Skill UI

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.

Use the OpenAlex client to query 240M+ scholarly works for literature searches, citation tracking, open access discovery, and bibliometric studies across papers, authors, and institutions.

A comprehensive tool for interacting with the arXiv database. It allows users to search for academic papers by topic or ID, retrieve detailed metadata (abstracts, authors, categories), and download the PDF files directly. Ideal for researchers and students needing to efficiently find, save, and summarize scientific literature.

This skill automates the generation of publication-quality figures and comparison tables for academic papers. It converts structured experimental data (e.g., JSON, CSV) into various plots, including line graphs, bar charts, scatter plots, and heatmaps, ensuring consistent styling suitable for LaTeX submissions. It handles the core data visualization needs for ML and research reports, greatly streamlining the process of creating professional results sections.

A comprehensive tool designed to search, analyze, and summarize academic research papers from multiple sources. It integrates data from local PDF libraries, Zotero, Obsidian, and major academic web APIs (e.g., Semantic Scholar, DeepXiv). Use this skill when conducting literature reviews, needing related work suggestions, or requiring a deep understanding of a specific research topic.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

This gate systematically evaluates experimental outcomes against intended research claims. It collects metrics from various sources (W&B, logs, etc.) and uses an objective judgment model to determine if the data supports, partially supports, or fails to support the hypothesis. It provides specific evidence gaps and suggests concrete next steps, guiding the research workflow before writing papers or finalizing designs.

This skill searches for published academic papers from major venues like IEEE, ACM, and Springer via the Semantic Scholar API. It is ideal for finding peer-reviewed journal articles and conference proceedings, providing crucial metadata such as citation counts, venue details, and TLDR summaries. Use it when you need verified, published literature beyond raw arXiv preprints.

This tool functions as a persistent, structured knowledge base for the entire research lifecycle. It accumulates and maps all core research entities—including published papers, generated ideas, experiments, and testable claims—along with their complex relationships (e.g., contradiction, support, inspiration). Inspired by LLM Wiki patterns, it ensures that knowledge is compounded and perpetually queryable, moving beyond single-use literature reviews.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

Paperzilla is a comprehensive skill for interacting with curated academic research data. Users can query the latest project recommendations, fetch canonical papers with detailed summaries (markdown), track project feeds, and provide feedback. It allows deep analysis and export of research insights, making it essential for researchers and students needing structured academic information.

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.