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    "source": "clawhub",
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    "sections": [
      {
        "title": "Journal Matchmaker",
        "body": "Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise."
      },
      {
        "title": "Use Cases",
        "body": "Find the best-fit journal for a new manuscript\nIdentify high-impact factor journals in specific research areas\nCompare journal scopes against paper content\nDiscover domain-specific publication venues"
      },
      {
        "title": "Usage",
        "body": "python scripts/main.py --abstract \"Your paper abstract text here\" [--field \"field_name\"] [--min-if 5.0] [--count 5]"
      },
      {
        "title": "Parameters",
        "body": "ParameterTypeRequiredDefaultDescription--abstractstrYes-Paper abstract text to analyze--fieldstrNoAuto-detectResearch field (e.g., \"computer_science\", \"biology\")--min-iffloatNo0.0Minimum impact factor threshold--max-iffloatNoNoneMaximum impact factor (optional)--countintNo5Number of recommendations to return--formatstrNotableOutput format: table, json, markdown"
      },
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        "title": "Examples",
        "body": "# Basic usage\npython scripts/main.py --abstract \"This paper presents a novel deep learning approach...\"\n\n# Specify field and minimum impact factor\npython scripts/main.py --abstract \"abstract.txt\" --field \"ai\" --min-if 10.0 --count 10\n\n# Output as JSON for integration\npython scripts/main.py --abstract \"...\" --format json"
      },
      {
        "title": "How It Works",
        "body": "Abstract Analysis: Extracts key terms, methodology, and research focus\nField Classification: Identifies the primary research domain\nJournal Matching: Compares content against journal scopes and aims\nImpact Factor Filtering: Applies IF constraints if specified\nRanking: Scores and ranks journals by relevance and impact"
      },
      {
        "title": "Technical Details",
        "body": "Difficulty: Medium\nApproach: Keyword extraction + journal database matching\nData Source: Journal metadata from references/journals.json\nAlgorithm: TF-IDF + cosine similarity for scope matching"
      },
      {
        "title": "References",
        "body": "references/journals.json - Journal database with impact factors and scopes\nreferences/fields.json - Research field classifications\nreferences/scoring_weights.json - Algorithm tuning parameters"
      },
      {
        "title": "Notes",
        "body": "Journal database should be updated periodically (quarterly recommended)\nImpact factor data sourced from Journal Citation Reports (JCR)\nScope descriptions parsed from official journal websites\nFor emerging fields, manual curation may be needed"
      },
      {
        "title": "Risk Assessment",
        "body": "Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow"
      },
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      },
      {
        "title": "Prerequisites",
        "body": "# Python dependencies\npip install -r requirements.txt"
      },
      {
        "title": "Success Metrics",
        "body": "Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable"
      },
      {
        "title": "Test Cases",
        "body": "Basic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time"
      },
      {
        "title": "Lifecycle Status",
        "body": "Current Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\n\nPerformance optimization\nAdditional feature support"
      }
    ],
    "body": "Journal Matchmaker\n\nAnalyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.\n\nUse Cases\nFind the best-fit journal for a new manuscript\nIdentify high-impact factor journals in specific research areas\nCompare journal scopes against paper content\nDiscover domain-specific publication venues\nUsage\npython scripts/main.py --abstract \"Your paper abstract text here\" [--field \"field_name\"] [--min-if 5.0] [--count 5]\n\nParameters\nParameter\tType\tRequired\tDefault\tDescription\n--abstract\tstr\tYes\t-\tPaper abstract text to analyze\n--field\tstr\tNo\tAuto-detect\tResearch field (e.g., \"computer_science\", \"biology\")\n--min-if\tfloat\tNo\t0.0\tMinimum impact factor threshold\n--max-if\tfloat\tNo\tNone\tMaximum impact factor (optional)\n--count\tint\tNo\t5\tNumber of recommendations to return\n--format\tstr\tNo\ttable\tOutput format: table, json, markdown\nExamples\n# Basic usage\npython scripts/main.py --abstract \"This paper presents a novel deep learning approach...\"\n\n# Specify field and minimum impact factor\npython scripts/main.py --abstract \"abstract.txt\" --field \"ai\" --min-if 10.0 --count 10\n\n# Output as JSON for integration\npython scripts/main.py --abstract \"...\" --format json\n\nHow It Works\nAbstract Analysis: Extracts key terms, methodology, and research focus\nField Classification: Identifies the primary research domain\nJournal Matching: Compares content against journal scopes and aims\nImpact Factor Filtering: Applies IF constraints if specified\nRanking: Scores and ranks journals by relevance and impact\nTechnical Details\nDifficulty: Medium\nApproach: Keyword extraction + journal database matching\nData Source: Journal metadata from references/journals.json\nAlgorithm: TF-IDF + cosine similarity for scope matching\nReferences\nreferences/journals.json - Journal database with impact factors and scopes\nreferences/fields.json - Research field classifications\nreferences/scoring_weights.json - Algorithm tuning parameters\nNotes\nJournal database should be updated periodically (quarterly recommended)\nImpact factor data sourced from Journal Citation Reports (JCR)\nScope descriptions parsed from official journal websites\nFor emerging fields, manual curation may be needed\nRisk Assessment\nRisk Indicator\tAssessment\tLevel\nCode Execution\tPython/R scripts executed locally\tMedium\nNetwork Access\tNo external API calls\tLow\nFile System Access\tRead input files, write output files\tMedium\nInstruction Tampering\tStandard prompt guidelines\tLow\nData Exposure\tOutput files saved to workspace\tLow\nSecurity Checklist\n No hardcoded credentials or API keys\n No unauthorized file system access (../)\n Output does not expose sensitive information\n Prompt injection protections in place\n Input file paths validated (no ../ traversal)\n Output directory restricted to workspace\n Script execution in sandboxed environment\n Error messages sanitized (no stack traces exposed)\n Dependencies audited\nPrerequisites\n# Python dependencies\npip install -r requirements.txt\n\nEvaluation Criteria\nSuccess Metrics\n Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable\nTest Cases\nBasic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time\nLifecycle Status\nCurrent Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\nPerformance optimization\nAdditional feature support"
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    "provenanceUrl": "https://clawhub.ai/AIPOCH-AI/journal-matchmaker",
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    "owner": "AIPOCH-AI",
    "version": "1.0.0",
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