Skill UI

Identifies and rewrites AI-generated phrasing by removing inflated claims, promotional language, formulaic structures, and robotic cadence so reviewed prose sounds natural, varied, and personality-driven.

Streamlines citation discovery via Google Scholar and PubMed, extracts complete metadata, validates references, and generates formatted BibTeX for accurate academic manuscripts or theses.

This skill transforms flawed writing—whether pasted text or uploaded documents—into publication-ready prose. It meticulously eliminates issues related to grammar, spelling, punctuation, clarity, and tone, all while strictly preserving the original author's unique voice and intent. It operates in two modes: inline text correction and structured file processing (supporting formats like .docx, .pdf, .txt). Ideal for academic papers, business reports, and any text requiring a professional polish.

Automates converting academic LaTeX papers between publisher templates by extracting core sections, mapping them into the new template structure, fixing formatting/floats, and compiling while surfacing log errors for debugging.

Deep research and scientific writing assistant that produces LaTeX/BibTeX documents with verified citations for papers, proposals, reports, and reviews, ensuring well-formatted, citation-backed outputs for academic or clinical contexts.

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.

Creates formally structured .docx academic documents with auto-updating TOC, OMML equations, styled headings, footnotes/endnotes, and bibliography formatting for research reports or policy briefs.

Generates professional conference presentation slides from a compiled research paper. This skill produces dual outputs: a polished Beamer LaTeX PDF for high-quality typesetting, and an editable PPTX for flexible adjustments. It supports multiple talk types (Poster, Spotlight, Oral, Invited) and includes detailed guidelines for speaker notes and talk scripting, making it essential for academic researchers preparing for major ML and systems conferences.

This technique guides users on decomposing multi-constraint questions, especially in the domain of football trivia or historical research. Instead of searching all criteria simultaneously, it instructs users to rank constraints by distinctiveness (e.g., specific match results first, then historical milestones). By performing incremental, cascading searches, the system dramatically narrows down the result set, leading to more accurate information retrieval.