{
  "schemaVersion": "1.0",
  "item": {
    "slug": "academic-research-hub",
    "name": "Academic Research Hub",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/anisafifi/academic-research-hub",
    "canonicalUrl": "https://clawhub.ai/anisafifi/academic-research-hub",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/academic-research-hub",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=academic-research-hub",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "SKILL.md",
      "references/readme.md",
      "scripts/requirements.txt",
      "scripts/research.py"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-04-23T16:43:11.935Z",
      "expiresAt": "2026-04-30T16:43:11.935Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard",
        "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/academic-research-hub"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/academic-research-hub",
    "agentPageUrl": "https://openagent3.xyz/skills/academic-research-hub/agent",
    "manifestUrl": "https://openagent3.xyz/skills/academic-research-hub/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/academic-research-hub/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Academic Research Hub",
        "body": "Search and retrieve academic papers from multiple sources including arXiv, PubMed, Semantic Scholar, and more. Download PDFs, extract citations, generate bibliographies, and build literature reviews.\n\n⚠️ Prerequisite: Install OpenClawCLI (Windows, MacOS)\n\nInstallation Best Practices:\n\n# Standard installation\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n# If you encounter permission errors, use a virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\nNever use --break-system-packages as it can damage your system's Python installation."
      },
      {
        "title": "Quick Reference",
        "body": "TaskCommandSearch arXivpython scripts/research.py arxiv \"quantum computing\"Search PubMedpython scripts/research.py pubmed \"covid vaccine\"Search Semantic Scholarpython scripts/research.py semantic \"machine learning\"Download paperspython scripts/research.py arxiv \"topic\" --downloadGet citationspython scripts/research.py arxiv \"topic\" --citationsGenerate bibliographypython scripts/research.py arxiv \"topic\" --format bibtexSave resultspython scripts/research.py arxiv \"topic\" --output results.json"
      },
      {
        "title": "1. Multi-Source Search",
        "body": "Search across multiple academic databases from a single interface.\n\nSupported Sources:\n\narXiv - Physics, mathematics, computer science, quantitative biology, quantitative finance, statistics\nPubMed - Biomedical and life sciences literature\nSemantic Scholar - Computer science and interdisciplinary research\nGoogle Scholar - Broad academic search (limited, no API)"
      },
      {
        "title": "2. Paper Download",
        "body": "Download full-text PDFs when available.\n\npython scripts/research.py arxiv \"deep learning\" --download --output-dir papers/"
      },
      {
        "title": "3. Citation Extraction",
        "body": "Extract and format citations from papers.\n\nSupported formats:\n\nBibTeX\nRIS\nJSON\nPlain text"
      },
      {
        "title": "4. Metadata Retrieval",
        "body": "Get comprehensive metadata for each paper:\n\nTitle, authors, abstract\nPublication date\nJournal/conference\nDOI, arXiv ID, PubMed ID\nCitation count\nReferences"
      },
      {
        "title": "arXiv Search",
        "body": "Search the arXiv repository for preprints.\n\n# Basic search\npython scripts/research.py arxiv \"quantum computing\"\n\n# Filter by category\npython scripts/research.py arxiv \"neural networks\" --category cs.LG\n\n# Filter by date\npython scripts/research.py arxiv \"transformers\" --year 2023\n\n# Download papers\npython scripts/research.py arxiv \"attention mechanism\" --download --max-results 10\n\nAvailable categories:\n\ncs.AI - Artificial Intelligence\ncs.LG - Machine Learning\ncs.CV - Computer Vision\ncs.CL - Computation and Language\nmath.CO - Combinatorics\nphysics.optics - Optics\nq-bio.GN - Genomics\nFull list\n\nOutput:\n\n1. Attention Is All You Need\n   Authors: Vaswani et al.\n   Published: 2017-06-12\n   arXiv ID: 1706.03762\n   Categories: cs.CL, cs.LG\n   Abstract: The dominant sequence transduction models...\n   PDF: http://arxiv.org/pdf/1706.03762v5"
      },
      {
        "title": "PubMed Search",
