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Verify each cited reference exists via web search.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Organize a paper PDF and its codebase into clean reading artifacts. Use this before any other read-* skills.
Deep extraction of all verifiable claims from a research paper. Actively proposes implicit claims, splits compound statements, and flags cross-reference inconsistencies.
Verify paper claims with evidence_type "code" against the actual codebase. Use after read-txt to check if code matches what the paper says.
Review experiment design and produce a prioritized reproduction plan. Targets claims with evidence_type "experiment" from check_claim.json. Output feeds into run-exp.
Web-search validation of citation and novelty claims from check_claim.json. Checks whether cited facts are accurate and novelty claims ("first", "pioneering") hold against published literature.
Verify theoretical claims — mathematical derivations, proofs, and formal arguments. Targets claims with evidence_type "theoretical" from check_claim.json.
| name | read-bib |
| description | Verify each cited reference exists via web search. |
| allowed-tools | Read, WebSearch |
Read the input references.json. For each entry, WebSearch its raw_text and record found or not_found.
Each entry's id, year, raw_text fields should be copied as-is into the output — do not modify them.
When status is warning or error, set quote to the URL(s) found during search (e.g. the correct arXiv/venue page) followed by a blank line and the original raw_text of the citation, so reviewers see both the candidate fix-up source and the original entry. Also include a suggest field with a concrete one-sentence fix the author can apply (e.g. "Update the year to 2025" or "Replace with the correct arXiv ID").
Input: {paper_dir}/metadata/paper/references.json
Output: {paper_dir}/reports/check_bib.json
{
"summary": { "total": 42, "pass": 38, "warning": 2, "error": 2 },
"results": [
{ "id": "ref-001", "year": "2016", "raw_text": "[1] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In CVPR, pages 5288–5296, 2016.", "status": "pass" },
{ "id": "ref-005", "year": "2020", "raw_text": "[5] ...", "status": "warning", "reason": "title found but venue/year differs", "quote": "https://arxiv.org/abs/...\n\n[5] ...", "suggest": "Update the year to 2021 to match the venue listing." },
{ "id": "ref-007", "year": "2017", "raw_text": "[7] ...", "status": "error", "reason": "not found or search returned no match", "quote": "[7] ...", "suggest": "Replace with a verified citation or remove the reference." }
]
}