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paper-compare论文比较

Agent Skill

paper-compare 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paper-compare(论文比较)
来源仓库:https://github.com/kexu9/paper-compare
安装命令:
openclaw skills install paper-compare
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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openclaw skills install paper-compare

简介

并排对比多篇学术论文的异同点。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

  • 适用于通过 DOI 比较 1-5 篇论文的研究需求。
  • 识别相似性、差异与研究空白,辅助文献综述撰写。
  • 建议提前确认数据来源合法性及 API 调用权限。
  • paper-compare 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
paper-compare
description
Compare academic research papers side-by-side to identify similarities, differences, and research gaps. Use when user wants to compare 1-5 papers via DOIs, URLs, search queries, or PDF files. Supports mixed input types. Outputs both comparison table and detailed narrative summary.
version
1.2.1
changelog
Added user_goal to history, clarified step structure
metadata
{"clawdbot":{"emoji":"📄","category":"research"}}

Paper Compare

Compare academic papers side-by-side with structured tables and detailed narrative analysis.


The Paper Comparison Reasoning Framework

┌─────────────────────────────────────────────────────────────┐
│  PAPER COMPARISON THINKING                                   │
├─────────────────────────────────────────────────────────────┤
│  1. INTERPRET  → What papers? What comparison goal?        │
│  2. RETRIEVE   → Fetch metadata, abstracts, full text     │
│  3. ANALYZE    → Extract across 10 dimensions              │
│  4. SYNTHESIZE → Build narrative, find gaps, score quality │
│  5. VALIDATE   → Check completeness, deliver              │
└─────────────────────────────────────────────────────────────┘

Decision Tree: Input Processing

USER INPUT
    │
    ├── 1 paper ──→ Single Paper Summary
    │       └── Skip comparison, show full summary
    │
    ├── 2-5 papers ──→ Full Comparison
    │       └── Proceed with 10 dimensions
    │
    ├── >5 papers ──→ Ask to Narrow
    │       └── "Please narrow to 2-5 for meaningful comparison"
    │
    ├── DOI ──→ Fetch via crossref/semantic scholar
    │       └── https://api.crossref.org/works/{doi}
    │
    ├── URL ──→ Fetch via web_fetch
    │       └── Extract title, authors, abstract
    │
    ├── Search query ──→ Search first
    │       └── Use web_search, present top 3, CONFIRM before proceeding
    │
    └── PDF file ──→ Extract text first
            └── Use pdf skill, then extract metadata

Decision Tree: Comparison Angle

WHAT IS THE COMPARISON ABOUT?
    │
    ├── Same topic, different methods ──→ 
    │       └── Focus: methodology differences, results comparison
    │
    ├── Same method, different domains ──→
    │       └── Focus: adaptation, performance across domains
    │
    ├── Evolution over time ──→
    │       └── Focus: improvements, what changed, SOTA progression
    │
    ├── Competing approaches ──→
    │       └── Focus: trade-offs, when to choose which
    │
    └── Complementary papers ──→
            └── Focus: how they combine, gaps each fills

Self-Check: After Identifying Angle

  • [ ] Does my analysis focus on the right aspects?
  • [ ] Will this help the user make a decision?

Step 1: Interpret the Request

What to Clarify

QuestionWhy It Matters
Which papers?Need exact references
What goal?Learning? Research? Writing?
What comparison angle?Focus analysis appropriately

Self-Check: Before Starting

  • [ ] Do I have all paper references?
  • [ ] Do I understand what user wants to learn?
  • [ ] Is the number of papers appropriate (1-5)?
  • [ ] What's the comparison angle?