        "body": "Search biomedical literature indexed in PubMed.\n\n# Basic search\npython scripts/research.py pubmed \"cancer immunotherapy\"\n\n# Filter by date range\npython scripts/research.py pubmed \"CRISPR\" --start-date 2023-01-01 --end-date 2023-12-31\n\n# Filter by publication type\npython scripts/research.py pubmed \"covid vaccine\" --publication-type \"Clinical Trial\"\n\n# Get full text links\npython scripts/research.py pubmed \"gene therapy\" --full-text\n\nPublication types:\n\nClinical Trial\nMeta-Analysis\nReview\nSystematic Review\nRandomized Controlled Trial\n\nOutput:\n\n1. mRNA vaccine effectiveness against COVID-19\n   Authors: Smith J, Jones K, et al.\n   Journal: New England Journal of Medicine\n   Published: 2023-03-15\n   PMID: 36913851\n   DOI: 10.1056/NEJMoa2301234\n   Abstract: Background: mRNA vaccines have shown...\n   Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876543/"
      },
      {
        "title": "Semantic Scholar Search",
        "body": "Search computer science and interdisciplinary research.\n\n# Basic search\npython scripts/research.py semantic \"reinforcement learning\"\n\n# Filter by year\npython scripts/research.py semantic \"graph neural networks\" --year 2022\n\n# Get highly cited papers\npython scripts/research.py semantic \"transformers\" --min-citations 100\n\n# Include references\npython scripts/research.py semantic \"BERT\" --include-references\n\nOutput includes:\n\nCitation count\nInfluential citation count\nReference list\nCiting papers\nFields of study\n\nOutput:\n\n1. BERT: Pre-training of Deep Bidirectional Transformers\n   Authors: Devlin J, Chang MW, Lee K, Toutanova K\n   Published: 2019\n   Paper ID: df2b0e26d0599ce3e70df8a9da02e51594e0e992\n   Citations: 15000+\n   Influential Citations: 2000+\n   Fields: Computer Science, Linguistics\n   Abstract: We introduce a new language representation model...\n   PDF: https://arxiv.org/pdf/1810.04805.pdf"
      },
      {
        "title": "Result Limits",
        "body": "Control the number of results returned.\n\n--max-results N    # Default: 10, range: 1-100\n\nExamples:\n\npython scripts/research.py arxiv \"machine learning\" --max-results 5\npython scripts/research.py pubmed \"diabetes\" --max-results 50"
      },
      {
        "title": "Output Formats",
        "body": "Choose how results are formatted.\n\n--format <text|json|bibtex|ris|markdown>\n\nText - Human-readable format (default)\n\npython scripts/research.py arxiv \"quantum\" --format text\n\nJSON - Structured data for processing\n\npython scripts/research.py arxiv \"quantum\" --format json\n\nBibTeX - For LaTeX documents\n\npython scripts/research.py arxiv \"quantum\" --format bibtex\n\nRIS - For reference managers (Zotero, Mendeley)\n\npython scripts/research.py arxiv \"quantum\" --format ris\n\nMarkdown - For documentation\n\npython scripts/research.py arxiv \"quantum\" --format markdown"
      },
      {
        "title": "Save to File",
        "body": "Save results to a file.\n\n--output <filepath>\n\nExamples:\n\npython scripts/research.py arxiv \"AI\" --output results.txt\npython scripts/research.py pubmed \"cancer\" --format json --output papers.json\npython scripts/research.py semantic \"NLP\" --format bibtex --output references.bib"
      },
      {
        "title": "Download Papers",
        "body": "Download full-text PDFs when available.\n\n--download\n--output-dir <directory>    # Where to save PDFs (default: downloads/)\n\nExamples:\n\n# Download to default directory\npython scripts/research.py arxiv \"deep learning\" --download --max-results 5\n\n# Download to specific directory\npython scripts/research.py arxiv \"transformers\" --download --output-dir papers/nlp/"
      },
      {
        "title": "Citation Extraction",
        "body": "Extract citations from papers.\n\n--citations              # Extract citations\n--citation-format <format>    # bibtex, ris, json (default: bibtex)\n\nExample:\n\npython scripts/research.py arxiv \"attention mechanism\" --citations --citation-format bibtex --output citations.bib"
      },
      {
        "title": "Date Filtering",
        "body": "Filter by publication date.\n\narXiv:\n\n--year <YYYY>           # Specific year\n--start-date <YYYY-MM-DD>\n--end-date <YYYY-MM-DD>\n\nPubMed:\n\n--start-date <YYYY-MM-DD>\n--end-date <YYYY-MM-DD>\n\nExamples:\n\npython scripts/research.py arxiv \"quantum\" --year 2023\npython scripts/research.py pubmed \"vaccine\" --start-date 2022-01-01 --end-date 2023-12-31"
      },
      {
        "title": "Author Search",
        "body": "Search for papers by specific authors.\n\n--author \"Last, First\"\n\nExamples:\n\npython scripts/research.py arxiv \"neural networks\" --author \"Hinton, Geoffrey\"\npython scripts/research.py semantic \"deep learning\" --author \"Bengio, Yoshua\""
      },
      {
        "title": "Sort Options",
        "body": "Sort results by different criteria.\n\n--sort-by <relevance|date|citations>\n\nExamples:\n\npython scripts/research.py arxiv \"machine learning\" --sort-by date\npython scripts/research.py semantic \"NLP\" --sort-by citations"
      },
      {
        "title": "Literature Review",
