Crawler Summary

paper-summary answer-first brief

Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these keywords)**: - Compare/M对比: "compare papers", "对比论文", "多论文分析" - Citations/引用: "citation network", "引用分析", "谁引用了这篇论文" - Figures/图表: "extract figures", "提取图表", "图片提取" - Related/推荐: "related papers", "类似论文", "推荐论文" Use when user asks to: summarize papers, compare papers, analyze citations, extract figures, find related papers, do literature survey, 总结论文, 分析文献, 论文对比, 引用网络, 图表提取. --- name: paper-summary description: > Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these k Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 2/25/2026.

Freshness

Last checked 2/25/2026

Best For

paper-summary is best for general automation workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

Claim this agent
Agent DossierGitHubSafety: 89/100

paper-summary

Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these keywords)**: - Compare/M对比: "compare papers", "对比论文", "多论文分析" - Citations/引用: "citation network", "引用分析", "谁引用了这篇论文" - Figures/图表: "extract figures", "提取图表", "图片提取" - Related/推荐: "related papers", "类似论文", "推荐论文" Use when user asks to: summarize papers, compare papers, analyze citations, extract figures, find related papers, do literature survey, 总结论文, 分析文献, 论文对比, 引用网络, 图表提取. --- name: paper-summary description: > Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these k

OpenClawself-declared

Public facts

5

Change events

1

Artifacts

0

Freshness

Feb 25, 2026

Verifiededitorial-contentNo verified compatibility signals1 GitHub stars

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 2/25/2026.

1 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 25, 2026

Vendor

Magicexia

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 2/25/2026.

Setup snapshot

git clone https://github.com/magicexia/paper-summary.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Magicexia

profilemedium
Observed Feb 25, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Feb 25, 2026Source linkProvenance
Adoption (1)

Adoption signal

1 GitHub stars

profilemedium
Observed Feb 25, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

bash

python3 {skill_dir}/scripts/extract_paper.py "<input>" --metadata

bash

python3 {skill_dir}/scripts/batch_extract.py <pdf_directory> --summarize --concurrency 4

bash

python3 {skill_dir}/scripts/advanced_analysis.py citations 1706.03762

bash

python3 {skill_dir}/scripts/advanced_analysis.py compare paper1.pdf paper2.pdf paper3.pdf --output comparison.md

bash

python3 {skill_dir}/scripts/advanced_analysis.py ocr paper.pdf --output ./figures/

bash

python3 {skill_dir}/scripts/advanced_analysis.py recommend 1706.03762

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these keywords)**: - Compare/M对比: "compare papers", "对比论文", "多论文分析" - Citations/引用: "citation network", "引用分析", "谁引用了这篇论文" - Figures/图表: "extract figures", "提取图表", "图片提取" - Related/推荐: "related papers", "类似论文", "推荐论文" Use when user asks to: summarize papers, compare papers, analyze citations, extract figures, find related papers, do literature survey, 总结论文, 分析文献, 论文对比, 引用网络, 图表提取. --- name: paper-summary description: > Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries. **Basic**: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述. **Advanced (trigger these k

Full README

name: paper-summary description: > Academic paper summarization and analysis agent. Extracts text from PDF papers (local files, arXiv IDs/URLs, DOIs, Semantic Scholar URLs, PubMed IDs, or remote PDF URLs) using pymupdf4llm, then produces structured research summaries.

Basic: summarize a paper, analyze a research paper, read a PDF paper, extract key findings, 总结论文, 分析文献, 论文解读, 文献综述.

Advanced (trigger these keywords):

  • Compare/M对比: "compare papers", "对比论文", "多论文分析"
  • Citations/引用: "citation network", "引用分析", "谁引用了这篇论文"
  • Figures/图表: "extract figures", "提取图表", "图片提取"
  • Related/推荐: "related papers", "类似论文", "推荐论文"

Use when user asks to: summarize papers, compare papers, analyze citations, extract figures, find related papers, do literature survey, 总结论文, 分析文献, 论文对比, 引用网络, 图表提取.

Paper Summary

Summarize academic papers into structured research reports.

