Skill UI

This tool acts as an orchestrator for academic manuscripts intended for the American Educational Research Journal (AERJ). It guides users through the entire writing lifecycle, determining the correct next step based on the current development stage and the paper's dominant education-research lens (e.g., policy, teaching, development). It is designed to ensure the contribution is pitched to the whole field, not just a subfield.

This skill guides authors on how to position their manuscript within the complex, multidisciplinary literature of Agricultural Systems (AgSy). It ensures that the paper advances the conversation by engaging both the domain knowledge and the core modeling/systems methodologies. The process focuses on identifying unresolved systemic trade-offs, pinpointing the exact research gap, and clearly articulating the unique contribution among established alternatives.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

This guide helps position academic manuscripts for a broad, four-field anthropological audience (sociocultural, archaeology, biological, linguistic). It teaches how to move beyond simple literature reviews by engaging the live scholarly conversation, challenging the dominant canon, and connecting disparate subfields to demonstrate a clear, theoretical contribution.

This guide provides a rigorous, step-by-step framework for developing robust academic theories, particularly for prestigious journals like the Academy of Management Review (AMR). It shifts the focus from simple assertions to a structured argument by requiring explicit definition of constructs (including domain and scope), stating underlying assumptions, specifying relationship forms (e.g., mediating, causal), articulating the mechanism (the 'why'), and formulating falsifiable, numbered propositions. Mastering this process ensures the theory is comprehensive and academically sound.

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

This detailed guide outlines the structure and scientific rigor required for publishing high-quality papers in the domain of Computer Aided Verification (CAV). It instructs authors to transition their work from mere system demonstrations or benchmark reports to formally guaranteed, mathematically precise contributions, emphasizing clear scope definition, stated theorems, and proof ideas.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.

A comprehensive guide for achieving experimental reproducibility and verifiability in academic writing, specifically targeting top-tier journals. It covers crucial aspects like fixing computing environments, managing random seeds, ensuring data provenance, preventing data leakage, and structuring scripts for external review, transforming research rigor into actionable steps.

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.

This skill guides authors in assessing the suitability and optimal framing of their machine learning research in the health domain for top-tier academic conferences like CHIL. It ensures the manuscript meets the specific evidence standards, emphasizing clinical grounding, fairness, robustness, and deployment constraints, transforming general CS narratives into specialized health ML contributions.