        "body": "Gather papers on a topic for a literature review.\n\n# Step 1: Search multiple sources\npython scripts/research.py arxiv \"graph neural networks\" --max-results 20 --format json --output arxiv_gnn.json\npython scripts/research.py semantic \"graph neural networks\" --max-results 20 --format json --output semantic_gnn.json\n\n# Step 2: Download key papers\npython scripts/research.py arxiv \"graph neural networks\" --download --max-results 10 --output-dir papers/gnn/\n\n# Step 3: Generate bibliography\npython scripts/research.py arxiv \"graph neural networks\" --max-results 20 --format bibtex --output gnn_references.bib"
      },
      {
        "title": "Finding Recent Research",
        "body": "Track the latest papers in a field.\n\n# Last year's papers\npython scripts/research.py arxiv \"large language models\" --year 2023 --sort-by date --max-results 30\n\n# Last month's biomedical papers\npython scripts/research.py pubmed \"gene therapy\" --start-date 2023-11-01 --end-date 2023-11-30 --format markdown --output recent_gene_therapy.md"
      },
      {
        "title": "Highly Cited Papers",
        "body": "Find influential papers in a field.\n\npython scripts/research.py semantic \"reinforcement learning\" --min-citations 500 --sort-by citations --max-results 25"
      },
      {
        "title": "Author Publication History",
        "body": "Track an author's work.\n\npython scripts/research.py arxiv \"deep learning\" --author \"LeCun, Yann\" --sort-by date --max-results 50 --output lecun_papers.json"
      },
      {
        "title": "Building a Reference Library",
        "body": "Create a comprehensive reference collection.\n\n# Create directory structure\nmkdir -p references/{papers,citations}\n\n# Search and download papers\npython scripts/research.py arxiv \"transformers NLP\" --download --max-results 15 --output-dir references/papers/\n\n# Generate citations\npython scripts/research.py arxiv \"transformers NLP\" --max-results 15 --format bibtex --output references/citations/transformers.bib"
      },
      {
        "title": "Cross-Source Validation",
        "body": "Verify findings across multiple databases.\n\n# Search same topic across sources\npython scripts/research.py arxiv \"federated learning\" --max-results 10 --output arxiv_fl.txt\npython scripts/research.py semantic \"federated learning\" --max-results 10 --output semantic_fl.txt\npython scripts/research.py pubmed \"federated learning\" --max-results 10 --output pubmed_fl.txt\n\n# Compare results\ndiff arxiv_fl.txt semantic_fl.txt"
      },
      {
        "title": "Text Format (Default)",
        "body": "Search Results: 3 papers found\n\n1. Attention Is All You Need\n   Authors: Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; et al.\n   Published: 2017-06-12\n   arXiv ID: 1706.03762\n   Categories: cs.CL, cs.LG\n   Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...\n   PDF: http://arxiv.org/pdf/1706.03762v5\n\n2. BERT: Pre-training of Deep Bidirectional Transformers\n   Authors: Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina\n   Published: 2018-10-11\n   arXiv ID: 1810.04805\n   Categories: cs.CL\n   Abstract: We introduce a new language representation model called BERT...\n   PDF: http://arxiv.org/pdf/1810.04805v2"
      },
      {
        "title": "JSON Format",
        "body": "[\n  {\n    \"title\": \"Attention Is All You Need\",\n    \"authors\": [\"Vaswani, Ashish\", \"Shazeer, Noam\", \"Parmar, Niki\"],\n    \"published\": \"2017-06-12\",\n    \"arxiv_id\": \"1706.03762\",\n    \"categories\": [\"cs.CL\", \"cs.LG\"],\n    \"abstract\": \"The dominant sequence transduction models...\",\n    \"pdf_url\": \"http://arxiv.org/pdf/1706.03762v5\",\n    \"doi\": \"10.48550/arXiv.1706.03762\"\n  }\n]"
      },
      {
        "title": "BibTeX Format",
        "body": "@article{vaswani2017attention,\n  title={Attention Is All You Need},\n  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\\L}ukasz and Polosukhin, Illia},\n  journal={arXiv preprint arXiv:1706.03762},\n  year={2017},\n  url={http://arxiv.org/abs/1706.03762}\n}"
      },
      {
        "title": "RIS Format",
        "body": "TY  - JOUR\nTI  - Attention Is All You Need\nAU  - Vaswani, Ashish\nAU  - Shazeer, Noam\nAU  - Parmar, Niki\nPY  - 2017\nDA  - 2017/06/12\nJO  - arXiv preprint\nVL  - arXiv:1706.03762\nUR  - http://arxiv.org/abs/1706.03762\nER  -"
      },
      {
        "title": "Markdown Format",
        "body": "# Search Results: 3 papers found\n\n## 1. Attention Is All You Need\n\n**Authors:** Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; et al.\n\n**Published:** 2017-06-12\n\n**arXiv ID:** 1706.03762\n\n**Categories:** cs.CL, cs.LG\n\n**Abstract:** The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...\n\n**PDF:** [Download](http://arxiv.org/pdf/1706.03762v5)"
