Skill UI

This comprehensive guide helps authors determine if their microbial ecology manuscript is suitable for The ISME Journal. It details the journal's focus on ecological and evolutionary mechanisms, outlining expectations for multi-omics integration, rigorous experimental design, and high-level evidence. Use this skill for venue selection, manuscript re-framing, and understanding the required standards for publication.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.

A comprehensive audit checklist for designing and evaluating security experiments, adhering to the highest academic standards (e.g., USENIX Security). It guides users through verifying adaptive attacker models, calculating precision at realistic base rates, ensuring statistical honesty for stochastic results, and maintaining ethical compliance when working with live systems.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

A comprehensive guide for designing and presenting highly rigorous technical evaluations for database or system papers. It covers establishing baseline necessity, proving mechanism causality through ablation, testing scalability across data sizes, and mandating the disclosure of loss cases to satisfy skeptical academic reviewers.

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

This comprehensive guide is designed for authors submitting manuscripts in water science or engineering. It details the high standards, scope, and expected rigor for the flagship journal, Water Research. It covers the necessary depth of mechanistic or methodological advances, strict experimental design requirements (including mass balances and controls), and the IMRaD structure required for publication.

This guide helps scholars rigorously defend the methodological design of academic papers, particularly in International Relations and Comparative Politics. It covers advanced techniques such as process tracing, case selection logic, quantitative cross-national panel data analysis (DID, IV, RDD), and experimental design. Its core purpose is to strengthen causal claims by explicitly addressing selection bias, defining scope conditions, and ruling out major rival explanations.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

This tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.

A skill for designing evidence and validation strategies for academic manuscripts submitted to the Journal of Systems Engineering. It covers standards for numerical experiments, simulations, real data, case studies, and empirical research, ensuring claims are supported by appropriate evidence rather than generic robustness checks.