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Paper Check

Community
fcakyon
paper-check

Check a paper against its code, results and citations before submission (numbers, terms, code vs paper, formulas vs code, BibTeX and cited claims via DBLP and Crossref, eval integrity), and prepare its code for open-source release. Use when the user says "verify claims" / "verify paper against code", "check numbers" / "do the numbers match" / "are my numbers right", "formula vs code", "audit my paper", "cross-check claims or results", "paper consistency check", "check citations", "verify BibTeX", "publish code" / "open source release" / "code release", "structure a research repository for publication", "create reproducible research artifacts", "reproducibility checklist for a code release", or "prepare for publication".

Overview

Publisherfcakyon
Repositorylabrat
Skill namepaper-check
Stars
438
Forks
38
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fcakyon on GitHub. Read the source before you install it.

Installation

Install the Paper Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fcakyon/labrat/tree/main/plugin/skills/paper-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fcakyon/labrat.git /tmp/labrat
mkdir -p .claude/skills
cp -r /tmp/labrat/plugin/skills/paper-check .claude/skills/paper-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Paper Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Paper Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Paper Check

Every number, method claim and citation in the paper must trace to code, data, results or a cited source. Output a prioritized list of issues with specific fixes.

For a code release request, follow references/code-release.md and skip Steps 1-4.

Step 1: Discover Files

  1. Find the paper's .tex files (Glob for **/*.tex)
  2. Identify the main .tex file (usually imports other sections) and the document structure
  3. Find .bib files for citation checking
  4. Locate result files (JSON, CSV, logs), configs, evaluation scripts and training logs
  5. Identify the key code modules referenced by the paper's methods

When you run the checks yourself, read the full paper, or the sections the user names. When you hand checks to subagents, pass them the file paths from items 1-5 instead.

Step 2: Run the Six Checks

Run all six checks by default. When the user names specific checks (for example only citations or BibTeX), run only those, and only the Step 1 items they need (check 5 needs just the .bib files and the \cite sites).

The checks are independent. If the harness supports subagents, they can run in parallel, one check each. Each check returns a structured list of findings.

1. Numbers

For every number in the paper (dataset sizes, metric values, percentages, counts):

  1. Extract the number and its context (section, sentence) from the .tex file
  2. Trace it to its source: result file, code output, config, log, or tracking system
  3. Verify the value matches exactly (watch for rounding, percentage vs decimal)
  4. Note rounding conventions and check they are consistent
  5. Flag any number that does not match its source or cannot be traced

Record each number as a row of the Numerical Audit table in Output Format.

Common numerical errors:

  • Rounding inconsistencies (3.14 in text, 3.1415 in table)
  • Stale numbers from earlier experiments not updated after re-runs
  • Percentage vs absolute confusion
  • Off-by-one in dataset counts (headers counted, or not)

2. Terms

  1. Extract all defined terms from the methods section
  2. Search for each term across ALL sections
  3. Flag any inconsistent usage:
    • Same concept, different names (e.g., "tag head" vs "classification head")
    • Same name, different meanings across sections
    • Defined but never used, or used but never defined

3. Code vs Paper

For each method or algorithm described in the paper:

  1. Find the corresponding code (function, class, module)
  2. Compare the paper's description with the actual implementation
  3. Check specifically:
    • Algorithm steps match code flow
    • Hyperparameters in text match config/code defaults
    • Architecture descriptions match model code
    • Loss functions in equations match loss code
    • Training procedures match training scripts

Common mismatches:

  • Paper describes an idealized version, code has edge cases not mentioned
  • Hyperparameters changed during development but paper not updated
  • Paper describes a method that was later modified or removed from code

4. Formulas vs Code

For each equation in the paper:

  1. Identify the equation and its variables
  2. Find the code that implements it
  3. Map each mathematical operation to its code equivalent
  4. Verify:
    • Summation bounds match loop bounds
    • Division operations handle edge cases
    • Normalization factors match
    • Gradient flow matches (detach, no_grad)
    • Reduction operations (mean vs sum) match

5. Citations

Read the .bib file(s). Search the .tex files for citation patterns (\cite{...}, \citep{...}, \citet{...}) to find where each entry is used. Check every entry. For a quick pass, spot-check the 5 most important citations.

