activepieces
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Crawler 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 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
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
Public facts
5
Change events
1
Artifacts
0
Freshness
Feb 25, 2026
Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 2/25/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Feb 25, 2026
Vendor
Magicexia
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
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.gitSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Magicexia
Protocol compatibility
OpenClaw
Adoption signal
1 GitHub stars
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
typescript
Parameters
bash
python3 {skill_dir}/scripts/extract_paper.py "<input>" --metadatabash
python3 {skill_dir}/scripts/batch_extract.py <pdf_directory> --summarize --concurrency 4bash
python3 {skill_dir}/scripts/advanced_analysis.py citations 1706.03762bash
python3 {skill_dir}/scripts/advanced_analysis.py compare paper1.pdf paper2.pdf paper3.pdf --output comparison.mdbash
python3 {skill_dir}/scripts/advanced_analysis.py ocr paper.pdf --output ./figures/bash
python3 {skill_dir}/scripts/advanced_analysis.py recommend 1706.03762Full documentation captured from public sources, including the complete README when available.
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
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):
Summarize academic papers into structured research reports.
pymupdf4llm installedpip3 install pymupdf4llm| 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 |
The skill automatically routes to the appropriate function based on your request:
Trigger keywords: "compare", "对比", "对比分析", "比较论文" Examples:
Command: python3 {skill_dir}/scripts/advanced_analysis.py compare <pdf1> <pdf2> ...
Trigger keywords: "citation", "引用", "谁引用了", "参考文献" Examples:
Command: python3 {skill_dir}/scripts/advanced_analysis.py citations <paper_id>
Trigger keywords: "extract figure", "提取图表", "图片", "figures", "图表" Examples:
Command: python3 {skill_dir}/scripts/advanced_analysis.py ocr <pdf>
Trigger keywords: "related", "类似", "推荐论文", "similar" Examples:
Command: python3 {skill_dir}/scripts/advanced_analysis.py recommend <paper_id>
python3 {skill_dir}/scripts/extract_paper.py "<input>" --metadata
python3 {skill_dir}/scripts/batch_extract.py <pdf_directory> --summarize --concurrency 4
python3 {skill_dir}/scripts/advanced_analysis.py citations 1706.03762
python3 {skill_dir}/scripts/advanced_analysis.py compare paper1.pdf paper2.pdf paper3.pdf --output comparison.md
python3 {skill_dir}/scripts/advanced_analysis.py ocr paper.pdf --output ./figures/
python3 {skill_dir}/scripts/advanced_analysis.py recommend 1706.03762
| 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 |
| Option | Description |
|--------|-------------|
| --output-dir | Output directory |
| --summarize | Auto-generate summaries |
| --concurrency | Parallel extraction count |
| --timeout | Per-paper timeout |
| --template | Custom summary template |
| Command | Description |
|---------|-------------|
| compare <files> | Compare multiple PDFs |
| citations <id> | Citation network analysis |
| ocr <pdf> | Extract figures/tables |
| recommend <id> | Find related papers |
scripts/extract_paper.pyscripts/batch_extract.pyscripts/advanced_analysis.pyreferences/summary-template.md{workspace}/paper-summaries/Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
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"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
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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
}
]Sponsored
Ads related to paper-summary and adjacent AI workflows.