AGENT SKILL

paper.rate

Rate an academic paper from 1 to 5 by page length, affiliation, citations

License 许可
Apache-2.0
所属仓库
causify-ai/helpers
最近更新
2026-11-02
Stars
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Forks
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01 前言 Front Matter

model
sonnet
description
Rate an academic paper from 1 to 5 by page length, affiliation, citations

03 SKILL.md 正文

Goal

  • Rate the passed paper from 1 (low) to 5 (best) using its page length, the affiliation of its authors, and its number of citations
  • Follow the rules in .claude/skills/paper.rules.md, # Paper Rating

Workflow

Get the Paper

  • Use the PDF passed by the user
  • If the input is a URL, an arXiv ID, or a DOI, download only the PDF with download_academic_paper_to_md.py --input <INPUT> --clear_actions --action download

Collect the Signals

  • Count the pages of the PDF
  • Read the affiliations of all the authors on the first page
  • Look up the citation count in Semantic Scholar for the DOI or the arXiv ID, and note the date
  • Do not use memory for any of these values

Rate

  • Score each signal and compute the rating with the tables in .claude/skills/paper.rules.md

Write the Rating File

  • Save <BASE>.rating.md next to the paper, where <BASE> is the name of the PDF without .pdf
  • Follow the template .claude/templates/paper_rating.template.md
  • Report the rating and the three scores to the user

Constraints

  • Use whole numbers only
  • If a signal cannot be found, write unknown and leave it out of the mean

Verification

  • [ ] The rating is an integer from 1 to 5
  • [ ] Each signal has a value and a score
  • [ ] The citation count has a source and a date
  • [ ] The rating equals the mean of the known scores, rounded half up

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