Skill UI

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill facilitates structured, bidirectional reading and knowledge synthesis for academic texts, theories, and articles. It forces users to first capture the source material's original intent, before critically mapping its relationships, conflicts, and absorptions against the CrossFrame framework. It ensures structured output with clear source boundaries, making it ideal for deep learning and scholarly work.

Generate a structured, evidence-based deep-reading note for a single paper and save it to an Obsidian-style vault from a title, DOI, URL, arXiv ID, Zotero item, or PDF.

Turns articles, books, PDFs, and web pages into evidence-first reading reports. Five modes (quick, deep, map, feynman, book) separate claims, evidence, data, assumptions, counterarguments, and limitations, with knowledge maps, confidence tags, and Feynman recall checks.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.