alphalab-ustc/researchclaw/paper-reader

Claude Code and agent skill by alphalab-ustc/researchclaw

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Deep-read an arXiv paper and generate structured Deep Note reading notes. Default output: markdown deep note file (low token cost, git-friendly). Optional: HTML page from template. Trigger: 帮我读一下, DNL, 论文速读, paper notes, or an arXiv link.

Install

npx vskill@latest install alphalab-ustc/researchclaw/paper-reader

SKILL.md

Paper Reader — Deep Reading Notes (v3.1)

Overview

Given an arXiv paper, produce a structured deep reading note as a markdown file. Optionally generate an HTML page from the paper-note template.

Default output format: Markdown Deep Note (saves tokens, git-friendly, composable). HTML output: Add --html or say 生成 HTML to also produce an HTML page.

Triggers: arXiv link · 帮我读一下 · DNL · 论文速读 · paper notes


Step 0 — Load Research Profile (Always First)

Before running, load: ~/.openclaw/workspace/research-claw-config.md

If missing, use defaults silently and mention at the end:

Want to customize? Say "更新我的研究画像"

Step 1 — Extract arXiv ID

Recognize patterns:

  • https://arxiv.org/abs/2503.XXXXX → 2503.XXXXX
  • https://arxiv.org/pdf/2503.XXXXX → 2503.XXXXX
  • https://arxiv.org/pdf/2503.XXXXX.pdf → 2503.XXXXX
  • Bare ID: 2503.XXXXX

Step 2 — Fetch Metadata

web_fetch: https://arxiv.org/abs/{ARXIV_ID}

Extract: title, authors, year, abstract, subject categories, venue if mentioned. Also fetch HTML version for figures: https://arxiv.org/html/{ARXIV_ID}v1

---## Step 3 — Analyze Paper Content

Use web_fetch on the HTML version for structured content extraction. Focus on: Abstract, Introduction, Method, Experiments (tables/numbers), Conclusion.

Extract using the Deep Note 7-section framework:

SectionWhat to extract
0) MetadataTitle, alias, authors, venue, date, links, tags, rating, scoring breakdown
1) Why-readOne-sentence: key claim + key observation
2) CRGPContext, Related work, Gap, Proposal — from Introduction
3) FiguresKey figures with URLs from arxiv HTML + one-line descriptions
4) ExperimentsMain results table, ablation highlights, limitations
5) Why it mattersResearch insights for the reader's own work (2-4 bullets)
6) Next stepsActionable follow-up items as checkboxes
7) ScoringRating breakdown explanation
Scoring System

Base score: 1 (any complete paper with benchmarks)

Quality bonus (0-2):

  • +1: Solid experiments with proper ablation
  • +2: Strong ablation + SOTA results + novel methodology

Observation bonus (0-2):

  • +1: Finding directly relevant to reader's research
  • +2: Paradigm-shifting insight for the field

Final = Base + Quality + Observation (max 5/5)

---## Step 4 — Write Markdown Deep Note File

Output directory: Same repo as reading notes (e.g., papers/ directory). Filename: YYYY-MM-DD_{alias}.md (e.g., 2026-04-01_gems.md)

Markdown Deep Note Template
markdown
# Deep Note — {ALIAS}
## 0) Metadata
-**Title:** {FULL_TITLE}
-**Alias:** {ALIAS}
-**Authors / Org:** {AUTHORS} ({INSTITUTIONS})
-**Venue / Status:** {VENUE_OR_ARXIV_ID} ({STATUS})
-**Date:** {PAPER_DATE}
-**Links:**
-Abs: https://arxiv.org/abs/{ARXIV_ID}
-HTML: https://arxiv.org/html/{ARXIV_ID}v1
-PDF: https://arxiv.org/pdf/{ARXIV_ID}
-Code: {CODE_URL_OR_PROJECT_PAGE}
-**Tags:** {comma-separated tags}
-**My rating:** {STARS} ({N}/5)
-**Read depth:** deep
-**Scoring ({BREAKDOWN}):** {EXPLANATION} = **{N}/5**
---
## 1) 一句话 Why-read
-**Key claim/contribution + key observation:** {ONE_PARAGRAPH}
---
## 2) CRGP 拆解 Introduction
### C — Context
{2-3 sentences on research background}
### R — Related work
{Bullet list grouped by methodology line}
### G — Gap
{2-3 sentences on specific limitations}
### P — Proposal
{2-3 sentences on proposed solution + key insight}
---
## 3) Figure 区
{For each key figure:}
-图N({description}):
![figN]({arxiv_html_figure_url})
{One-line interpretation}
---
## 4) Experiments — Key Numbers
### Main Results
| Benchmark | Metric | This Work | Best Baseline | Delta |
|-----------|--------|-----------|---------------|-------|
| ... | ... | ... | ... | ... |
### Ablation
{Key ablation findings with numbers}
### Limitations
{2-3 honest limitations}
---
## 5) Why it matters — 对我研究的启发
{2-4 numbered insights connecting to reader's research}
## 6) Actionable next step
-[ ] {Follow-up item 1}
-[ ] {Follow-up item 2}
-[ ] {Follow-up item 3}
## 7) 评分解释
**{N}/5({BREAKDOWN})**
{Bullet explanation for each component}
Key rules for markdown deep note:
  1. Use real numbers — never write "XX" or placeholders
  2. Figures must have URLs from arxiv HTML version when available
  3. Tables use pipe format — compatible with GitHub/Obsidian
  4. Chinese + English mixed — technical terms in English, analysis in Chinese
  5. Scoring breakdown must be explicit — show the math

---## Step 5 — Output Chat Summary

After writing the markdown file, output a brief summary in chat:

📝 Deep Note 完成 | {ALIAS}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📌 **{TITLE}**
👤 {AUTHORS} | 📅 {YEAR} | ⭐ {RATING}
🔗 https://arxiv.org/abs/{ARXIV_ID}
💡 **核心发现:** {WHY_READ one sentence}
📊 **关键数据:** {BEST_RESULT metric + number}
✨ **启发:** {TOP_INSIGHT one sentence}
💾 笔记 → {OUTPUT_PATH}

Step 6 — (Optional) Generate HTML

Only if user requests HTML (--html / 生成 HTML / 生成网页):

  1. Load template from {SKILL_DIR}/../../templates/paper-note.html
  2. Fill all {{PLACEHOLDER}} tags using extracted content
  3. Save to output directory as {ARXIV_ID}.html
  4. Report the saved path

See the root SKILL.md for the full placeholder mapping table.


Error Handling

ErrorAction
arXiv HTML unavailableUse abstract page only; note [HTML unavailable]
Very long paper (>50 pages)Focus on Abstract, Intro, Method, Results tables, Conclusion
No figures found in HTMLSkip Figure section, note [No figures extracted]
Config file missingUse defaults; suggest "更新我的研究画像"

Token Efficiency Notes

  • Markdown deep note costs ~2-3K output tokens (vs ~8-10K for HTML template filling)
  • Markdown files are git-friendly: diff, merge, grep all work naturally
  • Reading list tables can directly link to markdown notes via relative paths
  • HTML generation is now opt-in, not default — saves tokens on every paper read

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

7-day trend score
0

Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/alphalab-ustc/researchclaw/paper-reader/badge)](https://verified-skill.com/skills/alphalab-ustc/researchclaw/paper-reader)

Something wrong with this skill or its page? Tell us.