Skill UI

This technique guides users on how to construct highly specific search queries for academic papers. Instead of relying solely on broad topic keywords, users should prioritize combining concrete numerical identifiers—such as exact sample sizes, population percentages, concentration levels, or specific year ranges—with methodological terms and discipline keywords. Using these numerical anchors significantly increases the uniqueness and precision of the search results, helping to pinpoint the exact study required.

A powerful tool designed for academic researchers and scientists. It searches a curated database of scientific literature and retrieves structured metadata and quantitative experimental data (including methods, results, sample sizes, and quality scores) directly from full-text papers, going far beyond simple abstracts. Ideal for systematic literature reviews, evidence synthesis, and meta-analyses requiring detailed, comparable study information.

A powerful skill providing real-time, intelligent research lookup. It automatically routes queries to the best backend—whether it's fast general web search, deep multi-source synthesis via a Chat API, or specialized academic paper searches. Use this to gather current scientific findings, verify facts, conduct literature reviews, and build robust background research.

This skill performs a rigorous, zero-context audit to verify that every quantitative claim, comparison, and scope statement made in a research paper precisely matches the underlying raw experimental results. It is designed to eliminate confirmation bias by treating the paper text and raw data as independent evidence sources.

Access and query 10 major academic paper databases (including PubMed, arXiv, OpenAlex, Crossref, etc.) via REST APIs. This tool facilitates comprehensive scholarly literature searches, allowing users to find abstracts, check citation details (DOI/PMID), track open access status, retrieve preprints, and analyze citation graphs across diverse fields like biomedicine and physics.

An all-in-one toolkit for deep web research, encompassing web search, content extraction, data enrichment, and multi-source deep reporting. It specializes in academic and scientific sources, prioritizing peer-reviewed papers, preprints, and scholarly databases. Use this skill for comprehensive investigations, literature reviews, or structured data collection from the web.

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.

A comprehensive tool for searching academic literature using the OpenAlex API. Retrieve detailed metadata, including open citation graphs, author institutional affiliations, and funding sources. Ideal for deep, systematic academic research when basic preprint or citation data is insufficient.

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.

Bilig WorkPaper provides a code-first runtime environment for simulating spreadsheet-style business logic. It enables agents and tools to interact with complex formulas, calculate outputs, and validate data deterministically using API calls (MCP) and JSON workbooks, bypassing the need for fragile UI automation in Excel or web browsers. Ideal for budget modeling, quote generation, and data integrity checks.