      },
      {
        "title": "Search Strategy",
        "body": "Start broad - Use general terms to get an overview\nRefine iteratively - Add filters based on initial results\nUse multiple sources - Cross-reference findings\nCheck recent papers - Use date filters for current research"
      },
      {
        "title": "Result Management",
        "body": "Save searches - Use --output to preserve results\nOrganize downloads - Create logical directory structures\nExport citations early - Generate BibTeX as you search\nTrack sources - Note which database returned which papers"
      },
      {
        "title": "Download Guidelines",
        "body": "Respect rate limits - Don't download hundreds of papers at once\nCheck licensing - Verify you have rights to use papers\nOrganize by topic - Use clear directory names\nKeep metadata - Save JSON alongside PDFs"
      },
      {
        "title": "Citation Practices",
        "body": "Verify citations - Check DOIs and URLs\nUse standard formats - BibTeX for LaTeX, RIS for reference managers\nInclude abstracts - Helpful for later review\nUpdate regularly - Re-run searches for new papers"
      },
      {
        "title": "Installation Issues",
        "body": "\"Missing required dependency\"\n\n# Install all dependencies\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n# Or use virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n\"OpenClawCLI not found\"\n\nDownload from https://clawhub.ai/\nInstall for your OS (Windows/MacOS)"
      },
      {
        "title": "Search Issues",
        "body": "\"No results found\"\n\nTry broader search terms\nCheck spelling and terminology\nRemove restrictive filters\nTry a different database\n\n\"Rate limit exceeded\"\n\nWait a few minutes before retrying\nReduce --max-results value\nSpace out requests\n\n\"Download failed\"\n\nCheck internet connection\nSome papers may not have PDFs available\nVerify you have permissions to access\nTry downloading individually"
      },
      {
        "title": "API Issues",
        "body": "\"API timeout\"\n\nThe service may be temporarily unavailable\nRetry after a moment\nCheck status at respective service websites\n\n\"Invalid API response\"\n\nCheck if the service is down\nVerify your query syntax\nTry simpler queries"
      },
      {
        "title": "Access Restrictions",
        "body": "Not all papers have downloadable PDFs\nSome content requires institutional access\nPaywalled journals may only show abstracts\nGoogle Scholar has strict rate limits"
      },
      {
        "title": "Data Completeness",
        "body": "Citation counts may be outdated\nNot all metadata fields available for every paper\nSome older papers may have incomplete records\nPreprints may not have final publication info"
      },
      {
        "title": "Search Capabilities",
        "body": "Boolean operators vary by source\nNo unified query syntax across databases\nSome databases don't support all filters\nResults may differ from web interface searches"
      },
      {
        "title": "Legal Considerations",
        "body": "Respect copyright and licensing\nDon't redistribute downloaded papers\nFollow institutional access policies\nCheck terms of service for each database"
      },
      {
        "title": "Command Reference",
        "body": "python scripts/research.py <source> \"<query>\" [OPTIONS]\n\nSOURCES:\n  arxiv              Search arXiv repository\n  pubmed             Search PubMed database\n  semantic           Search Semantic Scholar\n\nREQUIRED:\n  query              Search query string (in quotes)\n\nGENERAL OPTIONS:\n  -n, --max-results  Maximum results (default: 10, max: 100)\n  -f, --format       Output format (text|json|bibtex|ris|markdown)\n  -o, --output       Save to file path\n  --sort-by          Sort by (relevance|date|citations)\n\nFILTERING:\n  --year             Filter by specific year (YYYY)\n  --start-date       Start date (YYYY-MM-DD)\n  --end-date         End date (YYYY-MM-DD)\n  --author           Author name\n  --min-citations    Minimum citation count\n\nARXIV-SPECIFIC:\n  --category         arXiv category (e.g., cs.AI, cs.LG)\n\nPUBMED-SPECIFIC:\n  --publication-type Publication type filter\n  --full-text        Include full text links\n\nSEMANTIC-SPECIFIC:\n  --include-references   Include paper references\n\nDOWNLOAD:\n  --download         Download paper PDFs\n  --output-dir       Download directory (default: downloads/)\n\nCITATIONS:\n  --citations        Extract citations\n  --citation-format  Citation format (bibtex|ris|json)\n\nHELP:\n  --help             Show all options"
      },
      {
        "title": "Quick Search",
        "body": "# Find recent papers\npython scripts/research.py arxiv \"quantum computing\"\n\n# Search biomedical literature\npython scripts/research.py pubmed \"alzheimer disease\""
