Academic paper summarization with dynamic SOP selection based on paper topic classification. Supports method, dataset, multimodal, and other paper types with...
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Academic Paper Summarizer automatically analyzes research papers and generates structured, in-depth summaries using dynamic analysis templates matched to the paper's topic and type. It applies evaluation standards from top-tier conference review criteria (NeurIPS, ICML, ICLR, ACL) to deliver rigorous, comprehensive assessments.
Unlike simple summarization tools, this skill classifies each paper by type — methodology, dataset, multimodal, survey, RL/alignment, and more — then applies a specialized analysis framework tailored to that category.
Users provide paper metadata (title, authors, abstract, topic classification) and research context. The skill classifies the paper type, selects the corresponding evaluation framework, generates a structured analysis following defined quality standards, and saves outputs to organized directories with corresponding prompt files for full reproducibility.
Install the skill — no complex setup or dependencies required. Provide paper metadata and the skill generates a structured analysis. Output files are saved to research/{domain}/ai_summaries/ with prompts preserved separately for audit purposes.
MIT-0 (Free to use, modify, and redistribute. No a
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