Skill UI

This skill implements an autonomous, multi-round research review loop, simulating the rigorous peer-review process of top-tier conferences. It iteratively reviews, identifies weaknesses, suggests fixes, and re-reviews a research document using the MiniMax API. The system manages state persistence across multiple rounds, ensuring continuous improvement until a 'ready' assessment is achieved or the maximum round limit is reached. Ideal for academic writing, thesis refinement, or comprehensive project documentation.

A 12-agent pipeline for writing academic papers across all disciplines. Supports 11 modes including full drafting, outlining, revision coaching, literature review, citation checks, AI disclosure, and conference rebuttal audits, with LaTeX, DOCX, and PDF output.

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.

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 helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

This guide provides comprehensive instructions for packaging multiagent system (MARL) and game theory evidence for academic submissions, particularly for conferences like AAMAS. It emphasizes that the artifact must be inspectable, containing not just a trained model, but the game definition, environment code, opponent sets, and interaction protocols, ensuring rigorous reproducibility and reviewability.

This comprehensive guide provides a detailed audit checklist for authors submitting to the AAMAS conference. It covers verifying the correct conference track, maintaining double-blind anonymity, adhering to strict formatting rules (e.g., page limits, supplementary zip caps), and identifying potential submission-blocking risks to ensure readiness before the deadline.

Provides comprehensive guidelines for authors preparing supplementary materials for conference submissions (AAMAS). It covers structuring full proofs, extended game definitions, extra experiments, and anonymized code assets, ensuring the main paper remains comprehensible while maximizing the impact of supporting evidence.

A comprehensive guide designed to help researchers determine the optimal conference or journal venue (e.g., AAMAS, NeurIPS, ICML, EC) for multi-agent AI research. It emphasizes that the contribution must center on the interaction result—such as mechanism design, coordination, or negotiation—rather than simply applying a single-agent method in a multiagent testbed. This tool sharpens the research framing before writing begins.

A comprehensive guide for researchers designing and auditing NLP experiments for top-tier conferences. It covers establishing defensible baselines, ensuring evaluation breadth, performing rigorous statistical tests (significance, variance), controlling for data contamination, and structuring detailed functional error analysis. Essential reading for publishing reliable AI research.

A comprehensive guide to modeling the entire academic review cycle for major NLP conferences like ACL. It details the stages from initial submission (ARR cycle intake) through peer review, author response, meta-review synthesis, commitment decision, and final venue decision (Main Conference, Findings, or Reject). It also outlines the roles of reviewers, area chairs, and program chairs, providing strategic advice for authors on navigating the complex publication landscape.