Skill UI

A comprehensive guide for designing and presenting rigorous experiments and simulations for top-tier journals in Control Theory, Robotics, and Smart Systems. Covers RQ-driven design, establishing fair baselines, providing theoretical proofs of stability/convergence, performing ablation studies, and ensuring statistical significance, helping authors build an undeniable evidence chain for double-blind external review.

This central routing skill guides authors through the complex process of journal selection for engineering and technology manuscripts. It does not replace a single journal submission but first classifies the content based on the engineering sub-discipline, contribution type (e.g., method, system, device), validation shape (e.g., simulation, field experiment), and intended audience breadth. It then routes the paper to the most specialized and appropriate journal skill for accurate re-framing and submission.

This comprehensive routing skill guides researchers in selecting the optimal journal venue for natural-science, clinical, or physical manuscripts. It classifies the work based on discipline, advance type (e.g., mechanism, discovery), evidence shape (e.g., clinical trial, simulation), and audience breadth. It helps optimize the manuscript's fit and submission chances across hundreds of leading scientific journals.

This guide establishes standards for reporting reproducibility in robotics research, addressing the difference between traditional ML and physical systems. It mandates that every claim must be classified as either 'rerunnable' (executable code/simulation) or 'auditable' (detailed logs, hardware specs, and protocols). It covers creating a complete evidence map, detailing the hardware specification ledger, explicitly reporting the sim-to-real gap, and writing an honest availability statement, all within the main paper body.

A comprehensive guide for researchers on how to structure, package, and document complex scientific artifacts (e.g., simulation results, specialized code) to ensure reproducibility for academic reviewers. It details setting scope tiers (Reproduce, Regenerate, Inspect) and mitigating technical challenges like build fragility and runtime discrepancies.

A comprehensive guide for ensuring the reproducibility of communications simulations for journal submissions. It details critical steps, such as fixing random seeds, channel models, simulation parameters, software versions, and organizing a complete reproduction package. This ensures that research results can be accurately and credibly verified by external reviewers in high-impact scientific journals.

A crucial guide for authors preparing for PODC (Proofs venue). It corrects the common misconception that runnable artifacts or badges are required. Instead, it redirects focus to the three core areas of evaluation: ensuring proof completeness, establishing model rigor by declaring all assumptions, and maintaining transparency for optional simulations.

A comprehensive guide for computer graphics researchers detailing how to achieve result-level reproducibility, especially for major conferences like SIGGRAPH. It emphasizes pinning all provenance—including assets, random seeds, hardware stacks, and hyperparameters—and methodologies for handling non-determinism in floating-point math and stochastic simulations, ensuring that the reported figures and timings can be regenerated by any reader.

A comprehensive guide for packaging and evaluating research artifacts for academic conferences like SIGMETRICS. It details the requirements for achieving ACM Badges (Functional, Reusable, Reproduced) by ensuring the packaged simulation can regenerate key figures and results on a clean machine, moving beyond mere tool execution.

This methodology allows engineers and analysts to conduct controlled counterfactual simulations when real-world testing (such as catastrophic failures, extreme load, or irreversible changes) is too rare, costly, or impossible. By isolating a single variable and tracing the mechanistic chain step-by-step, it helps surface critical system breakpoints, invariants, and necessary controls before deployment.