Skill UI

This skill provides a comprehensive fit and structural review tool for manuscripts targeting 'Field Crops Research.' It assesses whether an agronomy or crop-physiology paper meets the journal's high standard: rigorous, adequately replicated, multi-environment field experimentation. It guides authors on key topics like resource-use efficiency, crop modeling validation, and required methodological depth (e.g., mixed models, full metadata reporting).

A comprehensive guide for executing and reporting robust statistical analyses for manuscripts in Global Change Biology (GCB). This skill ensures the analysis meets the high standards of reproducibility required for publicly archived data (DOI). It covers advanced methods including mixed/hierarchical modeling, time-series and spatial analysis, meta-analysis, and rigorous uncertainty quantification (parameter, structural, and scenario). It emphasizes statistical rigor over result fabrication, guiding the use of appropriate inferential machinery based on data structure.

A comprehensive academic consulting tool designed for authors in management and organizational behavior. It helps researchers assess the suitability of their manuscript for the Journal of Management (JOM). The skill evaluates topic scope, theoretical contribution, methodological rigor (e.g., meta-analysis, SEM), and adherence to top academic publishing standards. It also provides advice on potential rejection risks and suggests alternative top-tier venues.

A comprehensive guide for analyzing multi-study packages for high-level academic journals. This skill focuses on executing internal meta-analyses, reporting standardized effect sizes with confidence intervals (CIs), establishing honest robustness checks, and reconciling confirmatory versus exploratory findings, meeting demanding journal standards.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.

A comprehensive pre-submission audit guide for authors targeting top-tier academic conferences. This skill helps ensure strict adherence to technical standards, including ACM formatting (acmsmall) and page limits. It is crucial for verifying double-blind anonymity by detecting identity leaks (e.g., GitHub names, metadata) and confirming compliance with policies like dual-submission rules, thereby preventing summary rejection.

This skill details the rigorous methodology for defining study eligibility criteria and coding data for comprehensive meta-analyses. It emphasizes pre-specifying inclusion/exclusion rules in a protocol, developing a detailed codebook, performing double-coding, and calculating inter-rater reliability (e.g., Kappa, ICC) to ensure the reproducibility and trustworthiness of synthesis findings.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.

This skill guides authors in assembling a comprehensive transparency package for academic submissions (especially meta-analyses). It ensures adherence to Open Science principles by detailing requirements for protocol preregistration (PROSPERO/OSF), depositing the coded database, codebook, and analysis scripts. It is essential for proving the full reproducibility of quantitative research findings.

This skill guides the creation of standard, high-quality exhibits for meta-analysis manuscripts, specifically adhering to APA 7th edition standards required by journals like Psychological Bulletin. It generates critical figures such as PRISMA flow diagrams, forest plots, funnel plots, and summary tables, ensuring the final report is self-contained, reproducible, and scientifically rigorous.

This skill guides researchers in transforming raw meta-analysis results (e.g., pooled effects, moderator analyses) into a genuine, high-impact theoretical contribution suitable for integrative review journals. It teaches how to interpret conflicting findings, build or refine theoretical models, clearly state scope conditions (boundary conditions), and set concrete future research agendas, ensuring the findings advance the field beyond mere statistics.