Skill UI

A powerful tool designed for systematic literature reviews and meta-analyses. It searches scientific papers and retrieves highly structured experimental data directly from the full text, going beyond mere abstracts. It extracts over 25 fields, including detailed methods, quantitative results, sample sizes, quality scores, and conclusions, enabling comprehensive evidence synthesis for academic research.

DeepTools is a comprehensive Python command-line toolkit for processing and analyzing high-throughput sequencing data. It supports quality control, normalization, sample comparison, and generating publication-quality visualizations for ChIP-seq, RNA-seq, and ATAC-seq experiments. Key features include converting BAM to bigWig, assessing sequencing depth, creating heatmaps around genomic features, and performing correlation analysis. Ideal for bioinformatics workflows requiring robust genomic data processing and visualization.

Evaluate scientific claims, experimental design, and evidence quality using GRADE and Cochrane risk-of-bias frameworks. Identifies biases, confounders, statistical pitfalls, and logical fallacies, and guides constructive critical analysis of research papers.

Provides guided statistical analysis for research data, covering test selection, assumption checking, effect sizes, and APA reporting. Supports frequentist and Bayesian methods including t-tests, ANOVA, regression, and correlation. Ideal for comparing groups, testing hypotheses, and analyzing experimental or survey data to produce robust, reviewer-ready results.

Plan experiments before data collection: choose designs, randomize, block, and generate reproducible DOE layouts to avoid confounding and pseudoreplication.

A comprehensive guide for authors aiming to rigorously prove the validity and robustness of their economic findings for top-tier journals. It covers three major pillars: pure theory (identifying load-bearing assumptions), structural econometrics (mapping parameters to data features), and experimental design (defining the estimand). Learn how to move beyond 'convergence' and establish true identification.

This guide provides a rigorous framework for strengthening economic manuscripts submitted to top journals, such as AEJ: Micro. It details how to systematically test the robustness of main results through theoretical extensions (e.g., relaxing assumptions, perturbations) and applied/experimental methods (e.g., sensitivity analyses, placebo tests). Learn how to identify extensions that truly 'earn their place' and significantly enhance the overall academic contribution of your work.

A comprehensive guide for reporting rigorous educational and social science analyses, adhering to high standards of warrant and transparency (AERA). It covers advanced quantitative methods (HLM, IRT, quasi-experimental), qualitative coding, and mixed-methods integration, ensuring results are reproducible and evidence-based for publication.

This skill provides a rigorous framework for defending the methodology of advanced educational research manuscripts, suitable for top-tier academic journals. It guides users through defining research structure across quantitative (e.g., multilevel, quasi-experimental), qualitative (e.g., ethnography, case study), and mixed methods paradigms. Focus areas include addressing rival explanations, ensuring measurement validity, establishing causal claims, and meeting stringent reporting standards (AERA).

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.

This guide provides comprehensive methodological frameworks for designing rigorous empirical studies suitable for top-tier academic journals. It helps researchers match theoretical claims (e.g., causal effects, cross-level dynamics) to appropriate designs (experiments, panel data, qualitative analysis). It details how to proactively address major threats like Common-Method Bias, Endogeneity, and inconsistent levels of analysis, ensuring high internal and external validity.

A comprehensive guide for conducting rigorous analysis for Accounting, Organizations, and Society (AOS) manuscripts. It covers both qualitative methods—such as coding, building evidence chains, and weighing counter-evidence—and quantitative techniques, including experimental, survey, and archival model estimation. The focus is on maintaining a fully reproducible audit trail and reconciling mixed-methods findings.