Skill UI

A comprehensive workflow for systematically reviewing medical evidence. It allows users to search databases for clinical studies, build curated citation collections, grade evidence using established frameworks like GRADE, and generate professionally formatted, structured reports in academic citation styles (e.g., AMA). Ideal for conducting literature reviews and supporting clinical research. This tool helps transform raw medical data into actionable, structured knowledge.

BGPT Paper Search queries the remote MCP server to retrieve structured experimental data from full-text studies, returning methods, results, sample sizes, quality scores, and conclusions for efficient literature reviews and evidence synthesis.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

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.

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.

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 tool synthesizes multiple, scattered research findings retrieved from memory into a single, structured, and actionable report. It automatically grades evidence quality (High/Medium/Low), resolves conflicting claims, and provides concrete, recommendation-based insights, making complex data digestible for decision-making.

This guide outlines a systematic methodology for conducting comprehensive literature reviews in economics. It moves beyond simple summarizing by teaching researchers how to build an evidence matrix, ensuring systematic coverage by sweeping databases (JEL codes, keywords, working papers). It is essential for preparing high-impact academic reviews that must prove exhaustive coverage and synthesize complex, often conflicting, findings.

Provides comprehensive guidelines for authors conducting systematic reviews and meta-analyses in psychology. It emphasizes documenting the literature search protocol, ensuring quantitative synthesis rigor (PRISMA flow, effect-size data), and making all findings open and reproducible for high-impact academic publication.

A methodological guide for documenting the rigor of literature reviews, especially in sociology. It details how to build a comprehensive 'coverage account' (a near-PRISMA record of the search process) and how to ensure any quantitative synthesis (meta-analysis, bibliometrics) conducted within the review is fully reproducible by depositing data and code.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

This guide models the rigorous, multi-cycle decision-making process for top-tier data mining conferences like KDD. It details how decisions are weighed, considering reviewer scores, rebuttal strategies, area chair synthesis, and specific criteria (e.g., reproducibility, ethics, industrial feasibility) across outcomes like Accept, Resubmit, and Reject. Essential for understanding academic publishing rigor.