Skill UI

This guide outlines the rigorous, model-based standards required for data analysis and statistical reporting in Cognitive Psychology manuscripts. It covers essential techniques such as model comparison (using AIC/BIC or Bayes factors), demonstrating parameter and model recovery, implementing mixed or hierarchical Bayesian models, and ensuring all findings are fully reproducible from shared code.

This guide details the rigorous requirements for achieving research reproducibility in academic publishing. It moves beyond simple data sharing, mandating that authors deposit model-fitting code, analysis scripts, and materials with persistent identifiers (DOI). Key steps include ensuring reported results can regenerate in a fresh session, completing declarations of competing interests, and linking pre-registrations, ensuring compliance with strict journal standards.

This tool serves as an orchestrator for authors preparing submissions to Cognitive Psychology (Elsevier). It guides users through the entire research lifecycle—from initial topic selection and theoretical formalization to study design, data analysis, and open science practices. It emphasizes structuring the contribution as a model-driven, multi-experiment program, ensuring all necessary steps, including code transparency, are addressed.

Provides comprehensive guidelines for executing and reporting advanced statistical analyses (e.g., ANOVA, SEM, multilevel models) for academic manuscripts. It mandates adherence to rigorous APA reporting standards, including reporting effect sizes, confidence intervals, and ensuring methodological transparency and reproducibility to withstand expert scientific scrutiny.

This comprehensive guide outlines the strict analytical and reporting standards required for top-tier conservation biology manuscripts. It covers everything from selecting appropriate statistical models (e.g., hierarchical, spatial) to honestly quantifying uncertainty, ensuring full reproducibility, and translating complex findings into actionable, decision-relevant conservation insights. Use this skill when performing main analyses or preparing manuscripts for expert peer review.

This tool acts as an orchestrator for academic manuscripts submitted to Conservation Biology. It guides authors through the entire scientific writing lifecycle, helping them determine the appropriate article type (e.g., Review, Essay, Contributed Paper) and the necessary sequence of sub-skills (from study design to data analysis and relevance checks). It ensures the final work has direct, transferable implications for the conservation of biological diversity.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This skill provides rigorous standards and best practices for running and reporting analyses within Comparative Political Studies (CPS). It addresses common pitfalls in cross-national data, such as measurement error, few clusters, and spurious correlation, ensuring the resulting research is transparent, robust, and replication-ready for academic review.

This guide outlines the rigorous standards required by Comparative Political Studies (CPS) for manuscripts, especially quantitative papers. It details how to prepare a complete reproducibility package, including data, code, log files, and a data availability statement, ensuring that analysis is rigorous and verifiable for final acceptance.

This comprehensive orchestrator guides authors through the entire manuscript lifecycle for Comparative Political Studies (CPS). It is designed to ensure that the paper maintains a genuinely comparative argument—across countries, regions, or time—at every stage, routing the user to the correct next skill (e.g., theory building, research design, data analysis). It does not generate content; it directs the scholarly workflow.

A comprehensive guide for rigorous data analysis execution and reporting in criminology research. This skill covers advanced econometric techniques required for analyzing complex crime data, including count models (Negative Binomial, Zero-Inflated), fixed-effects panel data, and survival/recidivism modeling. It emphasizes transparently reporting uncertainty, addressing selection bias, and ensuring methods are fully reproducible for expert peer review.