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daily-paper-generator日报生成器

Agent Skill

daily-paper-generator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,932

周安装

83

GitHub Stars

3,515

下载量

677
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:daily-paper-generator(日报生成器)
来源仓库:https://github.com/galaxy-dawn/claude-scholar
仓库路径:skills/daily-paper-generator
安装命令:
npx skills add https://github.com/galaxy-dawn/claude-scholar --skill daily-paper-generator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/galaxy-dawn/claude-scholar --skill daily-paper-generator

简介

daily-paper-generator 自动检索 arXiv 与 bioRxiv 的最新论文,筛选 Top 10 并按领域打分优选 Top 3。

  • 适合科研人员、AI 从业者或学生每日跟踪特定研究方向进展,节省文献调研时间。
  • 输出包含双语摘要与评分依据,支持保存为本地文件供离线阅读与笔记整理。
  • 时间窗口默认为最近一周,可通过参数调整回溯更长周期以满足深度调研需求。
  • 检索结果受学术数据库收录延迟影响,重要突破可能暂未上线,需辅以其他信源交叉验证。

SKILL.md

Daily Paper Generator

Overview

Discover, screen, and summarize recent papers for any research topic.

Supported sources:

  • arXiv
  • bioRxiv
  • both (--source both)

Core workflow:

  1. Define topic query and time window
  2. Search papers from arXiv / bioRxiv
  3. Select Top 10 candidates per field
  4. Score and narrow to Top 3 per field
  5. Choose Top 1 per field
  6. Generate bilingual summaries
  7. Save outputs to daily paper/

When to Use

Use this skill when:

  • The user asks for a daily/weekly paper digest on any topic
  • The user wants recent papers from arXiv and/or bioRxiv
  • The user needs structured bilingual notes for reading and tracking

Output Format

Each summary should contain:

  1. Paper title
  2. Authors and venue/source
  3. Link(s) and date
  4. Chinese review (~300 words)
  5. English review (concise academic prose)
  6. Metadata table
  7. Appendix (optional resources)

Quick Reference

TaskMethod
Search papersUse scripts/arxiv_search.py with `--source arxiv
Topic selectionUse general-topic queries from references/keywords.md
Evaluate qualityUse references/quality-criteria.md
Write Chinese reviewUse references/writing-style.md
Write English reviewFollow scientific writing best practices

Workflow

Step 1: Define query

Choose a concrete topic query. Examples:

  • test-time adaptation for medical imaging
  • multimodal foundation model for healthcare
  • protein language model interpretability

Step 2: Search arXiv and/or bioRxiv

Use helper script:

python skills/daily-paper-generator/scripts/arxiv_search.py \
  --query "test-time adaptation for medical imaging" \
  --source both \
  --months 1 \
  --max-results 80 \
  --output /tmp/papers.json

Notes:

  • --source arxiv: arXiv only
  • --source biorxiv: bioRxiv only
  • --source both: merge both sources and sort by date

Step 3: Top 10 candidate selection (per field)

For each candidate paper:

  1. Check topic relevance from title + abstract
  2. Remove obviously off-topic papers
  3. Keep Top 10 candidates for this field

Minimum rule:

  • Do not jump directly from raw search results to final paper.
  • Keep an explicit Top 10 list first.

Step 4: Top 3 quality shortlist (per field)

For the Top 10 pool:

  1. Score each paper with references/quality-criteria.md
  2. Rank by weighted score
  3. Keep Top 3

Step 5: Final Top 1 selection (per field)

For the Top 3 shortlist:

  1. Compare novelty + method completeness + experimental credibility
  2. Check practical impact for the field
  3. Select Top 1 as the final pick

Required output trace:

  • Top 10 candidate list
  • Top 3 scored shortlist (with weighted scores)
  • Final Top 1 and one-paragraph selection rationale

Step 6: Generate bilingual summaries

For each selected paper, generate:

  • 中文评语:背景、挑战、贡献、方法、结果、局限
  • English Review: concise, factual, non-formulaic

Step 7: Save output

Recommended directory and naming:

daily paper/
  YYYY-MM-DD-HHMM-paper-1.md
  YYYY-MM-DD-HHMM-paper-2.md
  YYYY-MM-DD-HHMM-paper-3.md

Additional Resources

  • references/keywords.md: general-topic query templates
  • references/quality-criteria.md: scoring rubric
  • references/writing-style.md: review writing style
  • example/daily paper example.md: output example
  • scripts/arxiv_search.py: arXiv + bioRxiv search helper

Important Notes

  1. Use explicit topic queries, avoid single-word vague queries.
  2. Keep the time window explicit (--months N).
  3. Distinguish source in metadata (arxiv vs biorxiv).
  4. Use the fixed narrowing rule: Top 10 -> Top 3 -> Top 1 (per field).
  5. If a paper lacks robust evaluation, mark confidence and limitations clearly.
  6. Do not fabricate unavailable fields (institution/GitHub/code links).

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.73%
按下载量换算249

Claude

27.68%
按下载量换算187

Cursor

19.05%
按下载量换算129

Gemini CLI

9.09%
按下载量换算62

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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