Skill UI

The Research Literature Review tool is designed to search, analyze, and summarize information from diverse academic sources. It integrates local PDF libraries, Zotero collections, Obsidian notes, and multiple web APIs (Semantic Scholar, DeepXiv, etc.). Use it when you need to conduct a comprehensive literature review, find related academic work, or understand the core ideas of a specific research paper.

Deep research and scientific writing assistant that produces LaTeX/BibTeX documents with verified citations for papers, proposals, reports, and reviews, ensuring well-formatted, citation-backed outputs for academic or clinical contexts.

Defines a systematic, multi-phase research methodology that prioritizes broad exploration, targeted deep dives, and validation before initiating any content generation, ensuring reliable, multi-angle web intelligence.

This toolkit provides comprehensive automation capabilities for Box cloud storage. It allows users to manage the entire document lifecycle, including file uploading and downloading, deep content searching using metadata filters, creating and modifying folders, and managing collaboration permissions. Ideal for integrating Box into automated business workflows, ensuring documents are always accessible and organized.

This skill transforms an LLM into a disciplined wiki maintainer for Obsidian. It moves beyond simple RAG by incrementally ingesting sources (papers, notes, articles) to build a persistent, interlinked "Second Brain." The LLM manages cross-references, updates core concepts, and maintains an evolving knowledge graph, ensuring that knowledge compounds over time rather than being re-derived on every query. Ideal for deep research, personal knowledge management (PKM), and building comprehensive internal wikis.

This technique guides users through a rigorous two-phase research strategy. First, conduct a broad search to identify potential papers or authors. Crucially, the system instructs the user to use the extracted "author full name" as a highly specific anchor in subsequent queries. Combining the author's name with details like journal, institution, year, or biographical information ensures the most precise and reliable results, significantly narrowing down the search scope and achieving deep academic insights.

This sophisticated orchestrator manages the complete, end-to-end academic publication lifecycle. It moves through a mandatory 10-stage workflow, guiding users from initial deep research and drafting to rigorous peer review, revision, and final integrity checking. It ensures reproducible quality gates, mandatory citation verification, and automatically generates process documentation, streamlining the complex journey from concept to final manuscript.

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.