Skill UI

Enables efficient querying of the bioRxiv preprint server with keywords, authors, dates, and categories, returning structured JSON metadata and optional PDF downloads for literature reviews or tracking research trends.

A specialized tool for searching published, peer-reviewed academic papers from major venues like IEEE, ACM, and Springer. Unlike preprint services (like arXiv), this skill provides verified citation counts, detailed venue metadata, and filtering options for finding established, high-impact research literature. Ideal for formal academic research and literature reviews.

Use this helper to search ten academic paper databases via their REST APIs whenever you need research papers, preprints, citation data, metadata, or open-access PDFs; it routes DOI/PMID/author/topic queries to the right endpoints and returns raw JSON plus the queried databases/paths.

Search 18 scholarly APIs for papers, preprints, citations, open-access full text, repository records, and journal OA status with reproducible provenance.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

A comprehensive pre-submission audit guide for authors submitting papers to EACL (Computational Linguistics). It covers critical areas such as adherence to format gates (8/4 content pages, required Limitations section), strict anonymity requirements, mandatory declarations (Responsible NLP, preprint status), and crucial advice regarding the single, non-fallback submission cycle. Use this before uploading to the ACL Rolling Review cycle.

This skill provides comprehensive guidance for authors targeting eLife, an open-access journal in life sciences. It details the unique Reviewed-Preprint model, covering rigorous standards for reproducibility, transparency, and evidential strength. Use this guide to assess if your manuscript, regardless of its novelty, aligns with eLife's mandate for open science and robust scientific reporting.

This guide offers advanced methodological advice for structuring the 'Related Work' section for top-tier theoretical computer science conferences (like FOCS). It teaches researchers how to treat research history as 'lineage engineering,' requiring precise citation of specific preprints (arXiv version pinning), distinguishing between conference and journal versions, and transparently addressing concurrent results. This ensures the bibliography is rigorous, consistent, and withstands expert review.

A comprehensive guide for structuring the "Related Work" section for top-tier computer vision conferences (ICCV, CVPR, ECCV). It covers best practices for citing intervening-cycle literature, managing concurrent preprints, ensuring double-blind anonymity, and correctly attributing source venues to maximize novelty perception and score highly with reviewers.

A detailed methodology guide for authors submitting to highly competitive software engineering venues (like ICSE). It teaches how to structure the Related Work section not as a bibliography, but as a 'delta statement' that explicitly outlines the unique differences and contributions compared to the state-of-the-art across multiple publication lanes (journals, preprints, flagship venues).

Provides comprehensive guidelines for authors submitting to major AI conferences. It helps authors audit originality, properly cite prior art (including preprints, workshop papers, and rejected versions), manage dual-submission risks, and transform the related work section from a mere list into a compelling, novel argument that clearly positions the contribution.

This guide outlines the standards for making pure theoretical claims independently verifiable. It demands complete, self-contained proofs for all central lemmas, explicit definitions for every symbol, and precise citation for all borrowed results. It ensures that the submitted paper, the full preprint version, and the mathematical arguments are perfectly consistent, addressing the highest level of academic rigor in theoretical computer science.