Skill UI

This skill guides authors in evaluating whether their chemical engineering manuscript meets the high standards of the AIChE Journal. It helps authors re-frame application-specific work into generalizable principles, focusing on fundamental scientific advances, rigorous modeling, and comprehensive experimental validation, ensuring the contribution transcends a single process.

This guide details the required process for authors responding to ASE (Automated Software Engineering) reviews. It distinguishes between the initial rebuttal and the formal, criteria-bound summary-of-changes required during a revision round. Key principles include maintaining double-anonymity, systematically addressing every reviewer criterion, and focusing on decision-critical points.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

A detailed guide for authors writing the Related Work section of an Automated Software Engineering (ASE) paper. It teaches the crucial 'delta argument' methodology, emphasizing that positioning must be against prior automated tools, covering multiple SE literature lanes, and conducting fair, head-to-head empirical comparisons to clearly delineate the novelty of the proposed work.

A comprehensive guide for researchers submitting to ASE (Automated Software Engineering). It details mandatory requirements for ensuring research reproducibility, covering the Data Availability Statement, providing anonymized and runnable tools, pinning system provenance (commits, versions, SHAs), and managing cached LLM outputs. Essential for passing rigorous peer review.

A comprehensive guide detailing the complete academic review pipeline for IEEE/ACM Automated Software Engineering (ASE) submissions. It explains unique stages like the early-rejection gate, the criteria-bound revision process, and the difference between ASE's cycle and those of peer conferences (e.g., ICSE, FSE). Essential reading for authors aiming to submit high-quality research in automatic software engineering.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

A comprehensive, step-by-step guide for researchers planning and executing a submission to the ASE (Automated Software Engineering) conference. Covers the entire academic lifecycle, from initial topic selection and evidence gathering (reproducibility) to abstract registration, double-anonymous submission, navigating the early-rejection gate, rebuttal, criteria-bound revision, and final camera-ready artifact submission.

Provides comprehensive guidelines for structuring and writing academic papers for Automated Software Engineering (ASE) conferences. It teaches authors how to present an automated task, emphasize the automation design over the deep learning model, provide evidence from real subjects, and adhere to strict formatting and argument presentation rules to maximize acceptance chances.

A comprehensive guide for authors targeting high-impact chemical engineering venues. It helps authors re-frame their work from simple material synthesis to demonstrating an applied advance with clear mechanistic insight. Focus areas include catalysis, separation, functional materials, and performance benchmarking under realistic process conditions.

This skill provides deep intelligence by systematically scraping and analyzing a competitor's paid advertising campaigns across platforms like Meta and Google. It goes beyond simple ad collection, performing structured analysis on creative patterns, common hooks (e.g., Fear/Loss, Outcome), CTAs, and reverse-engineering their landing page funnels to reveal their core positioning and potential vulnerabilities. Ideal for product marketing, growth strategy, and campaign optimization.

This detailed guide helps authors assess the scientific rigor and scope fit for manuscripts targeting Composites Part B: Engineering. It covers advanced domains like composite mechanics, manufacturing processes, multiscale modeling, and multifunctional materials. It emphasizes that a strong contribution must demonstrate a clear, mechanism-driven advance from material science to structural application, supported by rigorous, quantitative experimental evidence and model validation.