Agent skill

Paper Verification

by fcakyon in fcakyon/phd-skills

A skill your agent uses when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy.

MITAuto-check passedResearch & Science

Install Paper Verification

skills CLI
$ npx skills add fcakyon/phd-skills --skill paper-verification -a claude-code

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

GitHub CLI
$ gh skill install fcakyon/phd-skills paper-verification --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/fcakyon/phd-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/paper-verification .claude/skills/paper-verification && 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
paper-verification
GitHub stars
414
Token cost
~1.2k tokens
SKILL.md length
553 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy.

  • Works in 5 steps: Numerical Accuracy Audit → Terminology Consistency Audit → Code-Paper Alignment → …
  • The user wants to verify paper claims against code
  • SKILL.md covers Verification Dimensions, Verification Process and Output Format
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Paper Verification is an agent skill from fcakyon/phd-skills. Use when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy. Triggers on phrases like "verify claims", "check numbers", "do the numbers match", "formula vs code", "audit the paper", or "cross-check results".

Its SKILL.md is about 1.2k 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 Research & Science, covering Fact-checking and source verification and Citation management. It works with Visual Studio Code. The repository describes itself as: PhD Research Skills for Claude Code: paper reproduction, experiment design, paper review, result comparison and more. The licence is MIT.

When your agent uses it

  • The user wants to verify paper claims against code
  • Audit numerical accuracy
  • Check formula-code alignment
  • Validate citation accuracy

Example prompts

  • “verify claims”
  • “check numbers”
  • “do the numbers match”
  • “/paper-verification”

Workflow steps

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

  1. Numerical Accuracy Audit
  2. Terminology Consistency Audit
  3. Code-Paper Alignment
  4. Formula-Code Verification
  5. Citation Fact-Checking Protocol

What it can do on your machine

Read from SKILL.md and the folder at commit 67acd61. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    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

Paper Verification loads about 1.2k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 553 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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 passed

The automated check found no risky patterns in SKILL.md.

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 fcakyon/phd-skills at commit 67acd61, republished under its MIT licence (© fcakyon). 553 words, ~1,170 tokens.

Download SKILL.mdSave it as .claude/skills/paper-verification/SKILL.md (or your agent's skills folder).
name
paper-verification
description
Use when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy. Triggers on phrases like "verify claims", "check numbers", "do the numbers match", "formula vs code", "audit the paper", or "cross-check results".

Paper Verification Methodology

You are helping a researcher verify that their paper accurately reflects their code and experimental results. This is the most critical quality control step in academic writing.

Verification Dimensions

1. Numerical Accuracy Audit

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

  1. Extract the number and its context from the .tex file
  2. Trace it to its source: code output, result file, log, or tracking system
  3. Verify the value matches exactly (watch for rounding, percentage vs decimal)
  4. Flag any number that cannot be traced to a source

Template:

| Paper claim | Location (.tex) | Source file/code | Source value | Match? |
|-------------|-----------------|-----------------|-------------|--------|
| "13,999 frames" | abstract L3 | len(glob(labels/*.json)) | ? | ? |
| "4.2% improvement" | Table 2 | eval_results.json | ? | ? |

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. Terminology Consistency Audit
  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-Paper Alignment

For each method 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. Formula-Code Verification

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
Show full SKILL.md (224 more words)Show less
5. Citation Fact-Checking Protocol

For each citation in the paper:

Step 1: Extract the claim and the cited paper Step 2: Verify BibTeX metadata against DBLP:

  • Author names (exact spelling, correct order)
  • Paper title (exact, from published version not preprint)
  • Venue and year (confirmed against actual publication)

Step 3: For cited claims with specific numbers:

  • Locate the exact table/figure in the cited paper
  • Verify the number matches what the citing paper states
  • If the number cannot be confirmed, suggest qualitative language instead

Step 4: Check for common citation errors:

  • Citing preprint when published version exists
  • Wrong year (submission vs publication)
  • Author name misspellings
  • Citing for a claim the paper doesn't actually make

Verification Process

  1. Read the full paper (or specified sections)
  2. Build the verification table for each dimension
  3. For each entry, read the source and verify
  4. Produce a prioritized issue list:
    • HIGH: Incorrect numbers, wrong claims, missing citations
    • MEDIUM: Terminology inconsistencies, stale but close numbers
    • LOW: Minor formatting, optional improvements

Output Format

Produce a structured verification report:

  1. Summary: X issues found (Y high, Z medium, W low)
  2. Numerical audit table: each number with source and match status
  3. Terminology issues: inconsistent terms with locations
  4. Code-paper mismatches: description vs implementation gaps
  5. Citation issues: metadata errors and unverified claims
  6. Suggested fixes: specific text replacements for each issue

© fcakyon, 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 plugin/skills/paper-verification of fcakyon/phd-skills.

Open the folder on GitHubat commit 67acd61

Compare with similar skills

Paper Verification 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.

Paper Verification compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Paper Verification this skillfcakyon/phd-skills414—~1.2kAutomated safety check: PassMIT
Citation Verification GuideGalaxy-Dawn/claude-scholar5.7k3 repos~1.9kAutomated safety check: PassMIT
Article Fact Checkerdigoal/blog8.6k—~939Automated safety check: PassGPL-2.0
Citation VerificationLight0305/Light-skills641—~3.4kAutomated safety check: PassMIT
Grounded CitationsNousResearch/hermes-agent252k—~3.1kAutomated safety check: PassMIT
Reference VerifierYuan1z0825/nature-skills46k2 repos~1.4kAutomated safety check: PassApache-2.0

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Questions about Paper Verification

What does Paper Verification do?

A skill your agent uses when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy. Paper Verification is an agent skill from fcakyon/phd-skills. Use when the user wants to verify paper claims against code or data, audit numerical accuracy, check formula-code alignment, or validate citation accuracy.

When should I use Paper Verification?

Paper Verification fits situations like: the user wants to verify paper claims against code; audit numerical accuracy; check formula-code alignment; validate citation accuracy.

How do I install Paper Verification in Claude Code?

Run `npx skills add fcakyon/phd-skills --skill paper-verification -a claude-code`. Or copy the skill folder (plugin/skills/paper-verification in fcakyon/phd-skills) into .claude/skills/paper-verification in your project. Claude Code loads it when a task matches its description.

How do I install Paper Verification in Codex?

Run `npx skills add fcakyon/phd-skills --skill paper-verification -a codex`. Or copy the skill folder (plugin/skills/paper-verification in fcakyon/phd-skills) into .agents/skills/paper-verification in your project. Codex loads it when a task matches its description.

Can I use Paper Verification 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 fcakyon/phd-skills --skill paper-verification -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/paper-verification, .gemini/skills/paper-verification, .github/skills/paper-verification and .opencode/skills/paper-verification in your project.

What does Paper Verification need to run?

SKILL.md names no scripts, command-line tools or credentials: Paper Verification is instructions for the agent only.

Does Paper Verification 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 Paper Verification safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Paper Verification use?

Paper Verification 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 Paper Verification use?

About 1.2k tokens (SKILL.md is roughly 4.7k 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 Paper Verification?

Skills that share tags, products or a category with Paper Verification: Citation Verification Guide (Galaxy-Dawn/claude-scholar, 5.7k stars), Article Fact Checker (digoal/blog, 8.6k stars), Citation Verification (Light0305/Light-skills, 641 stars) and Grounded Citations (NousResearch/hermes-agent, 252k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Paper Verification?

fcakyon (a GitHub user) maintains it in fcakyon/phd-skills, which has 414 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 16, 2026.

Source: fcakyon/phd-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.