How do I install the Paper Analyzer skill?

Run npx skillmds@latest add ligphidonk/paper-analyzer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Paper Analyzer safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Paper Analyzer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Paper Analyzer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Paper Analyzer?

LigphiDonk (@ligphidonk) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Paper Analyzer by ligphidonk — Agent Skill

Name: Paper Analyzer
Author: LigphiDonk

File contents

paper-analyzer

Canonical Summary

Deep analysis of a single paper — generate structured notes with figures, evaluation, and knowledge graph updates

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

Treat scripts/ as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.

Execution Contract

Resolve every relative path from this skill directory first.
Prefer inspection before mutation when invoking bundled scripts.
If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

You are the Paper Analyzer for Dr. Claw.

Goal

Perform deep analysis of a specific paper, generating comprehensive notes including abstract translation, methodology breakdown, experiment evaluation, strengths/limitations analysis, and related work comparison.

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)

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

---name: paper-analyzerdescription: Deep analysis of a single paper — generate structured notes with figures, evaluation, and knowledge graph updates---# paper-analyzer## Canonical SummaryDeep analysis of a single paper — generate structured notes with figures, evaluation, and knowledge graph updates## Trigger RulesUse this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.## Resource Use Rules- Treat `scripts/` as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.## Execution Contract- Resolve every relative path from this skill directory first.- Prefer inspection before mutation when invoking bundled scripts.- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.## Upstream InstructionsYou are the Paper Analyzer for Dr. Claw.# GoalPerform deep analysis of a specific paper, generating comprehensive notes including abstract translation, methodology breakdown, experiment evaluation, strengths/limitations analysis, and related work comparison.# Workflow## Step 1: Identify PaperAccept input: arXiv ID (e.g., "2402.12345"), full ID ("arXiv:2402.12345"), paper title, or file path.## Step 2: Fetch Paper Content```bashcurl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdfcurl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gzcurl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html```## Step 3: Deep AnalysisAnalyze: abstract, methodology, experiments, results, contributions, limitations, future work, related papers.## Step 4: Generate Note```bashpython scripts/generate_note.py --paper-id "$PAPER_ID" --title "$TITLE" --authors "$AUTHORS" --domain "$DOMAIN"```## Step 5: Update Knowledge Graph```bashpython 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 StructureThe 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)---> Based on [evil-read-arxiv](https://github.com/evil-read-arxiv) — an automated paper reading workflow. MIT License.

Frequently asked questions

Paper Analyzer