Step 2: Retrieve Papers

Retrieval Strategy

Input TypeMethodSource
DOIAPIcrossref, semantic scholar
URLweb_fetcharXiv, IEEE, PubMed
Searchweb_search → web_fetchFind, then confirm
PDFpdf skillExtract text
Historymemory_searchPrior comparisons

Quality Priority

Must have:
├── Title
├── Authors
├── Year
├── Venue
├── Abstract (for methodology + results)

Nice to have:
├── Full text (for limitations)
├── Code/data links
├── Citation count (see below)

Citation Count

Use Semantic Scholar API:

https://api.semanticscholar.org/graph/v1/paper/{doi}?fields=citationCount

Self-Check: After Retrieval

  • [ ] Did I get the abstract?
  • [ ] Can I determine the methodology?
  • [ ] Are there any papers with missing critical info?
  • [ ] Did I get citation counts?

Step 3: Analyze (10 Dimensions)

Core Dimensions (Always Include)

#DimensionWhat to Extract
1TitleFull title
2AuthorsAll authors, first author highlighted
3YearPublication year
4VenueJournal/Conference
5Research QuestionWhat problem do they solve?
6MethodologyApproach, techniques used
7DatasetWhat data did they use?
8ResultsKey findings with numbers
9LimitationsWhat do they acknowledge?
10Code & DataLinks to artifacts?

Decision: What If Missing?

Missing dimension:
    │
    ├── Abstract missing ──→ Note "Unable to analyze methodology"
    │
    ├── Results missing ──→ Note "Results not available in metadata"
    │
    ├── Limitations missing ──→ Note "Not specified" (don't infer)
    │
    └── Dataset unclear ──→ Note "Not clearly specified"

Step 4: Synthesize

Quality Scoring

Evaluate each paper:

FactorScoreNotes
Venue Quality
- Top-tier (NeurIPS, ICML, ICLR, Nature, Science)⭐⭐⭐
- Good (AAAI, IJCAI, CVPR, EMNLP, IEEE)⭐⭐
- Other
Citations
- 100+⭐⭐⭐Highly cited
- 10-100⭐⭐Well-known
- <10Recent or niche
Code Available
- Yes, official⭐⭐⭐
- Yes, community⭐⭐
- No
Data Available
- Yes⭐⭐⭐
- No

Overall Quality: Sum stars (higher = more established)

Comparison Table Structure

| Dimension | Paper A | Paper B | ... |
|-----------|---------|---------|-----|
| Title | ... | ... | ... |
| Authors | ... | ... | ... |
| Year | ... | ... | ... |
| Venue | ... | ... | ... |
| Research Question | ... | ... | ... |
| Methodology | ... | ... | ... |
| Dataset | ... | ... | ... |
| Results | ... | ... | ... |
| Limitations | ... | ... | ... |
| Code & Data | ... | ... | ... |
| Quality Score | [⭐⭐⭐] | [⭐⭐] | ... |

Narrative Synthesis Template

Structure:

## Overview
[What problem each paper addresses - high-level]
[Comparison angle: what are we comparing?]

## Methodology Comparison
[Compare techniques - are they compression-based? architecture-based?
 What's the key algorithmic difference?
 How does the comparison angle affect this?]

## Results Analysis
[Quantitative results - specific numbers, metrics
 Performance comparison - trade-offs mentioned
 Which paper wins on what?]

## Limitations
[What each paper acknowledges - be honest about gaps]
[What's NOT covered that might matter]

## Research Gaps
[What's MISSING across ALL papers]
[What's not yet explored]
[Potential future directions]

## Quality Assessment
[Paper A: ⭐⭐⭐ - Why]
[Paper B: ⭐⭐ - Why]
[Note any concerns]

Step 5: Structured Verdict

Decision Matrix

Decision Matrix

| If You Need... | Choose | Why |
|----------------|--------|-----|
| [Best performance] | Paper [X] | [Reason] |
| [Easiest to implement] | Paper [X] | [Reason] |
| [Latest method] | Paper [X] | [Reason] |
| [Most cited/reliable] | Paper [X] | [Reason] |
| [Code available] | Paper [X] | [Reason] |

