Skill UI

This skill provides comprehensive guidance and established patterns for conducting rigorous scientific research and complex data analysis. It helps users structure their thinking process, ensuring a systematic and methodical approach to scholarly inquiry. Ideal for academic projects, literature reviews, and complex data interpretation.

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.

Literature Review Tool helps researchers gather and synthesize key insights from scholarly texts, highlighting patterns and critical arguments to accelerate literature surveying workflows even when structured content is initially unavailable.

This skill integrates 10 major academic literature APIs (including PubMed, arXiv, OpenAlex, Crossref, etc.). It guides users on converting complex research intents into reproducible, verifiable lookups for papers, preprints, citations, and open-access full texts. It ensures results include verifiable provenance and follows a rigorous workflow for maximum accuracy in scholarly research.

This powerful web toolkit uses Exa to perform advanced web searches and detailed URL content extraction. It is specifically tuned for scientific, technical, and academic research, allowing users to filter results by scholarly categories (e.g., research papers) and include academic domains. Use it for complex lookups, gathering current information, or batch extracting content from articles and PDFs.

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.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

This guide provides strict guidelines for compiling and formatting scholarly exhibits (tables, figures, and appendices) for top-tier accounting journals, such as CAR. It ensures compliance with academic style guides, requiring variable definitions, clustered standard errors, and self-contained presentation so that the results are immediately interpretable by reviewers. It is crucial for transforming raw analysis output into submission-ready academic materials.

This guide details the process of packaging artifacts for academic conferences (ICLR), ensuring that research claims are fully reproducible and reviewable. It emphasizes providing minimal reproduction paths, maintaining data provenance, rigorously anonymizing metadata to protect authors' identities, and creating robust, permanent records for the scholarly community.

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.

A comprehensive guide detailing best practices for creating self-contained, professional, and publication-ready exhibits (tables, figures, matrices) for scholarly manuscripts. It emphasizes making the body text standalone, adhering to journal-specific styles (e.g., APA, JAIS), and presenting complex data (like SEM results) clearly for rigorous peer review.

A comprehensive guide for structuring quantitative exhibits for scholarly manuscripts, specifically tailored for educational psychology journals. It focuses on adhering to APA 7th edition standards, accurately presenting model-based results, effect sizes with confidence intervals (CIs), growth trajectories, and ensuring full anonymization for masked review. It teaches researchers how to make complex statistical arguments legible and reproducible.