      },
      {
        "title": "Comprehensive Research",
        "body": "# Search multiple sources\npython scripts/research.py arxiv \"neural networks\" --max-results 30 --output arxiv.json\npython scripts/research.py semantic \"neural networks\" --max-results 30 --output semantic.json\n\n# Download important papers\npython scripts/research.py arxiv \"neural networks\" --download --max-results 10"
      },
      {
        "title": "Citation Management",
        "body": "# Generate BibTeX\npython scripts/research.py arxiv \"deep learning\" --format bibtex --output dl_refs.bib\n\n# Export to reference manager\npython scripts/research.py pubmed \"gene editing\" --format ris --output genes.ris"
      },
      {
        "title": "Tracking New Research",
        "body": "# This month's papers\npython scripts/research.py arxiv \"LLM\" --start-date 2024-01-01 --sort-by date\n\n# Recent highly-cited work\npython scripts/research.py semantic \"transformers\" --year 2023 --min-citations 50"
      },
      {
        "title": "Support",
        "body": "For issues or questions:\n\nCheck this documentation\nRun python scripts/research.py --help\nVerify dependencies are installed\nCheck database-specific documentation\n\nResources:\n\nOpenClawCLI: https://clawhub.ai/\narXiv API: https://arxiv.org/help/api\nPubMed API: https://www.ncbi.nlm.nih.gov/books/NBK25501/\nSemantic Scholar API: https://api.semanticscholar.org/"
      }
    ],
    "body": "Academic Research Hub\n\nSearch and retrieve academic papers from multiple sources including arXiv, PubMed, Semantic Scholar, and more. Download PDFs, extract citations, generate bibliographies, and build literature reviews.\n\n⚠️ Prerequisite: Install OpenClawCLI (Windows, MacOS)\n\nInstallation Best Practices:\n\n# Standard installation\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n# If you encounter permission errors, use a virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n\nNever use --break-system-packages as it can damage your system's Python installation.\n\nQuick Reference\nTask\tCommand\nSearch arXiv\tpython scripts/research.py arxiv \"quantum computing\"\nSearch PubMed\tpython scripts/research.py pubmed \"covid vaccine\"\nSearch Semantic Scholar\tpython scripts/research.py semantic \"machine learning\"\nDownload papers\tpython scripts/research.py arxiv \"topic\" --download\nGet citations\tpython scripts/research.py arxiv \"topic\" --citations\nGenerate bibliography\tpython scripts/research.py arxiv \"topic\" --format bibtex\nSave results\tpython scripts/research.py arxiv \"topic\" --output results.json\nCore Features\n1. Multi-Source Search\n\nSearch across multiple academic databases from a single interface.\n\nSupported Sources:\n\narXiv - Physics, mathematics, computer science, quantitative biology, quantitative finance, statistics\nPubMed - Biomedical and life sciences literature\nSemantic Scholar - Computer science and interdisciplinary research\nGoogle Scholar - Broad academic search (limited, no API)\n2. Paper Download\n\nDownload full-text PDFs when available.\n\npython scripts/research.py arxiv \"deep learning\" --download --output-dir papers/\n\n3. Citation Extraction\n\nExtract and format citations from papers.\n\nSupported formats:\n\nBibTeX\nRIS\nJSON\nPlain text\n4. Metadata Retrieval\n\nGet comprehensive metadata for each paper:\n\nTitle, authors, abstract\nPublication date\nJournal/conference\nDOI, arXiv ID, PubMed ID\nCitation count\nReferences\nSource-Specific Commands\narXiv Search\n\nSearch the arXiv repository for preprints.\n\n# Basic search\npython scripts/research.py arxiv \"quantum computing\"\n\n# Filter by category\npython scripts/research.py arxiv \"neural networks\" --category cs.LG\n\n# Filter by date\npython scripts/research.py arxiv \"transformers\" --year 2023\n\n# Download papers\npython scripts/research.py arxiv \"attention mechanism\" --download --max-results 10\n\n\nAvailable categories:\n\ncs.AI - Artificial Intelligence\ncs.LG - Machine Learning\ncs.CV - Computer Vision\ncs.CL - Computation and Language\nmath.CO - Combinatorics\nphysics.optics - Optics\nq-bio.GN - Genomics\nFull list\n\nOutput:\n\n1. Attention Is All You Need\n   Authors: Vaswani et al.\n   Published: 2017-06-12\n   arXiv ID: 1706.03762\n   Categories: cs.CL, cs.LG\n   Abstract: The dominant sequence transduction models...\n   PDF: http://arxiv.org/pdf/1706.03762v5\n\nPubMed Search\n\nSearch biomedical literature indexed in PubMed.