Paper Checks?

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.