/daily-paper-generator

name: daily-paper-generator
description: 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.
version: 0.5.1

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

| Task | Method | |---|---| | Search papers | Use `scripts/arxiv_search.py` with `--source arxiv|biorxiv|both` | | Topic selection | Use general-topic queries from `references/keywords.md` | | Evaluate quality | Use `references/quality-criteria.md` | | Write Chinese review | Use `references/writing-style.md` | | Write English review | Follow 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).