paper-claim-audit

paper-claim-audit is a skill for Claude Code from raja21068/AutoResearch. It costs 77 tokens per session (3,170 once invoked), scanned A, a copy of paper-claim-audit, MIT.

A verification workflow that checks every number, comparison, and scope claim in a research paper against the original result files. It uses a reviewer with no background context to reduce confirmation bias.

In plain words
What is it for?
Use it before submission to audit paper text against raw experimental results.
Why use it?
It catches reporting mistakes such as incorrect rounding, using the wrong experiment, or claiming an improvement that the data does not show.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions Codex.

Good fit Use it before submission to audit paper text against raw experimental results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/raja21068/autoresearch/paper-claim-audit
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 raja21068/AutoResearch --skill paper-claim-audit
Clone the repo
git clone --depth 1 https://github.com/raja21068/AutoResearch

Made for: Claude Code.

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 paper-claim-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-claim-audit/github.svg)](https://agentmods.dev/skills/raja21068/autoresearch/paper-claim-audit)
Your own site
<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-claim-audit/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 paper-claim-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-claim-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,170 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 89% copy Near-identical to another mod 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 $0.00077 $0.03170
Opus 5 $0.00039 $0.01585
Sonnet 5 $0.00015 $0.00634
Haiku 4.5 $0.00008 $0.00317

Measured 8d ago against content hash 834e017fb1f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

paper-claim-audit 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 8d 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.

Origin

This is a copy

89% identical to paper-claim-audit — 46 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/aris/paper-claim-audit/SKILL.md · 327 lines

How it starts

The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Paper Claim Audit: Zero-Context Evidence Verification

Verify that every claim in the paper matches raw evidence for: $ARGUMENTS

Why This Exists

The executor writes experiments AND writes the paper. It "knows" what the results should be. This creates confirmation bias:

  • Rounding 84.7% up to 85.3%
  • Reporting best seed instead of average
  • Citing metrics from a different experiment config
  • Claiming "improves by 15%" when the delta is actually 12.8%

A fresh reviewer with zero prior context catches these because it has no expectations — it just compares paper text vs raw files.

How This Differs From Other Audit Skills

Skill Question it answers
/experiment-audit Is the experiment code honest? (fake GT, normalization fraud)
/result-to-claim Does the data scientifically support this claim?
/paper-claim-audit Does the paper report the data truthfully and precisely?

Core Principle

Zero-context, fresh reviewer. The auditor receives ONLY:

  • Paper .tex files (the claims)
  • Raw result files (the evidence)

It does NOT receive:

  • ❌ EXPERIMENT_LOG.md
  • ❌ EXPERIMENT_TRACKER.md
  • ❌ AUTO_REVIEW.md
  • ❌ NARRATIVE_REPORT.md
  • ❌ Any executor summary or interpretation
  • ❌ Any prior audit results
  • ❌ Any conversation history

This is stricter than reviewer-independence — it's zero-context evidence audit.

Workflow

Step 1: Collect Files (Executor — Claude)

Locate paper and result files WITHOUT reading or interpreting them.

Paper files (claims) — paths shown relative to the shell's working directory so you can find them with ls; when writing them into audited_input_hashes, use paths relative to the paper dir (no paper/ prefix) per the "Submission Artifact Emission" section below:

paper/main.tex                # → hash key: main.tex
paper/sections/*.tex          # → hash key: sections/*.tex
paper/tables/*.tex (if separate)   # → hash key: tables/*.tex

Result files (evidence):

results/*.json, results/*.jsonl, results/*.csv, results/*.tsv
outputs/*.json, outputs/*.csv
wandb-summary.json (if exists)
**/metrics.json, **/eval_results.json
**/config.yaml, **/args.json (experiment configs)

Read the full file on GitHub · 327 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. 8d ago First seen · 327 lines · 77 tokens per session scan A 834e017fb1f2

Subscribe to this mod's changes

paper-claim-audit is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 3,170 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to paper-claim-audit, differing in 46 lines, and is treated as a copy.

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