Skill UI

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

This skill guides researchers on how to transform complex agricultural systems model results into actionable, honest, and scoped implications for decision-makers. It teaches authors to move beyond merely restating results by identifying the specific actors, articulating the necessary trade-offs, and clearly defining the system boundaries and uncertainty limits, ensuring the paper informs a decision rather than just demonstrating methods.

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.

This guide helps researchers rigorously evaluate if their project meets the criteria for a 'systems analysis' in high-impact journals. It teaches how to shift the research focus from single-factor field trials to complex systems questions that analyze interactions, trade-offs, and emergent behaviors across multiple hierarchical levels, such as the farm to the food system. Essential for properly framing scope and article type before submission.

An essential orchestrator for authors submitting manuscripts to Agricultural Systems. This tool guides users through the entire academic writing lifecycle, helping them determine the appropriate article type (e.g., Review, Perspective) and ensuring the core research question addresses true systems complexity, interactions, and trade-offs, rather than single-factor trials. It maps the manuscript stage to the necessary sub-skills.

A comprehensive guide designed to help researchers select, articulate, and defend the appropriate interpretive lens and scale for historical arguments. Whether focusing on social, cultural, global, or microhistory, this skill assists in structuring a robust methodology, ensuring that theory is always grounded in primary evidence, and preventing common academic pitfalls like anachronism or over-generalization.

This skill provides architectural guidance for writing high-level academic history articles, specifically tailored for the American Historical Review (AHR). It helps writers ensure that the narrative development and the core historical argument are seamlessly interwoven, adhering to strict length and scholarly standards. Use it when outlining complex drafts or restructuring material that feels disjointed.

This skill acts as the primary orchestrator for any American Historical Review (AHR) manuscript. It guides users through the entire academic writing lifecycle, determining the next necessary step based on the project stage. From developing a significant historical question and gathering primary sources to structuring the argument, refining prose, and managing citations, it ensures the focus remains on deep historiographical evidence and interpretation.

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.

This guide details the critical steps required after conference acceptance to prepare a paper for the final, public 'camera-ready' submission (e.g., AISTATS, PMLR). It covers transforming an anonymous manuscript into the final two-column format, restoring author metadata, handling public artifacts (code, data), resolving formatting issues, and confirming all presentation and copyright obligations.

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

This guide helps researchers assess the optimal venue for their work, determining if their project aligns best with AISTATS, or if it is better suited for major ML conferences (NeurIPS, ICML, ICLR), UAI, or specific statistics journals. It emphasizes the need for strong statistical foundations, inference guarantees, and the unique intersection of AI, ML, and statistics before starting the writing process.