paper-analyzer
installsforks
How to install paper-analyzer?
boom5426/nature-paper-skills/paper-analyzer
$npx -y skills add Boom5426/Nature-Paper-Skills --skill paper-analyzerUse when deeply analyzing a single paper and producing structured notes on claims, methods, figures, evaluation, strengths, limitations, and related work.
SKILL.md
| 1 | # Paper Analyzer |
| 2 | |
| 3 | ## Overview |
| 4 | |
| 5 | Perform deep analysis of a specific paper, generating structured notes that cover claims, methodology, experiment evaluation, strengths and limitations, and links to adjacent work. |
| 6 | |
| 7 | # Workflow |
| 8 | |
| 9 | ## Step 1: Identify Paper |
| 10 | Accept input: arXiv ID (e.g., "2402.12345"), full ID ("arXiv:2402.12345"), paper title, or file path. |
| 11 | |
| 12 | ## Step 2: Fetch Paper Content |
| 13 | ```bash |
| 14 | curl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdf |
| 15 | curl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gz |
| 16 | curl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html |
| 17 | ``` |
| 18 | |
| 19 | ## Step 3: Deep Analysis |
| 20 | Analyze: abstract, methodology, experiments, results, contributions, limitations, future work, related papers. |
| 21 | |
| 22 | ## Step 4: Generate Note |
| 23 | ```bash |
| 24 | python scripts/generate_note.py --paper-id "$PAPER_ID" --title "$TITLE" --authors "$AUTHORS" --domain "$DOMAIN" |
| 25 | ``` |
| 26 | |
| 27 | ## Step 5: Update Knowledge Graph |
| 28 | ```bash |
| 29 | python scripts/update_graph.py --paper-id "$PAPER_ID" --title "$TITLE" --domain "$DOMAIN" --score $SCORE |
| 30 | ``` |
| 31 | |
| 32 | # Scripts |
| 33 | - `scripts/generate_note.py` — Generate structured note template |
| 34 | - `scripts/update_graph.py` — Update paper relationship graph |
| 35 | |
| 36 | # Note Structure |
| 37 | The generated note includes: core info, abstract (EN/CN), research background, method overview with architecture figures, experiment results with tables, deep analysis, related paper comparison, tech roadmap positioning, future work, and comprehensive evaluation (0-10 scoring). |
| 38 | |
| 39 | # Dependencies |
| 40 | - Python 3.8+, PyYAML, requests |
| 41 | - Network access (arXiv) |
| 42 | |
| 43 | --- |
| 44 | > Based on [evil-read-arxiv](https://github.com/evil-read-arxiv) — an automated paper reading workflow. MIT License. |
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