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.

Analyzes research papers to break down structure and summarize key points, offering citation highlights and insights for desk research or literature review planning.

Python toolkit that queries the arXiv Atom API to search papers by keywords, authors, categories, or IDs, filter results, download PDFs, and emit structured JSON for AI/ML literature reviews and monitoring.

This skill automates the complex and tedious process of porting academic papers written in LaTeX from one publisher's journal template (e.g., Springer) to another (e.g., MDPI, IEEE). It executes a structured multi-stage workflow, including content extraction, structural mapping, systematic fixing of common LaTeX issues (like float placement and math environments), and final compilation to ensure the output adheres to the target journal's specifications.

This agent provides structured summarization for academic papers, reports, and articles. It goes beyond simple summarizing by extracting key findings, generating comparative matrices across multiple sources, and producing properly formatted citations (APA, IEEE, etc.). It is designed for analysts, product managers, and researchers who need actionable, deep insights from dense documentation.

This skill autonomously improves generated academic papers by simulating a multi-round review, fixing, and recompilation cycle. It is designed to enhance writing quality, fix theoretical inconsistencies, and improve presentation structure after the initial drafting phase. It operates in rounds (default 2 rounds) using external LLM review and can optionally accept a style reference paper to guide structural fixes.

This skill transforms a refined research proposal or method idea into a detailed, structured, and claim-driven experiment roadmap. It helps users determine exactly which experiments to run, in what order, and what evidence is needed to support the core claims of a research paper, ensuring focus on novelty, simplicity, and genuine contribution.

This skill generates a structured, section-by-section outline for academic papers. By synthesizing diverse inputs—including research narratives, auto-review conclusions, and experiment results—it guides researchers in structuring their findings into a cohesive draft plan. It also supports optional style references to align the outline's section structure with specific target venues or published works.

Automatically generates structured academic papers in LaTeX format. By integrating a detailed paper plan, the research narrative, and generated figures, this tool structures the content section by section. It supports major conference and journal templates (e.g., ICLR, NeurIPS, IEEE), manages complex citation fetching via DBLP/CrossRef, and allows structural guidance using reference papers. Ideal for drafting the complete manuscript.

This skill automates the end-to-end academic paper writing process. It transforms raw narrative reports, research ideas, or existing plans into polished, submission-ready LaTeX source and PDF. The comprehensive pipeline includes planning, figure generation, structured writing, compilation, and an advanced improvement loop, allowing users to achieve publication-quality results. Users can optionally guide the structural style using a reference paper.

A comprehensive knowledge-base-first skill designed for deep literature reviews in advanced communication domains. Use it for topics like 4G/5G/6G, wireless networking, satellite communications, Wi-Fi, routing protocols, and cross-layer optimization. It systematically searches structured academic databases (IEEE, ACM, Zotero, etc.) to provide related work, survey papers, and landscape summaries.

This tool systematically structures and derives complex research formulas and theoretical models. It takes scattered equations, messy notes, and ambiguous ideas, transforming them into a coherent, paper-ready derivation package. Use it when you need to build a rigorous theory line, organize explicit assumptions, or clarify the conceptual object underlying a set of formulas.