What problem does it solve?
This Skill converts academic paper PDFs into structured, bilingual research reports while extracting key scientific elements (metadata, abstract, logical entry points, methods, findings, innovations, and insights) and then applying automatic proofreading/quality checks to reduce missing content and low-quality output.
Core Features & Use Cases
- Structured paper analysis: Extracts bibliographic metadata (title/authors/journal/DOI/year/keywords), full abstract, and 12+ report sections such as core hypotheses, research motivation, theoretical basis, research problem, solution approach, verification methods, characterization techniques, key findings, innovations, and writing references.
- Multi-skill integration workflow: Orchestrates calls to related skills/tools (e.g., paper-parse, literature-tree, paper-downloader, file-manager) to enrich and deepen analysis (e.g., reference network support and metadata completion).
- Quality assurance & proofreading: Runs format, scientific, readability, and publication-readiness scoring, flags issues, and supports improvement/regeneration loops when the quality threshold is not met.
- Use cases: Rapidly produce a literature review draft for a single PDF, run batch analysis across a directory of papers, and generate consistent reports suitable for research summarization and downstream knowledge graph/insight workflows.
Quick Start
Use the paper-auto-analyzer skill to analyze the PDF file in your working folder named "0-Nano Lett.-快充不对称.pdf" and generate the structured bilingual analysis report with quality checks.
