Skill UI

This skill provides a comprehensive fit assessment and checklist for clinical research targeting The American Journal of Gastroenterology. It guides authors on required evidence levels, reporting standards (e.g., CONSORT, STROBE), structuring the manuscript, and ensuring the study has clear, practice-relevant clinical implications for GI/Hepatology practitioners. It serves as a crucial pre-submission refinement aid.

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.

A guide for writing theoretical computer science papers, detailing how to structure proofs and correctly incorporate computational evidence. It emphasizes matching arguments (e.g., upper/lower bounds) to claim shapes and ensures that computation (like SMT verification or exhaustive checks) is rigorous, reproducible, and serves only to certify a step, not replace the main theorem.

A comprehensive guide for authors targeting the IEEE International Requirements Engineering Conference (RE). This skill helps assess if a computer science manuscript is a strong fit for the venue, focusing on requirements elicitation, modeling, traceability, and stakeholder-centered validation. It provides guidance on re-framing submissions and structuring evidence for academic conference acceptance.

This skill provides a comprehensive guide for authors determining the fit of their empirical software engineering (SE) manuscript for the International Symposium on Empirical Software Engineering and Measurement (ESEM). It helps authors rigorously check the study design, evidence bar, proper framing, and necessary re-framing required to transform a journal or arXiv-style paper into a high-quality conference submission suitable for ESEM reviewers. It focuses on measurement validity, experimental rigor, and evidence synthesis.

This guide provides a comprehensive checklist and style manual for authors submitting papers to the Journal of Computer-Aided Design & Computer Graphics (JCAD&CG). It covers all necessary formatting details, including the four elements of a reportative abstract, the problem-gap-contribution structure, adherence to GB/T 7714 citation standards, and specific rules for graphical evidence presentation in CAD and graphics research.

This skill helps authors assess the suitability of their translational or mechanistic manuscript for the Journal of Clinical Investigation (JCI). It provides detailed guidance on meeting JCI's high standards, emphasizing primary evidence from human clinical samples, clear mechanistic insight into disease, and appropriate framing. Use this tool when targeting JCI or when needing to select the most appropriate high-impact scientific venue.

This skill provides a comprehensive framework for evaluating manuscript suitability for Molecular Psychiatry. It guides authors on journal positioning, required scope (e.g., psychiatric genetics, translational neuroscience), necessary evidence depth (dual-level evidence), structural guidelines, and official submission checklists to maximize publication chances.

This skill acts as a comprehensive router for the NeurIPS academic workflow. It guides users through every stage of preparing a machine learning manuscript, from initial topic and track selection to writing, mandatory paper checklist completion, OpenReview submission, handling the double-blind review process, and finalizing the camera-ready version. It helps ensure the manuscript is robust, evidence-backed, and adheres to all conference policies.

This skill provides comprehensive guidance for authors considering submitting a large-scale, monograph-length review to Physics Reports. It covers journal positioning, required scope across all physics subfields, the rigorous evidence bar, house style, and submission checklist. Use this tool to help structure and refine an article intended to become a definitive, self-contained academic reference.

This skill processes raw, report-only optimization observations generated by Caveman tools. It guides the user through an evidence-first, rigorous evaluation process, requiring explicit operator choice, designing a minimal candidate change, and performing a paired baseline evaluation using identical fixed inputs. It ensures that any proposed code change is validated against quality checks and established performance metrics before implementation.

This skill governs the cluster-level resolution of complex defect backlogs. It moves beyond surface fixes by identifying and resolving the true causal root of issues. The methodology mandates rigorous evidence checking, creating read-only mechanism maps, and prioritizing destructive fixes (deletion-driven) to ensure clean, verifiable code state and prevent compatibility debt.