Skill UI

This specialized tool drafts a single, academically rigorous section of a research paper. It accepts detailed structured inputs, including a complete writing plan, overall paper outline, citation budget, and optional figure/table context. The output is a pure LaTeX fragment that adheres to strict structural conventions for various sections (e.g., Introduction, Method, Results). It ensures the section advances the paper's single narrative thread while maintaining professional academic quality.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.

A comprehensive guide for academic researchers aiming to submit papers to the top accounting journal, *Accounting Research*. This skill helps authors identify if their research focus aligns with core accounting mechanisms, such as disclosure quality, auditing standards, and regulatory changes, distinguishing it from general corporate finance or economics.

This comprehensive guide provides expert strategies for structuring and auditing the "Related Work" section for top-tier NLP conferences (like ACL). It covers citation hygiene (Anthology vs. arXiv), maintaining temporal freshness against rapidly evolving baselines, and articulating clear technical contrasts to prove the novelty of your research. Essential for high-stakes academic publishing.

This comprehensive guide assists authors in evaluating the suitability, framing, and structural requirements for submitting research on wireless, mobile, IoT, sensing, and network security/privacy to the ACM WiSec conference. It provides critical expertise on aligning the paper's contribution type, threat model, and evidence standards with top-tier academic conference expectations, ensuring the work emphasizes the physical or network context.

A comprehensive guide for authors submitting academic papers to the ACM Symposium on Virtual Reality Software and Technology (VRST). This resource helps determine conference fit, frame the research contribution, and outlines the specific evidence (software, performance, immersive systems) required to meet the stringent standards of top-tier immersive computing venues.

This guide helps researchers elevate general empirical ideas (e.g., 'X affects Y') into rigorous, theory-driven accounting contributions. It outlines four sources of academic contribution, identifies key areas (auditing, financial accounting, governance), and teaches how to anchor research in specific accounting mechanisms and China's institutional context, meeting the standards of top-tier accounting journals.

A comprehensive guide on positioning academic manuscripts within the broader field conversation, specifically for general education-research journals. It teaches researchers how to move beyond niche citation clusters to establish a field-wide gap, articulate the stakes, and connect the study to adjacent subfields, ensuring the contribution is broadly relevant.

Provides a comprehensive guide to the American Educational Research Journal (AERJ) manuscript review process. It details the stages, including fit framing, editorial screening, and double-anonymous peer review, helping authors understand how to structure their work to meet rigorous field-wide academic standards and maximize publication chances.

A comprehensive guide for developing professional, self-contained, and APA 7th edition compliant tables and figures for academic manuscripts. It provides critical principles for preparing exhibits for masked review, ensuring accessibility, proper statistical reporting (e.g., SEs, CIs, random effects), and adherence to journal standards.

A comprehensive guide for building and strengthening the conceptual or theoretical framework for academic manuscripts, particularly for submission to top-tier journals like AERJ. It teaches researchers how to ensure that theory drives the entire study—from question generation and design choices to data analysis and interpretation—transforming findings from mere reports into genuine theoretical contributions.

This comprehensive guide helps researchers position their academic paper for AISTATS, ensuring maximum novelty and eligibility. It provides detailed advice on comparative analysis, teaching authors how to contrast statistical methods against both modern ML literature (e.g., NeurIPS, ICML) and established statistical theory (e.g., JASA, Annals of Statistics). It covers proper citation of concurrent work, understanding archival status, and structuring the 'Related Work' section to highlight unique technical contributions.