Skill UI

This guide provides a rigorous framework for designing and interpreting quantitative case studies in academic manuscripts. It instructs authors on justifying parameter sources (real data, literature, policy), structuring comprehensive experiment matrices (effectiveness, sensitivity, boundaries, context), and moving beyond mere result description to deep mechanism-based analysis.

A comprehensive guide for designing rigorous, multi-experiment cognitive psychology studies. This framework ensures that experiments are engineered to discriminate between competing theoretical models by tightly controlling confounds, optimizing stimulus construction, and providing adequate power for the critical contrast, rather than just the main effect. It guides the entire planning process from hypothesis formulation to pre-data lockdown.

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.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

A comprehensive guide to strengthening the quantitative research design for communication manuscripts. It covers best practices and methodological rigor for experiments (lab/online), panel surveys, and content analysis. Learn how to pre-register designs, control for common confounds (e.g., common-method variance), and rigorously establish causality, helping you defend your work against skeptical reviewers.

This skill serves as the central orchestrator for Communication Research (CR) manuscripts. It guides users through the entire academic publishing lifecycle, assessing whether a manuscript is quantitative, theory-driven, and methodologically sound. It routes the user to the appropriate specialized sub-skill (e.g., design, analysis, theory-building) based on the study type (experiment, survey, content analysis) and the current stage of the research process.

This guide provides a framework for rigorously defending the methodological design of scientific manuscripts in conservation biology. It covers advanced topics including detection modeling (occupancy, capture-recapture), addressing spatial structure and pseudoreplication, quasi-experimental designs (BACI, difference-in-differences), and systematic review protocols (PRISMA). The goal is to ensure that the evidence presented directly and credibly supports transferable, decision-relevant conservation conclusions.

A comprehensive guideline for researchers developing robot-learning papers. It details best practices for experimental design, emphasizing statistical rigor, handling stochasticity (training seeds, initial states), bridging the sim-to-real gap, and ensuring fairness across baseline policy families (BC/RL/VLA). This resource helps ensure claims are supported by credible evidence.

Provides a comprehensive framework for defending the methodological rigor of comparative social science research. This skill guides users through establishing comparative leverage, whether in cross-national panel data, case-based comparisons, or natural experiments. It emphasizes techniques like causal inference, process tracing, and ruling out rival theoretical accounts to strengthen the study's claims.

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

A comprehensive guide for structuring and presenting rigorous, high-impact experimental chapters suitable for top-tier computer science journals. Learn to design experiments driven by Research Questions (RQ), select fair baselines, conduct statistical significance testing, and implement ablation/sensitivity analyses. Ensures evidence is reproducible and perfectly aligns with abstract claims.