paper-claim-audit

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Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity.

AI & Automation 14,964 stars 1313 forks Updated today MIT

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Skill Content

# Paper Claim Audit: Zero-Context Evidence Verification > 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It is > verdict-bearing — it judges paper-to-evidence fidelity with a deliberately > zero-context fresh reviewer. Re-firing that verdict on a wall-clock timer adds > no new signal (it changes only when the *paper or results* change). Schedule > the *external wait that precedes it* — paper draft ready → then audit > **once**. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md). 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 rev...

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Author
wanshuiyin
Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Created
5 months ago
Last Updated
today
Language
Python
License
MIT

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paper-claim-audit

Zero-context verification that every number, comparison, and scope claim in the paper matches raw result files. Uses a fresh cross-model reviewer with NO prior context to prevent confirmation bias. Use when user says "审查论文数据", "check paper claims", "verify numbers", "论文数字核对", or before submission to ensure paper-to-evidence fidelity. Do not use for code/data/formula verification (use paper-verification).

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Zero-context verification that every bibliographic entry in the paper is real, correctly attributed, and used in a context the cited paper actually supports. Uses a fresh cross-model reviewer with web/DBLP/arXiv lookup to catch hallucinated authors, wrong years, fabricated venues, version mismatches, and wrong-context citations (cite present but the cited paper does not establish the claim). Use when user says "审查引用", "check citations", "citation audit", "verify references", "引用核对", or before submission to ensure bibliography integrity.

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