Agent skill · research science · bighardperson
paper_summarize
Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with rigorous analysis templates.
Why this skill is useful
Adds dynamic SOP selection and rigorous analysis templates for academic paper summarization that are not commonly available in public resources.
What it needs
About 2k tokens when loaded. Last updated 2026-04-26. 34 stars on the source repository.
What this skill does
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 techreport: System/model release papers application: Applied AI papers survey: Survey/review papers rlalignment: RL/Alignment/Safety papers speechaudio: 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: {papertitle}.md in research/{domain}/aisummaries/ 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 …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bighardperson/paper-summarize-academic