Skill UI

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

A comprehensive guide for developing professional, self-contained, and APA 7th edition compliant tables and figures for academic manuscripts. It provides critical principles for preparing exhibits for masked review, ensuring accessibility, proper statistical reporting (e.g., SEs, CIs, random effects), and adherence to journal standards.

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 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.

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.

Provides essential guidelines for designing rigorous, generalizable field experiments in crop research. Covers multi-environment trials (MET), proper randomization, blocking strategies, split-plot designs, and Genotype-by-Environment (G×E) analysis. Use this tool when planning or defending the scientific rigor of an agricultural manuscript.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.

This comprehensive guide helps researchers evaluate whether a clinical or health-services study is appropriate for publication in JAMA Internal Medicine. It details the journal's high bar for methodological rigor, including requirements for randomized trials (CONSORT), observational studies (STROBE), and systematic reviews (PRISMA). It covers everything from study design and statistical reporting to addressing potential desk-reject triggers, ensuring authors submit high-quality, impactful research.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.

This guide provides a comprehensive checklist and systematic approach for enhancing the reproducibility of research in Computer Graphics and CAD. It addresses the inherent sensitivity of graphics and geometry results to environment variables, random seeds, rendering parameters, and data versions, ensuring that published results can be reliably replicated by reviewers and readers.

This guide provides a comprehensive framework for building verifiable reproducibility evidence for academic manuscripts. It instructs authors on critical engineering disciplines, including freezing environment dependencies, controlling random seeds, versioning datasets and models, and creating anonymous, self-contained running materials that stand up to rigorous external peer review.