Skill UI

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.

Compiles comprehensive, verifiable external evidence for writing high-quality scientific manuscripts or research briefs. It conducts deep, multi-source academic searches, targeting verified references and structuring the findings into a manuscript-ready research packet. Ideal for in-depth literature reviews, hypothesis testing, and building structured evidence matrices.

This skill provides a robust, systematic framework for evaluating the rigor of scientific claims and research studies. It guides users in assessing experimental design validity, detecting various biases (selection, confounding, publication), and evaluating statistical evidence. By applying established grading standards like GRADE and Cochrane Risk of Bias, it is essential for conducting systematic literature reviews, peer reviews, and deep scientific critical analysis.

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.

A powerful resource for academic research, DeepXiv facilitates structured and progressive reading of open-access papers. It moves beyond simple retrieval by allowing users to search by topic or ID, retrieve brief summaries, view section maps, or focus on specific chapters, ensuring a layered and efficient understanding of complex scientific literature.

This skill guides the systematic process of conducting comprehensive literature reviews across multiple academic databases (e.g., PubMed, arXiv, Semantic Scholar). It supports meta-analyses, scoping reviews, and state-of-the-art investigations. The rigorous workflow includes structured searching, data extraction, citation verification, thematic synthesis, and generating publication-quality reports (PDF/Markdown) complete with mandatory scientific schematics (like PRISMA flow diagrams) to ensure academic rigor and reproducibility.

This skill integrates 10 major academic literature APIs (including PubMed, arXiv, OpenAlex, Crossref, etc.). It guides users on converting complex research intents into reproducible, verifiable lookups for papers, preprints, citations, and open-access full texts. It ensures results include verifiable provenance and follows a rigorous workflow for maximum accuracy in scholarly research.

This tool facilitates comprehensive prior art searches across global patent databases (Google Patents, Espacenet) and academic literature (Google Scholar, arXiv). It guides users through extracting core inventive concepts, performing targeted searches using keywords and IPC/CPC codes, and analyzing the results to assess novelty, overlap risk, and general freedom-to-operate.

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.

A comprehensive server providing research-grade access to arXiv. Users can search papers by topic, author, or date, download full texts, and analyze the scientific landscape by traversing citation graphs. It also supports local semantic search over downloaded documents and enables monitoring for new papers with real-time alerts. Ideal for academic research and literature review.