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.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

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.

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.