\n\n# Basic search\npython scripts/research.py pubmed \"cancer immunotherapy\"\n\n# Filter by date range\npython scripts/research.py pubmed \"CRISPR\" --start-date 2023-01-01 --end-date 2023-12-31\n\n# Filter by publication type\npython scripts/research.py pubmed \"covid vaccine\" --publication-type \"Clinical Trial\"\n\n# Get full text links\npython scripts/research.py pubmed \"gene therapy\" --full-text\n\n\nPublication types:\n\nClinical Trial\nMeta-Analysis\nReview\nSystematic Review\nRandomized Controlled Trial\n\nOutput:\n\n1. mRNA vaccine effectiveness against COVID-19\n   Authors: Smith J, Jones K, et al.\n   Journal: New England Journal of Medicine\n   Published: 2023-03-15\n   PMID: 36913851\n   DOI: 10.1056/NEJMoa2301234\n   Abstract: Background: mRNA vaccines have shown...\n   Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876543/\n\nSemantic Scholar Search\n\nSearch computer science and interdisciplinary research.\n\n# Basic search\npython scripts/research.py semantic \"reinforcement learning\"\n\n# Filter by year\npython scripts/research.py semantic \"graph neural networks\" --year 2022\n\n# Get highly cited papers\npython scripts/research.py semantic \"transformers\" --min-citations 100\n\n# Include references\npython scripts/research.py semantic \"BERT\" --include-references\n\n\nOutput includes:\n\nCitation count\nInfluential citation count\nReference list\nCiting papers\nFields of study\n\nOutput:\n\n1. BERT: Pre-training of Deep Bidirectional Transformers\n   Authors: Devlin J, Chang MW, Lee K, Toutanova K\n   Published: 2019\n   Paper ID: df2b0e26d0599ce3e70df8a9da02e51594e0e992\n   Citations: 15000+\n   Influential Citations: 2000+\n   Fields: Computer Science, Linguistics\n   Abstract: We introduce a new language representation model...\n   PDF: https://arxiv.org/pdf/1810.04805.pdf\n\nEssential Options\nResult Limits\n\nControl the number of results returned.\n\n--max-results N    # Default: 10, range: 1-100\n\n\nExamples:\n\npython scripts/research.py arxiv \"machine learning\" --max-results 5\npython scripts/research.py pubmed \"diabetes\" --max-results 50\n\nOutput Formats\n\nChoose how results are formatted.\n\n--format <text|json|bibtex|ris|markdown>\n\n\nText - Human-readable format (default)\n\npython scripts/research.py arxiv \"quantum\" --format text\n\n\nJSON - Structured data for processing\n\npython scripts/research.py arxiv \"quantum\" --format json\n\n\nBibTeX - For LaTeX documents\n\npython scripts/research.py arxiv \"quantum\" --format bibtex\n\n\nRIS - For reference managers (Zotero, Mendeley)\n\npython scripts/research.py arxiv \"quantum\" --format ris\n\n\nMarkdown - For documentation\n\npython scripts/research.py arxiv \"quantum\" --format markdown\n\nSave to File\n\nSave results to a file.\n\n--output <filepath>\n\n\nExamples:\n\npython scripts/research.py arxiv \"AI\" --output results.txt\npython scripts/research.py pubmed \"cancer\" --format json --output papers.json\npython scripts/research.py semantic \"NLP\" --format bibtex --output references.bib\n\nDownload Papers\n\nDownload full-text PDFs when available.\n\n--download\n--output-dir <directory>    # Where to save PDFs (default: downloads/)\n\n\nExamples:\n\n# Download to default directory\npython scripts/research.py arxiv \"deep learning\" --download --max-results 5\n\n# Download to specific directory\npython scripts/research.py arxiv \"transformers\" --download --output-dir papers/nlp/\n\nAdvanced Features\nCitation Extraction\n\nExtract citations from papers.\n\n--citations              # Extract citations\n--citation-format <format>    # bibtex, ris, json (default: bibtex)\n\n\nExample:\n\npython scripts/research.py arxiv \"attention mechanism\" --citations --citation-format bibtex --output citations.bib\n\nDate Filtering\n\nFilter by publication date.\n\narXiv:\n\n--year <YYYY>           # Specific year\n--start-date <YYYY-MM-DD>\n--end-date <YYYY-MM-DD>\n\n\nPubMed:\n\n--start-date <YYYY-MM-DD>\n--end-date <YYYY-MM-DD>\n\n\nExamples:\n\npython scripts/research.py arxiv \"quantum\" --year 2023\npython scripts/research.py pubmed \"vaccine\" --start-date 2022-01-01 --end-date 2023-12-31\n\nAuthor Search\n\nSearch for papers by specific authors.\n\n--author \"Last, First\"\n\n\nExamples:\n\npython scripts/research.py arxiv \"neural networks\" --author \"Hinton, Geoffrey\"\npython scripts/research.py semantic \"deep learning\" --author \"Bengio, Yoshua\"\n\nSort Options\n\nSort results by different criteria.\n\n--sort-by <relevance|date|citations>\n\n\nExamples:\n\npython scripts/research.py arxiv \"machine learning\" --sort-by date\npython scripts/research.py semantic \"NLP\" --sort-by citations\n\nCommon Workflows\nLiterature Review\n\nGather papers on a topic for a literature review.