Skill UI

A comprehensive guide for researchers to determine if their work represents a genuine cross-modal or media-systems contribution suitable for ACM Multimedia (ACM MM). It helps route projects by testing for cross-modality, media system advances, or human-centric results, distinguishing between pure CV, NLP, or system-heavy claims.

A comprehensive guide for revising technical papers submitted to ACM Multimedia (ACM MM). It teaches authors how to shift focus from single-modality benchmarks to cross-modal fusion mechanisms, ensuring all media elements (figures, audio, video) function as direct evidence. The guide emphasizes structuring the argument within a tight 6-8 page conference limit, making the multimedia contribution visible from page one.

A three-tier model team orchestrator that decomposes oversized tasks into parallel subtasks, dispatches cheap workers in batches, gets plan review from a stronger advisor model, verifies each result, and synthesizes the final deliverable.

This skill evaluates and assembles the smallest, most efficient set of AI agent skills required to achieve a complex, natural language goal. It dynamically derives multi-step workflows, searching local indices, registries, and external sources using four specialized lenses (need, operation, outcome, method). It prioritizes user-friendly plain-language outputs while rigorously verifying compatibility, safety, and overlap between candidate skills before recommending the final stack.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

This comprehensive guide helps computer vision researchers strategically position their work for the Asian Conference on Computer Vision (ACCV). It covers how to evaluate manuscript fit, restructure the narrative from general to conference-specific, optimize evidence presentation (including ablations and failure modes), and understand ACCV's regional niche compared to global flagships like CVPR or ECCV.

A comprehensive guide for authors aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

A comprehensive guide for authors targeting the British Machine Vision Conference (BMVC). This skill helps assess whether a computer vision manuscript fits the venue, providing expert advice on framing, necessary evidence gaps, structural improvements, and differentiating the work from related CV conferences (e.g., CVPR, ICCV). It ensures the contribution is clear and highly specialized for specialist reviewers.

This comprehensive guide outlines best practices for achieving rigorous reproducibility in complex AI and knowledge graph research. It details how to pin every component—from tokenizers and KGs to LLM prompts and environment settings—ensuring that results are fully documented and verifiable by peers, even when proprietary data is involved.

A comprehensive guide detailing the mandatory submission requirements for major AI conferences like CIKM. It covers critical areas such as strict page budget rules (including appendices), mandatory GenAI Usage Disclosure, EasyChair nomination processes, maintaining double-blind anonymity, and adhering to crucial deadlines like the abstract-gate week to prevent desk rejection.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.