Final Recommendation

## Verdict

**For [user's goal]:**

- **Best overall:** [Paper X] — [key reason]
- **Best for implementation:** [Paper Y] — [key reason]  
- **Best for research depth:** [Paper Z] — [key reason]

**My recommendation:** [Paper X] because [specific reason matching user's goal]

**If you're unsure:** Start with [Paper X] for [reason], then explore [Paper Y] if you need [different aspect].

Self-Check: Before Delivering

  • [ ] Did I answer the user's original question?
  • [ ] Did I identify the comparison angle?
  • [ ] Are all 10 dimensions covered?
  • [ ] Is quality scored?
  • [ ] Is verdict actionable?

Step 6: Validate & Deliver

For Single Paper (1 only)

Output:

## Paper Summary

**Title:** [title]
**Authors:** [authors]
**Year:** [year]
**Venue:** [venue]

### Research Question
[What problem they address]

### Methodology
[Brief description]

### Key Results
[With numbers]

### Limitations
[What they acknowledge]

### Code & Data
[Links or "Not specified"]

### Citation Count
[If available]

### Quality Score
[⭐⭐⭐]

For Comparison (2-5 papers)

Deliver:

  1. Comparison Angle — What we're comparing and why
  2. Comparison Table — All 10 dimensions + quality
  3. Narrative Summary — 6-section synthesis
  4. Quality Assessment — Scored factors
  5. Structured Verdict — Decision matrix + recommendation

Edge Cases to Note

SituationHow to Handle
Different fieldsWarn: "Comparing CS vs Biology papers"
Very different yearsNote: "2010 vs 2024 — comparison may be unfair"
PreprintNote: "Preprint — not peer-reviewed"
Conflicting resultsNote: "Paper A claims X, Paper B claims Y"

Error Handling

If Retrieval Fails

FETCH FAILS
    │
    ├── DOI not found ──→ Check DOI format, try search
    │       └── "DOI not found. Did you mean...?"
    │
    ├── URL inaccessible ──→ Try alternative source
    │       └── e.g., arXiv → semantic scholar
    │
    ├── Search returns nothing ──→ Try different keywords
    │       └── "No papers found for [query]. Try...?"
    │
    └── PDF extraction fails ──→ Note "Unable to extract"
            └── Can still use metadata if available

History (Persistence)

Save After Comparison

{
  "last_comparison": {
    "date": "2026-03-04",
    "user_goal": "[what user wanted to achieve - learning/research/writing/decision]",
    "papers": [
      {"title": "...", "doi": "10.xxxx/xxx"},
      {"title": "...", "url": "..."}
    ],
    "topic": "[what was compared]",
    "comparison_angle": "[same topic different methods / etc]",
    "verdict": "[which paper recommended]",
    "dimensions": {
      "methodology": "...",
      "key_difference": "..."
    }
  }
}

Load History

  • Read memory/paper-compare-history.json if exists
  • Use memory_search to find prior comparisons

Dependencies

SkillUse For
pdfExtract text from uploaded PDFs
web_searchFind papers by query
web_fetchGet paper content from URLs

Quick Reference

InputAction
1 DOISingle summary
2 DOIsFull comparison
arXiv URLFetch abstract
"search for X"Search → confirm → proceed
Upload PDFExtract → analyze

Summary Checklist

  • [ ] Identify comparison angle
  • [ ] Retrieve all papers (metadata + abstract)
  • [ ] Extract 10 dimensions
  • [ ] Score quality (venue, citations, code, data)
  • [ ] Build comparison table
  • [ ] Write narrative summary
  • [ ] Create structured verdict
  • [ ] Save to history

Notes

  • Always confirm before proceeding with search results
  • Keep comparisons focused: 2-5 papers max
  • Don't infer missing information — state "Not specified"
  • Save to history for future reference
  • Quality scoring helps users make informed decisions

适合场景

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用户想查找某类 Agent Skill 时

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能力 5

展示第三方安全扫描或审计结果

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