Skill UI

Provides a comprehensive audit of content quality, focusing on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals, readability, and structural integrity. Use this tool when reviewing content for SEO optimization, assessing academic citation readiness, or ensuring adherence to modern search engine and AI best practices.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

A comprehensive mentor guiding users through the entire lifecycle of academic literature reviews. It teaches systematic search strategies (PICO/PCC), critical appraisal methods, and advanced synthesis techniques. The skill covers searching major English and Chinese databases (e.g., PubMed, Web of Science, CNKI), organizing findings, and effectively utilizing AI research tools like Elicit and Semantic Scholar to build robust, evidence-based research papers.

Utilizes Grammarly's advanced APIs to conduct comprehensive content auditing. This skill detects the likelihood of AI-generated content and checks the provided text against vast databases for plagiarism. It provides a combined score assessing overall writing quality, clarity, and academic integrity, making it essential for professional, academic, and published content review.

Conduct comprehensive systematic literature reviews across biomedical and scientific domains using multiple academic databases. Synthesize findings, verify citations, and generate professional markdown or PDF documents. Includes AI-generated scientific schematics for visual enhancement. Ideal for research synthesis, meta-analyses, and thesis writing.

Zero-context verification system that checks every number, comparison, and scope claim in research papers against raw result files. Uses a fresh cross-model reviewer with no prior context to prevent confirmation bias. Ideal for academic integrity, paper submission, and ensuring paper-to-evidence fidelity before publication.

Generates publication-quality academic figures through a multi-stage workflow: Claude plans and reviews, while a local Codex app-server bridge renders images with native image generation. Iterates with strict scoring until the figure is paper-ready.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.

This skill leverages the Gemini AI model to perform comprehensive and broad literature discovery across academic domains. Unlike traditional APIs, it decomposes complex research queries into sub-problems, explores topic aliases, and surfaces highly relevant papers that might otherwise be missed. It is ideal for conducting deep, multi-angled literature reviews and achieving a holistic view of a research field.

A powerful skill for searching and retrieving comprehensive academic metadata using the OpenAlex API. It provides a unified view of research papers, including full open citation graphs, detailed institutional affiliations, and crucial funding information (e.g., NSF, NIH). Ideal for deep academic research, comparative literature reviews, and identifying research trends across multiple disciplines.

Per-page Codex review plus targeted python-pptx and Beamer fixes for existing academic talk decks: scales PPTX fonts to projector-readable size, resizes text frames, removes italic style leaks, fixes overflow, and aligns visual weight with a reference PDF.