Skill UI

This skill provides a comprehensive pre-submission checklist for determining if a computer science research topic is suitable for a major journal, such as the Chinese Journal of Computers (CJC). It guides users on assessing topic coherence, originality increment, manuscript type selection, and differentiating between various specialized journals, ensuring submissions meet high academic standards.

Provides an expert framework for authors submitting to the Conference on Robot Learning (CoRL). It guides writers on how to re-frame general computer science manuscripts into specialized, robust robot-learning narratives. The guide covers defining the contribution type, establishing the evidence bar (from simulation to real-world transfer), and strategically positioning the work against major robotics conferences like ICRA and IROS.

A specialized routing tool designed for academic researchers submitting manuscripts to top-tier Computer Science and AI conferences. It helps users navigate complex decision points, such as selecting the best venue (e.g., ICML vs NeurIPS), matching contribution types (theory, system, empirical study), and adhering to specific submission guidelines and review constraints.

A comprehensive guide for structuring and presenting rigorous, high-impact experimental chapters suitable for top-tier computer science journals. Learn to design experiments driven by Research Questions (RQ), select fair baselines, conduct statistical significance testing, and implement ablation/sensitivity analyses. Ensures evidence is reproducible and perfectly aligns with abstract claims.

This comprehensive guide provides best practices for structuring both 'Related Work' sections and full 'Literature Review' papers for top-tier Computer Science journals. It emphasizes identifying the innovation gap (delta), organizing work by problem dimension, creating clear taxonomies, and adhering to academic citation standards (GB/T 7714).

A comprehensive guide for authors submitting to Computer Science journals, detailing how to achieve full experimental reproducibility. It systematically covers locking environments (dependencies, hardware, seeds), preventing data leakage, structuring the submission package, and creating a clear audit trail so reviewers can independently reproduce all main results from raw data.

This skill provides a comprehensive guide to the academic publication review process for 'Computer Science' journal. It details the single-blind review stages, from initial editorial screening to final acceptance. Users can learn key evaluation criteria, potential outcomes (acceptance, minor/major revision, rejection), expected timelines, and the specific 'leverage points' or actions authors should take at each step to manage expectations and optimize revisions.

This comprehensive guide provides a meticulous audit checklist for authors preparing a computer science manuscript before submitting it to a high-tier academic journal. It covers all required elements, including the 'Manuscript Six Components,' formatting standards (e.g., GB/T 7714), ethical declarations, structural integrity (e.g., report-style abstract), and institutional submission protocols. Use this checklist to significantly reduce the risk of rejection due to procedural or structural deficiencies.

This skill guides researchers on selecting and positioning their academic paper for the journal *Computer Science*. It helps determine if a topic fits the journal's broad, multi-disciplinary scope, identifies the correct column (e.g., AI, Software), and advises on the appropriate paper type (review, research, application). It also provides strategic guidance on comparing it with other specialized journals, making it ideal for pre-drafting planning.

A comprehensive guide for authors submitting manuscripts to a high-level Computer Science journal. It covers all critical aspects of academic formatting, including reportive abstracts (4 elements), proper citation styles (GB/T 7714-2015), figure/table standards, nomenclature, and general academic style, ensuring manuscripts pass initial editorial review.

Provides comprehensive guidelines for creating durable, verifiable evidence objects (artifacts) for theoretical computer science papers. It details best practices for managing proofs, certificates, checker code, and version histories across different publication formats (arXiv, IEEE Xplore) to ensure long-term academic credibility and reproducibility.

This guide provides rigorous advice for researchers on how to appropriately incorporate computation, case analysis, and constructions into top-tier theoretical computer science papers. It distinguishes between proof-bearing computations, honestly reported discoveries (including those found by AI), and mere illustrations, ensuring submissions maintain academic rigor while avoiding the pitfalls of empirical benchmarking.