Skill UI

A comprehensive preflight checklist for authors submitting to the Annals of the American Association of Geographers (AAG). This guide ensures manuscripts meet the strict standards for double-anonymity, word count caps (e.g., 11,000 words), Chicago style formatting, and proper handling of sensitive geographic data before submission via ScholarOne.

This guide teaches researchers how to rigorously assess a topic's geographic significance and determine its optimal fit within the Annals of the American Association of Geographers (AAG). It implements a two-part fit test—ensuring the argument is fundamentally geographic (space/place/scale are load-bearing) and possesses sufficient reach to interest neighboring subfields. It guides authors in choosing the correct area (e.g., Human, Physical, Methods) and appropriate journal venue (Annals vs. specialty). Use this when defining a project scope, overcoming reviewer critiques, or deciding wher...

This tool serves as an orchestrator for manuscripts submitted to the Annals of the AAG. It guides authors through the complex submission process by helping define the appropriate academic area (e.g., Human Geography, Physical Geography), the article format (Article, Commentary), and the sequential developmental stages—from topic selection and theory building to data analysis and final submission. It ensures the work is framed legitimately as *geography*.

This guide assists in polishing and formatting academic manuscripts for high-level geographical journals. It ensures adherence to specific academic standards, such as the Chicago Manual of Style (author-date), managing strict word count caps, and structuring the argument to be legible to a broad, interdisciplinary audience. It focuses on elevating prose from subfield jargon to broadly readable academic discourse.

This skill provides authors with a comprehensive guide to positioning their computer science manuscript for the AAAI/ACM Conference on AI, Ethics, and Society (AIES). It covers critical aspects such as framing the contribution, meeting evidence standards, addressing social impacts, and distinguishing the work from similar venues like FAccT. Use it to refine submissions focused on AI ethics, governance, and socio-technical methods.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

Draft concise AAAI author responses (rebuttals) under strict character limits, no-URL rules, and no-new-results guidance. Prioritize decision-critical factual corrections, handle AI-generated reviews, and optimize limited space to influence SPC/AC discussions.

Guides authors of accepted AAAI papers through camera-ready source submission, covering template compliance, page limits, extra page purchase, deanonymization, copyright, registration, presentation, and artifact release.

A comprehensive guide for positioning AI/ML manuscripts for top-tier general AI conferences like AAAI. It helps authors refine their scope, frame their contribution, provide necessary evidence (e.g., ablations, baselines), and strategically compare their work against related research and similar venues (ICML, IJCAI). Ideal for pre-submission manuscript refinement.

This skill provides comprehensive guidance for authors targeting the AAAI Conference on Human Computation and Crowdsourcing (HCOMP). It helps authors strategically frame, position, and strengthen the evidence of manuscripts focused on crowdsourcing, human-AI collaboration, and collective intelligence, ensuring the work meets the specific academic bar of the venue.

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

Explains AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review handling, author feedback, and stage-by-stage planning for submissions.