Prerequisites

  • Python 3.8+ with pymupdf4llm installed
  • Install: pip3 install pymupdf4llm

Supported Input Sources

| Source | Example | |--------|---------| | Local PDF | /path/to/paper.pdf | | arXiv ID | 2301.07041 | | arXiv URL | https://arxiv.org/abs/2301.07041 | | DOI | 10.1234/example.doi | | Semantic Scholar | https://www.semanticscholar.org/paper/... | | PubMed ID | PMID:12345678 or 12345678 | | Remote PDF URL | https://example.com/paper.pdf |

Natural Language Triggers

The skill automatically routes to the appropriate function based on your request:

📊 Compare Papers (多论文对比)

Trigger keywords: "compare", "对比", "对比分析", "比较论文" Examples:

  • "对比这几篇论文的特点"
  • "Compare paper A and paper B"
  • "分析这三个论文的差异"

Command: python3 {skill_dir}/scripts/advanced_analysis.py compare <pdf1> <pdf2> ...

🔗 Citation Network (引用网络分析)

Trigger keywords: "citation", "引用", "谁引用了", "参考文献" Examples:

  • "这篇论文被谁引用了?"
  • "分析论文的引用网络"
  • "show citations of this paper"

Command: python3 {skill_dir}/scripts/advanced_analysis.py citations <paper_id>

🖼️ Extract Figures (图表提取)

Trigger keywords: "extract figure", "提取图表", "图片", "figures", "图表" Examples:

  • "提取论文中的图表"
  • "extract all figures from this PDF"
  • "保存论文里的图片"

Command: python3 {skill_dir}/scripts/advanced_analysis.py ocr <pdf>

🎯 Related Papers (相关论文推荐)

Trigger keywords: "related", "类似", "推荐论文", "similar" Examples:

  • "推荐类似的论文"
  • "find related papers"
  • "有哪些论文和这个相关?"

Command: python3 {skill_dir}/scripts/advanced_analysis.py recommend <paper_id>

Basic Workflow

Single Paper Extraction

python3 {skill_dir}/scripts/extract_paper.py "<input>" --metadata

Batch Processing

python3 {skill_dir}/scripts/batch_extract.py <pdf_directory> --summarize --concurrency 4

Advanced Features (Manual)

Citation Network Analysis

python3 {skill_dir}/scripts/advanced_analysis.py citations 1706.03762

Multi-Paper Comparison

python3 {skill_dir}/scripts/advanced_analysis.py compare paper1.pdf paper2.pdf paper3.pdf --output comparison.md

Figure/Table Extraction

python3 {skill_dir}/scripts/advanced_analysis.py ocr paper.pdf --output ./figures/

Related Paper Recommendation

python3 {skill_dir}/scripts/advanced_analysis.py recommend 1706.03762

All Options

extract_paper.py

| Option | Description | |--------|-------------| | --metadata | Include metadata in output | | --format json | JSON output format | | --tables | Extract tables | | --timeout | Download timeout (seconds) | | --cache-dir | Cache directory for PDFs | | --verbose | Verbose logging |

batch_extract.py

| Option | Description | |--------|-------------| | --output-dir | Output directory | | --summarize | Auto-generate summaries | | --concurrency | Parallel extraction count | | --timeout | Per-paper timeout | | --template | Custom summary template |

advanced_analysis.py

| Command | Description | |---------|-------------| | compare <files> | Compare multiple PDFs | | citations <id> | Citation network analysis | | ocr <pdf> | Extract figures/tables | | recommend <id> | Find related papers |

Output Guidelines

  • Use the paper's language for the summary body; use bilingual section headers
  • Include quantitative results — actual numbers, not vague claims
  • For survey/review papers, emphasize the taxonomy and key reference landscape
  • Keep TL;DR to 1-2 sentences maximum
  • Rate the paper briefly at the end (novelty, rigor, clarity, impact)

Paths

  • Extraction script: scripts/extract_paper.py
  • Batch script: scripts/batch_extract.py
  • Advanced analysis: scripts/advanced_analysis.py
  • Summary template: references/summary-template.md
  • Default output: {workspace}/paper-summaries/

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/contract"
curl -s "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

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Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-10-09T08:46:59.809Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Magicexia",
    "href": "https://github.com/magicexia/paper-summary",
    "sourceUrl": "https://github.com/magicexia/paper-summary",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-25T02:06:36.955Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-25T02:06:36.955Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "1 GitHub stars",
    "href": "https://github.com/magicexia/paper-summary",
    "sourceUrl": "https://github.com/magicexia/paper-summary",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-25T02:06:36.955Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/magicexia-paper-summary/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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