Skill UI

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.

A powerful web toolkit powered by Exa, specifically optimized for scholarly and technical research. This skill handles advanced web searching (semantic lookups, current events) and precise URL content extraction (fetching articles, PDFs, and web pages). It excels in academic contexts, allowing users to bias results toward peer-reviewed papers and specific scholarly domains for high-quality, reliable information gathering.

This skill acts as a comprehensive hybrid router for any research request. It first deterministically classifies the user's intent, delegating the query to specialized modules like Pulse (trends/sentiment), Grants (NIH funding), or LitReview (academic papers). If no specialist matches, it falls back to a robust general search and synthesis workflow. The system ensures routing transparency, provides comprehensive reports, and includes full citations and an audit log.

This skill transforms a course syllabus (PDF, DOCX, etc.) into a highly curated, supplementary academic reading list. It parses topics and learning outcomes, calibrates content difficulty based on the target audience (undergraduate, graduate, etc.), and searches academic databases (Consensus) for recent, peer-reviewed papers. The output is a professional .docx file containing summaries, links, and advanced discussion questions, making it invaluable for educators and students needing structured, up-to-date course resources.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

This meta-skill automates the process of compiling a daily briefing on the top submissions from the arXiv repository. It fetches the latest papers in a specified category, generates a structured, per-paper digest summarizing the core claims, methods, and key findings. It then renders this information into a professional PowerPoint deck (one slide per paper) and persists the entire digest into long-term memory. Ideal for daily research briefings or knowledge management.

This tool is designed to generate a concise and academically rigorous abstract for completed research papers. By taking the revised body, the function synthesizes the core problem, methodology, key results, and overall significance. It adheres strictly to defined style preferences, audience needs, and outputting a pure LaTeX fragment, ensuring the summary is accurate and professional.

This tool assists researchers in structuring academic papers by mapping specific claims across different sections (Introduction, Method, Results, Discussion) to required external citations (BibTeX keys). It enforces citation density rules and ensures the correct role alignment (e.g., background, comparison, limitation) before the actual drafting process, guaranteeing that every core claim is properly supported and distributed.

This meta-skill orchestrates the entire academic writing workflow, from initial requirement extraction (topic, length, audience) to drafting, citation management, and final LaTeX/PDF compilation. It supports various modes, including compact skeletons for quick drafts and full manuscripts for publication-ready work. Ideal for users needing structured help with research papers or academic manuscripts.

This tool reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.

This utility reads structured JSON data from multiple search engines (multi-search-engine JSON) provided via standard input. It processes this academic data and converts it into a minimal, standardized BibTeX format, primarily generating @misc{} entries. This feature is ideal for automating the creation of a bibliography or reference list for scholarly papers, enhancing research workflow efficiency.

This specialized tool revises fragmented drafts of academic papers (including Introduction, Method, Results, and Discussion) into a single, cohesive, and professionally structured LaTeX body. It ensures strict consistency across terminology, metrics, and tone, while adhering to specific preferences like audience, venue style, and required citations. It is designed for academic authors preparing the main manuscript body before writing the abstract.