Skill UI

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

Provides LaTeX templates, formatting rules, and submission guidance covering journals (Nature, Science, PLOS, IEEE, ACM), conferences (NeurIPS, ICML, CVPR, CHI, etc.), posters, and grants so researchers can prepare compliant manuscripts efficiently.

Generates a structured, section-by-section paper outline from review conclusions, narrative reports and experiment results. Supports venue-specific page limits (ICLR, NeurIPS, ICML, CVPR, ACL, IEEE) and optional style references for layout guidance.

Draft a LaTeX paper section by section from a paper plan and narrative report. Automatically applies venue-specific styles for ICLR, NeurIPS, ICML, CVPR, ACL, AAAI, ACM and IEEE formats, fetches real BibTeX from DBLP/CrossRef to avoid hallucinated citations, and runs reviewer-backed section reviews.

This sophisticated tool generates publication-quality AI illustrations and diagrams for academic papers. It implements a multi-stage, iterative workflow, using advanced LLMs (like Claude and Gemini) to parse user requests, optimize complex layouts, verify academic styling (e.g., CVPR/NeurIPS standards), and render high-fidelity figures. Ideal for researchers needing professional, complex visuals for their manuscripts.

End-to-end pipeline turning a completed paper into a conference-ready talk: slide outline, Beamer source, editable PPTX, speaker notes, script, visual polish, and claim/citation/anonymity assurance checks for venues like NeurIPS, ICML and ICLR.

Build academic conference posters (ICML/NeurIPS/ICLR/CVPR) as a single HTML/CSS file rendered to print-ready PDF via headless Chromium, with measurement-driven hard gates, real paper figures, provenance manifests, and two-hue design tokens.

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.

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 helps authors audit the novelty and eligibility of multiagent system research for submission to AAMAS. It provides comprehensive advice on positioning the work against literature spanning multiple top-tier venues (e.g., NeurIPS, ICML, AAAI, EC, JAAMAS), integrating concepts from multiagent RL, game theory, and social choice. It ensures submissions are positioned accurately compared to the broader academic community, maximizing impact and minimizing rejection risk.

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.

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.