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.

This skill functions as an expert-level web researcher, utilizing advanced search strategies (query optimization, domain filtering) to gather, synthesize, and validate information from multiple web sources. It is ideal for complex research tasks requiring fact verification, identifying trends, and producing structured, actionable insights, ensuring high credibility for the final output.

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.

Guides structured hypothesis generation by combining literature searches, evidence synthesis, competing explanations, and experimental design, with mandatory AI-generated scientific schematics for complete reports.

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.

Searches BGPT scientific papers by topic or DOI and retrieves claim-level evidence extracted from full text, including experiments, reported statistics, scope, limitations, and provenance. Supports literature reviews and evidence synthesis.

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 meta-skill orchestrates a comprehensive workflow for analyzing PDF documents. It handles complex tasks such as summarizing, cross-document comparison, and targeted Q&A. It ensures findings are traceable by indexing evidence from various sources (full PDFs, pasted excerpts, etc.) and synthesizing structured insights across multiple files.

A comprehensive framework for structuring user interviews, customer discovery, or Jobs-to-be-Done (JTBD) research. This guide provides structured steps for screener creation, deep behavioral exploration (focusing on past actions, not future predictions), and systematic synthesis/pattern detection, ensuring the gathering of genuine, actionable insights.

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.