{
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    "slug": "quality-filter-research",
    "name": "Research Paper Quality Filter",
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    "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. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Quality Filter Skill",
        "body": "This skill provides systematic quality filtering for academic papers with a rigorous scoring system and complete audit trail for research workflows."
      },
      {
        "title": "Capabilities",
        "body": "Relevance Scoring: Evaluates paper relevance based on title and abstract keywords\nQuality Assessment: Assesses technical quality and experimental rigor\nComprehensive Logging: Maintains detailed records of all filtering decisions\nManual Recall Support: Preserves filtered papers for potential human review\nLocal File Storage: Saves all results to organized directory structure"
      },
      {
        "title": "Relevance Scoring (Max 3 points)",
        "body": "Strong Match (+3): Title contains \"music\" or \"song\" keywords\nMedium Match (+2): Title contains \"audio\" + \"generation\"\nWeak Match (+1): Title has weak but related keywords\nNegative Scoring: Abstract verification can subtract points (-1 to -3)"
      },
      {
        "title": "Quality Assessment (Max 3 points)",
        "body": "High Quality (+3): Complete experiments, multiple baselines, strong results\nMedium Quality (+2): Experiments present but limited baseline comparison\nLow Quality (+1): Limited technical contribution or incomplete evaluation"
      },
      {
        "title": "Pass Threshold",
        "body": "Minimum Score: 6/10 points required to pass filtering\nStrong Relevance Override: Papers with clear \"music/song generation\" focus may pass with lower scores"
      },
      {
        "title": "Workflow Integration",
        "body": "This skill integrates with the broader research workflow:\n\nInput: Raw paper list from arXiv search\nProcessing: Applies scoring system to each paper\nOutput: Categorizes papers as \"passed\" or \"filtered\"\nAudit Trail: Maintains complete record for manual recall"
      },
      {
        "title": "Local File Storage",
        "body": "Main Log: research/{domain}/quality_filtering/quality_filtering_log.md\nAppend Mode: All results appended to single comprehensive file\nDirectory Structure: Automatically created if missing"
      },
      {
        "title": "Log Structure",
        "body": "Each filtering session includes:\n\nSession Header: Date, domain, search parameters\nScoring Standards: Detailed criteria used\nIndividual Paper Results: Title, authors, score breakdown, decision\nSummary Statistics: Pass/fail counts, score distribution\nManual Recall Section: List of filtered papers available for human review"
      },
      {
        "title": "Usage Examples",
        "body": "# Filter music generation papers\nquality_filter --domain \"music_generation\" --papers \"[paper_list]\" --date \"2026-02-28\"\n\n# Filter with custom threshold\nquality_filter --domain \"speech_audio\" --threshold 5 --papers \"[paper_list]\""
      },
      {
        "title": "Files Created",
        "body": "research/{domain}/quality_filtering/quality_filtering_log.md (append mode)\nDirectory structure automatically created if missing"
      },
      {
        "title": "Audit Trail Requirements",
        "body": "All filtering decisions must include:\n\nComplete score breakdown (relevance + quality components)\nClear pass/fail rationale\nPreservation of filtered papers for manual recall\nTimestamp and session context"
      }
    ],
    "body": "Quality Filter Skill\n\nThis skill provides systematic quality filtering for academic papers with a rigorous scoring system and complete audit trail for research workflows.\n\nCapabilities\nRelevance Scoring: Evaluates paper relevance based on title and abstract keywords\nQuality Assessment: Assesses technical quality and experimental rigor\nComprehensive Logging: Maintains detailed records of all filtering decisions\nManual Recall Support: Preserves filtered papers for potential human review\nLocal File Storage: Saves all results to organized directory structure\nFiltering Criteria\nRelevance Scoring (Max 3 points)\nStrong Match (+3): Title contains \"music\" or \"song\" keywords\nMedium Match (+2): Title contains \"audio\" + \"generation\"\nWeak Match (+1): Title has weak but related keywords\nNegative Scoring: Abstract verification can subtract points (-1 to -3)\nQuality Assessment (Max 3 points)\nHigh Quality (+3): Complete experiments, multiple baselines, strong results\nMedium Quality (+2): Experiments present but limited baseline comparison\nLow Quality (+1): Limited technical contribution or incomplete evaluation\nPass Threshold\nMinimum Score: 6/10 points required to pass filtering\nStrong Relevance Override: Papers with clear \"music/song generation\" focus may pass with lower scores\nWorkflow Integration\n\nThis skill integrates with the broader research workflow:\n\nInput: Raw paper list from arXiv search\nProcessing: Applies scoring system to each paper\nOutput: Categorizes papers as \"passed\" or \"filtered\"\nAudit Trail: Maintains complete record for manual recall\nOutput Format\nLocal File Storage\nMain Log: research/{domain}/quality_filtering/quality_filtering_log.md\nAppend Mode: All results appended to single comprehensive file\nDirectory Structure: Automatically created if missing\nLog Structure\n\nEach filtering session includes:\n\nSession Header: Date, domain, search parameters\nScoring Standards: Detailed criteria used\nIndividual Paper Results: Title, authors, score breakdown, decision\nSummary Statistics: Pass/fail counts, score distribution\nManual Recall Section: List of filtered papers available for human review\nUsage Examples\n# Filter music generation papers\nquality_filter --domain \"music_generation\" --papers \"[paper_list]\" --date \"2026-02-28\"\n\n# Filter with custom threshold\nquality_filter --domain \"speech_audio\" --threshold 5 --papers \"[paper_list]\"\n\nFiles Created\nresearch/{domain}/quality_filtering/quality_filtering_log.md (append mode)\nDirectory structure automatically created if missing\nAudit Trail Requirements\n\nAll filtering decisions must include:\n\nComplete score breakdown (relevance + quality components)\nClear pass/fail rationale\nPreservation of filtered papers for manual recall\nTimestamp and session context"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/nomorecoding/quality-filter-research",
    "publisherUrl": "https://clawhub.ai/nomorecoding/quality-filter-research",
    "owner": "nomorecoding",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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