145 lines
4.0 KiB
Markdown
145 lines
4.0 KiB
Markdown
---
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name: daily-paper-generator
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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.
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version: 0.5.1
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---
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# Daily Paper Generator
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## Overview
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Discover, screen, and summarize recent papers for any research topic.
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Supported sources:
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- arXiv
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- bioRxiv
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- both (`--source both`)
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Core workflow:
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1. Define topic query and time window
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2. Search papers from arXiv / bioRxiv
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3. Select Top 10 candidates per field
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4. Score and narrow to Top 3 per field
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5. Choose Top 1 per field
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6. Generate bilingual summaries
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7. Save outputs to `daily paper/`
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## When to Use
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Use this skill when:
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- The user asks for a daily/weekly paper digest on any topic
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- The user wants recent papers from arXiv and/or bioRxiv
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- The user needs structured bilingual notes for reading and tracking
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## Output Format
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Each summary should contain:
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1. Paper title
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2. Authors and venue/source
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3. Link(s) and date
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4. Chinese review (~300 words)
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5. English review (concise academic prose)
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6. Metadata table
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7. Appendix (optional resources)
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## Quick Reference
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| Task | Method |
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|---|---|
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| Search papers | Use `scripts/arxiv_search.py` with `--source arxiv|biorxiv|both` |
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| Topic selection | Use general-topic queries from `references/keywords.md` |
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| Evaluate quality | Use `references/quality-criteria.md` |
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| Write Chinese review | Use `references/writing-style.md` |
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| Write English review | Follow scientific writing best practices |
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## Workflow
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### Step 1: Define query
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Choose a concrete topic query. Examples:
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- `test-time adaptation for medical imaging`
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- `multimodal foundation model for healthcare`
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- `protein language model interpretability`
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### Step 2: Search arXiv and/or bioRxiv
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Use helper script:
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```bash
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python skills/daily-paper-generator/scripts/arxiv_search.py \
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--query "test-time adaptation for medical imaging" \
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--source both \
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--months 1 \
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--max-results 80 \
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--output /tmp/papers.json
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```
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Notes:
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- `--source arxiv`: arXiv only
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- `--source biorxiv`: bioRxiv only
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- `--source both`: merge both sources and sort by date
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### Step 3: Top 10 candidate selection (per field)
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For each candidate paper:
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1. Check topic relevance from title + abstract
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2. Remove obviously off-topic papers
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3. Keep **Top 10 candidates** for this field
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Minimum rule:
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- Do not jump directly from raw search results to final paper.
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- Keep an explicit Top 10 list first.
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### Step 4: Top 3 quality shortlist (per field)
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For the Top 10 pool:
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1. Score each paper with `references/quality-criteria.md`
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2. Rank by weighted score
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3. Keep **Top 3**
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### Step 5: Final Top 1 selection (per field)
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For the Top 3 shortlist:
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1. Compare novelty + method completeness + experimental credibility
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2. Check practical impact for the field
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3. Select **Top 1** as the final pick
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Required output trace:
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- Top 10 candidate list
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- Top 3 scored shortlist (with weighted scores)
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- Final Top 1 and one-paragraph selection rationale
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### Step 6: Generate bilingual summaries
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For each selected paper, generate:
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- 中文评语：背景、挑战、贡献、方法、结果、局限
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- English Review: concise, factual, non-formulaic
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### Step 7: Save output
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Recommended directory and naming:
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```text
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daily paper/
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YYYY-MM-DD-HHMM-paper-1.md
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YYYY-MM-DD-HHMM-paper-2.md
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YYYY-MM-DD-HHMM-paper-3.md
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```
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## Additional Resources
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- `references/keywords.md`: general-topic query templates
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- `references/quality-criteria.md`: scoring rubric
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- `references/writing-style.md`: review writing style
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- `example/daily paper example.md`: output example
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- `scripts/arxiv_search.py`: arXiv + bioRxiv search helper
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## Important Notes
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1. Use explicit topic queries, avoid single-word vague queries.
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2. Keep the time window explicit (`--months N`).
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3. Distinguish source in metadata (`arxiv` vs `biorxiv`).
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4. Use the fixed narrowing rule: **Top 10 -> Top 3 -> Top 1** (per field).
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5. If a paper lacks robust evaluation, mark confidence and limitations clearly.
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6. Do not fabricate unavailable fields (institution/GitHub/code links).
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