Skill UI

A comprehensive coach designed to guide students and researchers through the entire scholarly writing lifecycle. This skill covers structuring complex academic works—including research papers (IMRaD), theses, dissertations, grant proposals, and conference abstracts. It teaches fundamental writing principles, proper source integration, avoiding plagiarism, and navigating diverse citation styles (APA, MLA, Chicago, GB/T 7714), helping users develop an independent, professional academic voice.

This comprehensive guide provides structured frameworks and best practices for all developer advocacy activities. Use it to plan conference talks (including CFP templates), prepare live coding demos with the 10-3-1 rule, and strategize for podcast appearances and building in public. It covers everything from audience context to presentation polish.

A comprehensive guide for structuring and writing high-quality AI research manuscripts intended for major academic conferences (e.g., AAAI). It teaches how to structure the first page for maximum impact, clearly state contributions (method, theory, system, etc.), ensure two-column readability, and address ethical limitations to appeal to a broad, non-specialist committee audience.

A comprehensive guide for authors transitioning an accepted, anonymous conference submission into the final, public camera-ready version for publication. It covers de-anonymization steps, including restoring author details, updating proceedings metadata, reflowing figures for multiagent papers, and integrating rebuttal promises without expanding the core contribution. This ensures adherence to strict publishing standards.

A comprehensive guide for authors on drafting a strategic response during the OpenReview cycle. This resource covers how to address reviewer objections, shape the Area Chair's meta-review summary, manage the discussion phase, and decide whether to defend the paper or withdraw for a future revision cycle. Focus on clarity, conciseness, and evidence-based arguments.

This comprehensive guide outlines the meticulous workflow for preparing a research paper for final publication (Camera-Ready) after acceptance at major NLP conferences like ACL. It covers critical steps including de-anonymization (restoring author details and acknowledgements), integrating mandates from meta-reviews, adding required disclosures (e.g., funding, AI assistance), and ensuring all technical metadata, font embedding, and licensing comply with the permanent open-access standards of the ACL Anthology.

A comprehensive audit checklist for authors submitting papers to major NLP conferences (like ACL) via OpenReview. This guide covers mandatory formatting rules, maintaining anonymity, addressing ethical declarations (Responsible NLP), managing resubmission links, and ensuring adherence to the entire submission workflow to prevent desk rejection.

This guide provides authors with a comprehensive framework for positioning technical manuscripts for the ACM Interactive Surfaces and Spaces (ISS) conference. It covers essential areas like defining scope, aligning hardware evidence with interaction techniques, optimizing the narrative structure, and understanding the specific community expectations for spatial and tangible computing.

A comprehensive guide for drafting professional and strategic rebuttals for major academic conferences, such as ACM Multimedia. Learn how to triage reviewer comments, structure arguments for the Area Chair, address factual errors, and incorporate small, confirmatory results while maintaining anonymity and adhering to strict policy guardrails.

This comprehensive guide details the essential steps required to transition an accepted, anonymous conference paper into the final, public 'camera-ready' version for major academic publications like ACM MM. It covers de-anonymization, completing ACM eRights forms, incorporating CCS concepts, releasing permanent code/data artifacts, and preparing for the final presentation.

A comprehensive guide for authors preparing the supplementary material for multimedia conferences (e.g., ACM MM). It details how to structure the content, separating core arguments (Body) from supporting evidence (Supplement). Learn best practices for packaging videos, audio, and interactive demos, ensuring technical compatibility (H.264/MP4), maintaining anonymity for double-blind review, and creating a legible, reviewer-friendly submission index.

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.