\n\n# Step 1: Search multiple sources\npython scripts/research.py arxiv \"graph neural networks\" --max-results 20 --format json --output arxiv_gnn.json\npython scripts/research.py semantic \"graph neural networks\" --max-results 20 --format json --output semantic_gnn.json\n\n# Step 2: Download key papers\npython scripts/research.py arxiv \"graph neural networks\" --download --max-results 10 --output-dir papers/gnn/\n\n# Step 3: Generate bibliography\npython scripts/research.py arxiv \"graph neural networks\" --max-results 20 --format bibtex --output gnn_references.bib\n\nFinding Recent Research\n\nTrack the latest papers in a field.\n\n# Last year's papers\npython scripts/research.py arxiv \"large language models\" --year 2023 --sort-by date --max-results 30\n\n# Last month's biomedical papers\npython scripts/research.py pubmed \"gene therapy\" --start-date 2023-11-01 --end-date 2023-11-30 --format markdown --output recent_gene_therapy.md\n\nHighly Cited Papers\n\nFind influential papers in a field.\n\npython scripts/research.py semantic \"reinforcement learning\" --min-citations 500 --sort-by citations --max-results 25\n\nAuthor Publication History\n\nTrack an author's work.\n\npython scripts/research.py arxiv \"deep learning\" --author \"LeCun, Yann\" --sort-by date --max-results 50 --output lecun_papers.json\n\nBuilding a Reference Library\n\nCreate a comprehensive reference collection.\n\n# Create directory structure\nmkdir -p references/{papers,citations}\n\n# Search and download papers\npython scripts/research.py arxiv \"transformers NLP\" --download --max-results 15 --output-dir references/papers/\n\n# Generate citations\npython scripts/research.py arxiv \"transformers NLP\" --max-results 15 --format bibtex --output references/citations/transformers.bib\n\nCross-Source Validation\n\nVerify findings across multiple databases.\n\n# Search same topic across sources\npython scripts/research.py arxiv \"federated learning\" --max-results 10 --output arxiv_fl.txt\npython scripts/research.py semantic \"federated learning\" --max-results 10 --output semantic_fl.txt\npython scripts/research.py pubmed \"federated learning\" --max-results 10 --output pubmed_fl.txt\n\n# Compare results\ndiff arxiv_fl.txt semantic_fl.txt\n\nOutput Format Examples\nText Format (Default)\nSearch Results: 3 papers found\n\n1. Attention Is All You Need\n   Authors: Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; et al.\n   Published: 2017-06-12\n   arXiv ID: 1706.03762\n   Categories: cs.CL, cs.LG\n   Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...\n   PDF: http://arxiv.org/pdf/1706.03762v5\n\n2. BERT: Pre-training of Deep Bidirectional Transformers\n   Authors: Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina\n   Published: 2018-10-11\n   arXiv ID: 1810.04805\n   Categories: cs.CL\n   Abstract: We introduce a new language representation model called BERT...\n   PDF: http://arxiv.org/pdf/1810.04805v2\n\nJSON Format\n[\n  {\n    \"title\": \"Attention Is All You Need\",\n    \"authors\": [\"Vaswani, Ashish\", \"Shazeer, Noam\", \"Parmar, Niki\"],\n    \"published\": \"2017-06-12\",\n    \"arxiv_id\": \"1706.03762\",\n    \"categories\": [\"cs.CL\", \"cs.LG\"],\n    \"abstract\": \"The dominant sequence transduction models...\",\n    \"pdf_url\": \"http://arxiv.org/pdf/1706.03762v5\",\n    \"doi\": \"10.48550/arXiv.1706.03762\"\n  }\n]\n\nBibTeX Format\n@article{vaswani2017attention,\n  title={Attention Is All You Need},\n  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\\L}ukasz and Polosukhin, Illia},\n  journal={arXiv preprint arXiv:1706.03762},\n  year={2017},\n  url={http://arxiv.org/abs/1706.03762}\n}\n\nRIS Format\nTY  - JOUR\nTI  - Attention Is All You Need\nAU  - Vaswani, Ashish\nAU  - Shazeer, Noam\nAU  - Parmar, Niki\nPY  - 2017\nDA  - 2017/06/12\nJO  - arXiv preprint\nVL  - arXiv:1706.03762\nUR  - http://arxiv.org/abs/1706.03762\nER  -\n\nMarkdown Format\n# Search Results: 3 papers found\n\n## 1. Attention Is All You Need\n\n**Authors:** Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; et al.\n\n**Published:** 2017-06-12\n\n**arXiv ID:** 1706.03762\n\n**Categories:** cs.CL, cs.LG\n\n**Abstract:** The dominant sequence transduction models are based on complex recurrent or convolutional neural networks...\n\n**PDF:** [Download](http://arxiv.org/pdf/1706.03762v5)\n\nBest Practices\nSearch Strategy\nStart broad - Use general terms to get an overview\nRefine iteratively - Add filters based on initial results\nUse multiple sources - Cross-reference findings\nCheck recent papers - Use date filters for current research\nResult Management\nSave searches - Use --output to preserve results\nOrganize downloads - Create logical directory structures\nExport citations early - Generate BibTeX as you search\nTrack sources - Note which database returned which papers\nDownload Guidelines\nRespect rate limits - Don't download hundreds of papers at once\nCheck licensing - Verify you have rights to use papers\nOrganize by topic - Use clear directory names\nKeep metadata - Save JSON alongside PDFs\nCitation Practices\nVerify citations - Check