Skill UI

This guide helps authors polish the prose, abstract, and introduction of quantitative economics manuscripts for a general-interest Econometric Society readership. It emphasizes structuring the paper to clearly present both a substantive economic question and a quantitative method. It adheres to QE house rules, requiring findings to be communicated through point estimates with standard errors and confidence sets, rather than relying on statistical asterisks.

A comprehensive war-gaming guide designed for authors preparing manuscripts for top-tier journals, such as QJE. It teaches how to anticipate hostile referee objections across different academic archetypes (e.g., identification hawks, 'so what?' skeptics) and proactively address them—both fundamentally and for robustness—before submission. This process stress-tests the paper's core claims, structure, and arguments, significantly boosting the chances of passing the initial desk-screen.

This router guides the author through the complex, multi-stage process of submitting a top-tier economics manuscript to the Quarterly Journal of Economics (QJE). It diagnoses the current bottleneck—whether it's conceptual framing, causal identification, robustness checks, or writing style—and directs the user to the appropriate specialized skill stage (e.g., literature positioning, identifying, rebuttal). It does not replace specialized skills but ensures optimal workflow sequencing.

This comprehensive guide details advanced econometric methodologies required for rigorous causal inference studies. It covers how to defend both the causal identification strategy (DiD, RD, IV) and the quality of variables (handling measurement error, index construction). Learn to meet the stringent standards of top-tier journals by applying techniques like staggered DiD estimators, density testing, and attenuation correction.

This comprehensive guide helps authors determine if their robotics manuscript is suitable for the RoboCup Symposium. It provides specialized advice on conference positioning, required evidence (e.g., league performance, benchmarks), and framing for competitive robotics domains like robot soccer, rescue, and service robots. It emphasizes grounding the contribution in empirical, competition-based evidence rather than general theoretical work, serving as a fit and re-framing tool for high-level CI/AI submissions.

This comprehensive guide helps authors determine the suitability and optimal presentation of their robotics scientific manuscripts for the Robotics: Science and Systems (RSS) conference. It provides strategic advice on conference positioning, defining the evidence bar, re-framing algorithmic or theoretical contributions, and addressing the specific needs of top-tier embodied systems reviewers.

This router guides authors through the complex, multi-stage process of preparing a manuscript for a top-tier finance journal like the Review of Finance (RoF). It diagnoses current bottlenecks—whether related to contribution framing, data analysis, or rebuttal strategy—and systematically recommends the next specialized skill to ensure a complete and high-quality submission.

A comprehensive guide for packaging robotic research artifacts (code, policies, ledgers, hardware specs) for peer review and public release. It details the necessary steps to ensure maximum reproducibility, auditability, and permanence, covering both anonymous review-time evidence and citable public repositories.

A rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

A comprehensive guide for positioning research papers in competitive robotics conferences. It teaches authors how to move beyond simple literature review by establishing clear, verifiable contrasts with existing work across multiple academic lanes (e.g., RSS, ICRA, CoRL, Journals). This methodology ensures the paper's novelty is rigorously defensible.

A comprehensive guide for ensuring robust scientific reproducibility in robotics research (RSS). It structures evidence into distinct 'Replication Tiers' (e.g., Rerunnable, Auditable), distinguishing between the computational half (code, seeds) and the physical half (robot, environment, human protocol). It mandates the use of platform ledgers and raw trial logs to convert claims of trust into verifiable, auditable evidence.

Explains the RSS (Robotics: Science and Systems) double-blind review pipeline, two-stage threshold rebuttal, Area Chair meeting decisions, and author strategies for submissions and responses.