Skill UI

Get a deep, multi-round, and adversarial critical review of your research work, ideas, or drafts. This skill simulates the perspective of a senior ML reviewer (e.g., NeurIPS/ICML level) using dedicated external backends (Codex or Manual). It is designed to find logical gaps, missing experiments, and narrative weaknesses, providing brutal honesty to elevate your academic contribution.

This tool systematically designs and plans ablation studies crucial for advanced research paper submissions. It simulates the role of a rigorous ML reviewer, guiding the process of isolating the specific contributions of each novel component, testing hyperparameter sensitivities, and addressing potential reviewer questions. The workflow ensures that every experiment has a clear hypothesis, maximizing the scientific impact of the research results.

Generates structured peer-review quality critiques of academic papers, covering summaries, methodology scrutiny, contribution evaluation, literature positioning, and recommendations across domains whenever a user requests reviews, analyses, or summaries of research publications.

This skill helps discover new Japanese Natural Language Processing (NLP) resources, including GitHub repositories and Hugging Face models/datasets, that are not yet documented in the awesome-japanese-nlp-resources list. It analyzes a given topic, suggests candidates using web search, and outputs contribution-ready markdown for easy integration.

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.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

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.

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 NLP researchers on strategically positioning their work before writing. It teaches how to determine the core linguistic contribution, select the optimal academic venue (ACL, EMNLP, etc.), manage the scope (long vs. short papers), and align the research focus with specific conference themes and review expectations, maximizing publication impact.

This skill guides authors on positioning their manuscript for the ACM/IEEE International Conference on Human-Robot Interaction (HRI). It helps determine if the work fits the venue, how to re-frame the narrative, and what evidence (user studies, interaction contribution) is expected by HRI reviewers. Ideal for pre-submission strategy and scope calibration in human-robot interaction research.

This guide helps authors assess the suitability of their computer science manuscripts for the ACM International Conference on Multimedia (ACM MM). It provides detailed strategies for framing, highlighting multimodal contributions, structuring evidence, and aligning the work with top-tier academic conference expectations in multimedia retrieval, multimodal learning, and media applications.