Skill UI

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

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.

Provides comprehensive guidelines for statistical analysis and reporting in biological manuscripts, particularly in cancer research. It teaches researchers how to correctly define 'n' (distinguishing biological from technical replicates), select appropriate statistical tests (ANOVA, survival analysis, etc.), and manage multiple comparisons, ensuring high scientific rigor and reproducibility in published data.

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.

A comprehensive guide for developing scientifically rigorous, APA-formatted tables and figures for quantitative communication research. This skill emphasizes that exhibits must be self-contained, displaying effect sizes and uncertainty (not just significance stars), and following strict reporting standards for maximum clarity for reviewers.

This resource provides a comprehensive fit-check and framing guide for submitting research to Critical Care Medicine, the flagship journal of SCCM. It outlines the high academic and clinical bar required for studies on the critically ill, covering necessary focus on clinically important advances, adherence to specific reporting guidelines (e.g., CONSORT, STROBE), and rigorous methodological expectations for ICU-related research.

A comprehensive guide for analyzing and reporting longitudinal data in Developmental Psychology manuscripts, ensuring compliance with high academic standards. It emphasizes modeling developmental change (e.g., growth curves, multilevel models, SEM), establishing measurement invariance (configural, metric, scalar) before interpretation, and rigorously reporting effect sizes and confidence intervals (CIs) using JARS guidelines. It guides the distinction between confirmatory and exploratory findings.

A comprehensive guide for researchers in Developmental Psychology on meeting rigorous Open Science standards. It details best practices for data and materials sharing using Persistent Identifiers (DOIs), mandatory preregistration of hypotheses, sample-size justification, and adhering to ethical requirements when working with minors and vulnerable populations. It ensures compliance and reproducibility in modern scientific reporting.

Provides comprehensive guidelines for authors preparing tables and figures for high-impact economics journals. The core principle is ensuring that every exhibit is self-contained, legible, and communicates the economic magnitude and meaning to a generalist audience, even without reading the surrounding text. It covers best practices for reporting elasticities, confidence bands, and structuring arguments.

A comprehensive guide for authors preparing manuscripts for top economics journals (e.g., EER). It details best practices for designing self-contained tables and figures, emphasizing the correct reporting of standard errors (SEs), confidence intervals (CIs), and interpreting results cleanly. This guide ensures exhibits adhere to rigorous academic publishing standards, moving beyond simple star counts to let the magnitude of the result speak for itself.

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

This guide provides comprehensive guidelines for advanced statistical and qualitative data analysis in Human Resource Management (HRM) research. It details the rigorous application of methods like Multilevel Modeling (HLM), Structural Equation Modeling (SEM), mediation testing, and fixed-effects models. It emphasizes methodological rigor, transparency, addressing endogeneity, and reporting effect sizes to ensure publication-quality empirical findings.