DOIs and URLs\nUse standard formats - BibTeX for LaTeX, RIS for reference managers\nInclude abstracts - Helpful for later review\nUpdate regularly - Re-run searches for new papers\nTroubleshooting\nInstallation Issues\n\n\"Missing required dependency\"\n\n# Install all dependencies\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n# Or use virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\npip install arxiv scholarly pubmed-parser semanticscholar requests\n\n\n\"OpenClawCLI not found\"\n\nDownload from https://clawhub.ai/\nInstall for your OS (Windows/MacOS)\nSearch Issues\n\n\"No results found\"\n\nTry broader search terms\nCheck spelling and terminology\nRemove restrictive filters\nTry a different database\n\n\"Rate limit exceeded\"\n\nWait a few minutes before retrying\nReduce --max-results value\nSpace out requests\n\n\"Download failed\"\n\nCheck internet connection\nSome papers may not have PDFs available\nVerify you have permissions to access\nTry downloading individually\nAPI Issues\n\n\"API timeout\"\n\nThe service may be temporarily unavailable\nRetry after a moment\nCheck status at respective service websites\n\n\"Invalid API response\"\n\nCheck if the service is down\nVerify your query syntax\nTry simpler queries\nLimitations\nAccess Restrictions\nNot all papers have downloadable PDFs\nSome content requires institutional access\nPaywalled journals may only show abstracts\nGoogle Scholar has strict rate limits\nData Completeness\nCitation counts may be outdated\nNot all metadata fields available for every paper\nSome older papers may have incomplete records\nPreprints may not have final publication info\nSearch Capabilities\nBoolean operators vary by source\nNo unified query syntax across databases\nSome databases don't support all filters\nResults may differ from web interface searches\nLegal Considerations\nRespect copyright and licensing\nDon't redistribute downloaded papers\nFollow institutional access policies\nCheck terms of service for each database\nCommand Reference\npython scripts/research.py <source> \"<query>\" [OPTIONS]\n\nSOURCES:\n  arxiv              Search arXiv repository\n  pubmed             Search PubMed database\n  semantic           Search Semantic Scholar\n\nREQUIRED:\n  query              Search query string (in quotes)\n\nGENERAL OPTIONS:\n  -n, --max-results  Maximum results (default: 10, max: 100)\n  -f, --format       Output format (text|json|bibtex|ris|markdown)\n  -o, --output       Save to file path\n  --sort-by          Sort by (relevance|date|citations)\n\nFILTERING:\n  --year             Filter by specific year (YYYY)\n  --start-date       Start date (YYYY-MM-DD)\n  --end-date         End date (YYYY-MM-DD)\n  --author           Author name\n  --min-citations    Minimum citation count\n\nARXIV-SPECIFIC:\n  --category         arXiv category (e.g., cs.AI, cs.LG)\n\nPUBMED-SPECIFIC:\n  --publication-type Publication type filter\n  --full-text        Include full text links\n\nSEMANTIC-SPECIFIC:\n  --include-references   Include paper references\n\nDOWNLOAD:\n  --download         Download paper PDFs\n  --output-dir       Download directory (default: downloads/)\n\nCITATIONS:\n  --citations        Extract citations\n  --citation-format  Citation format (bibtex|ris|json)\n\nHELP:\n  --help             Show all options\n\nExamples by Use Case\nQuick Search\n# Find recent papers\npython scripts/research.py arxiv \"quantum computing\"\n\n# Search biomedical literature\npython scripts/research.py pubmed \"alzheimer disease\"\n\nComprehensive Research\n# Search multiple sources\npython scripts/research.py arxiv \"neural networks\" --max-results 30 --output arxiv.json\npython scripts/research.py semantic \"neural networks\" --max-results 30 --output semantic.json\n\n# Download important papers\npython scripts/research.py arxiv \"neural networks\" --download --max-results 10\n\nCitation Management\n# Generate BibTeX\npython scripts/research.py arxiv \"deep learning\" --format bibtex --output dl_refs.bib\n\n# Export to reference manager\npython scripts/research.py pubmed \"gene editing\" --format ris --output genes.ris\n\nTracking New Research\n# This month's papers\npython scripts/research.py arxiv \"LLM\" --start-date 2024-01-01 --sort-by date\n\n# Recent highly-cited work\npython scripts/research.py semantic \"transformers\" --year 2023 --min-citations 50\n\nSupport\n\nFor issues or questions:\n\nCheck this documentation\nRun python scripts/research.py --help\nVerify dependencies are installed\nCheck database-specific documentation\n\nResources:\n\nOpenClawCLI: https://clawhub.ai/\narXiv API: https://arxiv.org/help/api\nPubMed API: https://www.ncbi.nlm.nih.gov/books/NBK25501/\nSemantic Scholar API: https://api.semanticscholar.org/"
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