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daily-paper-generator

Use when the user asks to generate daily paper digests on a general topic. This skill supports both arXiv and bioRxiv (or either one), then produces structured Chinese/English summaries for selected papers.

インストール方法を見る

含まれるファイル(6)

  • SKILL.md4.0 KB
  • example/daily paper example.md6.1 KB
  • references/keywords.md1.7 KB
  • references/quality-criteria.md4.4 KB
  • references/writing-style.md4.6 KB
  • scripts/arxiv_search.py9.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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).

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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