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    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Paper Summarize Skill",
        "body": "This skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification."
      },
      {
        "title": "Capabilities",
        "body": "Dynamic SOP Selection: Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.)\nRigorous Analysis: Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL)\nStructured Output: Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses\nLocal File Storage: Saves summaries to organized directory structure with proper naming\nPrompt Tracking: Maintains record of actual prompts used for reproducibility\nDataset Focus: Explicit attention to training/evaluation datasets used in experiments"
      },
      {
        "title": "Supported Paper Types",
        "body": "method: Algorithm/architecture papers\ndataset: Dataset/benchmark papers\nmultimodal: Cross-modal learning papers\ntech_report: System/model release papers\napplication: Applied AI papers\nsurvey: Survey/review papers\nrl_alignment: RL/Alignment/Safety papers\nspeech_audio: Speech/audio processing papers\nbenchmark: Evaluation/benchmark papers\nanalysis: Empirical analysis papers"
      },
      {
        "title": "Input Requirements",
        "body": "Paper title, authors, abstract\nTopic classification (one of supported types)\nResearch context (keywords, subtopics)"
      },
      {
        "title": "Output Format",
        "body": "Local file: {paper_title}.md in research/{domain}/ai_summaries/\nContent structure:\n\nPaper information (title, authors, venue, links)\nCore contribution summary\nMethodology critique (2000+ words)\nExperimental assessment (1000+ words, with dataset focus)\nStrengths and weaknesses\nCritical questions for authors\nImpact assessment"
      },
      {
        "title": "Quality Standards",
        "body": "Methodology Critique: 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes\nExperimental Assessment: 1000+ characters, rigorous evaluation with explicit focus on datasets used for training and testing, protocol rigor, baseline fairness, ablation completeness, and statistical significance\nOverall Analysis: 3000+ characters, critical perspective\nTechnical Precision: Correct terminology, specific method names, exact metrics"
      },
      {
        "title": "Workflow Integration",
        "body": "This skill integrates with the broader research workflow:\n\nPaper Discovery: Works with arXiv search results\nQuality Filtering: Processes papers that pass relevance screening\nBatch Processing: Can be called repeatedly for multiple papers\nReport Generation: Outputs feed into final research report"
      },
      {
        "title": "Configuration",
        "body": "SOP templates are defined in:\n\nsrc/lib/agents/topic-sops.ts (primary location)\nsummarization_prompt.ts (backup/reference)\n\nBoth files contain identical SOP definitions with shared output format requirements."
      },
      {
        "title": "Examples",
        "body": "# Summarize a method paper\npaper_summarize --title \"SongEcho: Cover Song Generation\" --topic \"method\" --abstract \"...\" --authors \"...\"\n\n# Summarize a dataset paper  \npaper_summarize --title \"MusicSem: Language-Audio Dataset\" --topic \"dataset\" --abstract \"...\" --authors \"...\""
      },
      {
        "title": "Files Created",
        "body": "research/{domain}/ai_summaries/{paper_title}.md\nresearch/{domain}/prompts/{paper_title}_prompt.txt\nDirectory structure automatically created if missing"
      }
    ],
    "body": "Paper Summarize Skill\n\nThis skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification.\n\nCapabilities\nDynamic SOP Selection: Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.)\nRigorous Analysis: Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL)\nStructured Output: Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses\nLocal File Storage: Saves summaries to organized directory structure with proper naming\nPrompt Tracking: Maintains record of actual prompts used for reproducibility\nDataset Focus: Explicit attention to training/evaluation datasets used in experiments\nSupported Paper Types\nmethod: Algorithm/architecture papers\ndataset: Dataset/benchmark papers\nmultimodal: Cross-modal learning papers\ntech_report: System/model release papers\napplication: Applied AI papers\nsurvey: Survey/review papers\nrl_alignment: RL/Alignment/Safety papers\nspeech_audio: Speech/audio processing papers\nbenchmark: Evaluation/benchmark papers\nanalysis: Empirical analysis papers\nUsage\nInput Requirements\nPaper title, authors, abstract\nTopic classification (one of supported types)\nResearch context (keywords, subtopics)\nOutput Format\nLocal file: {paper_title}.md in research/{domain}/ai_summaries/\nContent structure:\nPaper information (title, authors, venue, links)\nCore contribution summary\nMethodology critique (2000+ words)\nExperimental assessment (1000+ words, with dataset focus)\nStrengths and weaknesses\nCritical questions for authors\nImpact assessment\nQuality Standards\nMethodology Critique: 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes\nExperimental Assessment: 1000+ characters, rigorous evaluation with explicit focus on datasets used for training and testing, protocol rigor, baseline fairness, ablation completeness, and statistical significance\nOverall Analysis: 3000+ characters, critical perspective\nTechnical Precision: Correct terminology, specific method names, exact metrics\nWorkflow Integration\n\nThis skill integrates with the broader research workflow:\n\nPaper Discovery: Works with arXiv search results\nQuality Filtering: Processes papers that pass relevance screening\nBatch Processing: Can be called repeatedly for multiple papers\nReport Generation: Outputs feed into final research report\nConfiguration\n\nSOP templates are defined in:\n\nsrc/lib/agents/topic-sops.ts (primary location)\nsummarization_prompt.ts (backup/reference)\n\nBoth files contain identical SOP definitions with shared output format requirements.\n\nExamples\n# Summarize a method paper\npaper_summarize --title \"SongEcho: Cover Song Generation\" --topic \"method\" --abstract \"...\" --authors \"...\"\n\n# Summarize a dataset paper  \npaper_summarize --title \"MusicSem: Language-Audio Dataset\" --topic \"dataset\" --abstract \"...\" --authors \"...\"\n\nFiles Created\nresearch/{domain}/ai_summaries/{paper_title}.md\nresearch/{domain}/prompts/{paper_title}_prompt.txt\nDirectory structure automatically created if missing"
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    "owner": "nomorecoding",
    "version": "1.0.1",
    "license": null,
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