Skill UI

This skill defines a framework-agnostic, scalable architecture pattern for React and React Native applications. It mandates feature modules, strict state separation (server vs. UI), and disciplined import rules to create predictable, maintainable, and highly scalable codebases, regardless of the chosen framework (Next.js, Remix, etc.).

Standards for maintainable Flutter/Dart apps: layer separation, feature-based structure, immutable state, modern Dart, widget choices, testing, review checklists, monorepo and localization. Use when planning, writing, reviewing, or refactoring Flutter code.

A comprehensive guide to production-level Laravel architectural patterns. It details best practices for structuring scalable web applications and APIs, covering service layers, dedicated actions, Eloquent ORM modeling, resource design, queue management, and advanced routing techniques like scope bindings. Learn how to maintain clean separation of concerns in large-scale PHP projects.

A comprehensive guide to best practices for building production-ready React Native and Expo applications. Covers core concepts including Expo Router navigation, state management separation, robust data fetching with TanStack Query and Zod, efficient list rendering, styling consistency, and accessing native APIs.

This guide outlines the critical rules for structuring technical research papers submitted to top-tier conferences like ASE. It emphasizes the 'decision-criticality' rule, instructing authors on how to separate core arguments (which must fit within the 10-page body) from reproduction details, raw logs, and supplementary material (the artifact). Mastering this separation is crucial for ensuring reviewers judge the paper on its merits, not its supplementary data.

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.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

A comprehensive guide for authors submitting to FAccT, detailing the mandatory separation of content into three zones: the main Body, the Endmatter statements (including Ethics and AI Usage), and the Appendix/Supplementary material. It outlines strict rules regarding anonymity, page limits, and ensuring the core argument is decision-critical and visible from the main reviewed pages.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

This guide assists researchers in determining the optimal venue for their theoretical machine learning contributions. It provides a framework for evaluating whether a result is best suited for COLT, or if it belongs in other top-tier conferences (e.g., NeurIPS, ICML) or journals (e.g., JMLR). It emphasizes differentiating between rigorous mathematical proofs (bounds, separations) and purely empirical findings.

A detailed guide for authors preparing manuscripts for the IMF Economic Review (IMFER). This checklist ensures compliance with strict academic publishing standards, including double-blind anonymization, proper formatting (Chicago style, JEL codes), and the correct separation of author-identifying information before final submission via Editorial Manager.

This guide helps researchers properly structure graphical and CAD papers submitted to academic journals. It details the separation of core, 'decisive' evidence (main results, key figures) that must reside in the main text, versus 'extensive' supplementary materials (videos, extra data, full derivations). It emphasizes maintaining the self-sufficiency of the main article while ensuring all necessary evidence is presented.