Paper Summarize

name paper_summarize description Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with rigorous analysis templates. author Claude (克劳德) version 1.0.1 Paper Summarize Skill This skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification. Capabilities Dynamic SOP Selection : Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.) Rigorous Analysis : Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL) Structured Output : Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses Local File Storage : Saves summaries to organized directory structure with proper naming Prompt Tracking : Maintains record of actual prompts used for reproducibility Dataset Focus : Explicit attention to training/evaluation datasets used in experiments Supported Paper Types method : Algorithm/architecture papers dataset : Dataset/benchmark papers multimodal : Cross-modal learning papers tech_report : System/model release papers application : Applied AI papers survey : Survey/review papers rl_alignment : RL/Alignment/Safety papers speech_audio : Speech/audio processing papers benchmark : Evaluation/benchmark papers analysis : Empirical analysis papers Usage Input Requirements Paper title, authors, abstract Topic classification (one of supported types) Research context (keywords, subtopics) Output Format Local file : {paper_title}.md in research/{domain}/ai_summaries/ Content structure : Paper information (title, authors, venue, links) Core contribution summary Methodology critique (2000+ words) Experimental assessment (1000+ words, with dataset focus) Strengths and weaknesses Critical questions for authors Impact assessment Quality Standards Methodology Critique : 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes Experimental Assessment : 1000+ characters, rigorous evaluation with explicit focus on datasets used for training and testing , protocol rigor, baseline fairness, ablation completeness, and statistical significance Overall Analysis : 3000+ characters, critical perspective Technical Precision : Correct terminology, specific method names, exact metrics Workflow Integration This skill integrates with the broader research workflow: Paper Discovery : Works with arXiv search results Quality Filtering : Processes papers that pass relevance screening Batch Processing : Can be called repeatedly for multiple papers Report Generation : Outputs feed into final research report Configuration SOP templates are defined in: src/lib/agents/topic-sops.ts (primary location) summarization_prompt.ts (backup/reference) Both files contain identical SOP definitions with shared output format requirements. Examples