  1. Extract the claim and the cited paper key.

    Use paper-search tool get_crossref_paper_by_doi <doi> to retrieve the cited record. For an arXiv paper, use paper-search download arxiv <id> and paper-search read arxiv <id>. Follow paper search setup if the CLI is missing. Inspect the PDF itself when text extraction loses table or equation layout.

  2. Verify BibTeX metadata against DBLP. Search DBLP for the paper title via web search (query: site:dblp.org "paper title") and check:

    • Author names (exact spelling, correct order, completeness)
    • Paper title (exact, from the published version, not the preprint title), and cite the published version when one exists
    • Venue (conference or journal name) and year (publication year, not submission or preprint year), confirmed against the actual publication

    Flag any discrepancy with the corrected value.

  3. For cited claims with specific numbers:

    • Locate the exact table, figure or text in the cited paper
    • Verify the number matches what the citing paper states
    • If the claim cannot be verified, or the cited paper does not make the claim at all, suggest qualitative language instead

6. Eval Integrity

Read the evaluation scripts and result files. Check for:

  • Data leakage between train/val/test splits
  • Off-by-one errors in metric computation
  • Wrong aggregation methods (micro vs macro)
  • Mismatched ground-truth and prediction alignment
  • Zero-division edge cases
  • Results that don't reproduce from the evaluation scripts

Run the evaluation if possible and compare its output to the paper's values.

Step 3: Merge Findings

Merge all findings into one prioritized list:

  • HIGH (must fix before submission): incorrect numbers, wrong claims, missing citations
  • MEDIUM (should address): terminology inconsistencies, stale but close numbers
  • LOW (nice to have): minor formatting, optional improvements

Before reporting, check the findings:

  1. Every flagged issue is backed by specific evidence (file path, line number, exact value)
  2. Numerical comparisons are exact, not approximate
  3. Verified issues are kept apart from suspected issues
  4. Each suggested fix is specific enough to act on

Refine any finding that fails these checks.

Output Format

## Paper Check Report

### Summary
- Files checked: N .tex files, M code files, K result files
- Issues found: X HIGH, Y MEDIUM, Z LOW

### HIGH
1. [Check] Description
   - Paper says: "..." (file:line)
   - Code/data shows: "..." (file:line)
   - Suggested fix: specific replacement text

### MEDIUM
[Same format]

### LOW
[Same format]

### Numerical Audit
| Paper claim | Location (.tex) | Source file/code | Source value | Match? |
|-------------|-----------------|-----------------|-------------|--------|

### Unverified Claims
| .tex location | Claim | Cited paper | Status | Suggestion |
|--------------|-------|------------|--------|------------|

### Corrected BibTeX Entries
(Ready-to-paste corrected entries for any that need fixing, each with a comment naming its DBLP or Crossref source)

### Verified Claims
(Claims checked and correct)

Tag each issue with its check name.

Step 4: Auto-Fix Offer

For HIGH priority issues that have clear fixes (wrong numbers, typos, stale values):

  • Show the exact .tex location and current text
  • Show the verified correct value from the source
  • Ask the user for confirmation before applying fixes

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Paper Check AI skill do?

Check a paper against its code, results and citations before submission (numbers, terms, code vs paper, formulas vs code, BibTeX and cited claims via DBLP and Crossref, eval integrity), and prepare its code for open-source release. Use when the user says "verify claims" / "verify paper against code", "check numbers" / "do the numbers match" / "are my numbers right", "formula vs code", "audit my paper", "cross-check claims or results", "paper consistency check", "check citations", "verify BibTeX", "publish code" / "open source release" / "code release", "structure a research repository for p...

Why use Paper Check on TypingMind?

Because you install it once and use it with any model. Paper Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Paper Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/labrat/tree/main/plugin/skills/paper-check. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paper Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Paper Check?

As many as you like. As long as a model supports skills, you can use Paper Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Paper Check AI skill free?

Yes. It is published on GitHub by fcakyon under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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