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name: paper-analyzer description: Use when deeply analyzing a single paper and producing structured notes on claims, methods, figures, evaluation, strengths, limitations, and related work.

Paper Analyzer

Overview

Perform deep analysis of a specific paper, generating structured notes that cover claims, methodology, experiment evaluation, strengths and limitations, and links to adjacent work.

Workflow

Step 1: Identify Paper

Accept input: arXiv ID (e.g., "2402.12345"), full ID ("arXiv:2402.12345"), paper title, or file path.

Step 2: Fetch Paper Content

curl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdf
curl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gz
curl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html

Step 3: Deep Analysis

Analyze: abstract, methodology, experiments, results, contributions, limitations, future work, related papers.

Step 4: Generate Note

python scripts/generate_note.py --paper-id "$PAPER_ID" --title "$TITLE" --authors "$AUTHORS" --domain "$DOMAIN"

Step 5: Update Knowledge Graph

python scripts/update_graph.py --paper-id "$PAPER_ID" --title "$TITLE" --domain "$DOMAIN" --score $SCORE

Scripts

• scripts/generate_note.py — Generate structured note template
• scripts/update_graph.py — Update paper relationship graph

Note Structure

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).

Dependencies

• Python 3.8+, PyYAML, requests
• Network access (arXiv)

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Based on evil-read-arxiv — an automated paper reading workflow. MIT License.