Skill UI

This skill is a comprehensive fit and re-framing tool for quantitative macroeconomists targeting top-tier dynamic economics journals like the Review of Economic Dynamics (RED). It provides a detailed checklist covering structural model requirements, data discipline (calibration/estimation), computational rigor, and adherence to specific house styles, helping authors assess their manuscript's suitability before submission.

A comprehensive guide for authors submitting manuscripts to the RAND Journal of Economics (RJE). It details how to build a robust, reproducible replication package, focusing on creating a master script, pinning the computational environment, and properly documenting restricted datasets. It specifically addresses RJE's unique policy, which does not mandate formal data or code deposits, while ensuring academic rigor is maintained for reviewers.

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.

A comprehensive guide on methodological rigor for submitting manuscripts to Social Forces. It covers designing causal inference (panel/DID/IV/RDD), demographic analysis, comparative-historical case selection, and network/computational methods. The focus is on strengthening the design by rigorously defending assumptions and ruling out alternative explanations, ensuring methodological soundness beyond mere association.

This skill provides comprehensive guidance for authors submitting highly rigorous theoretical computer science manuscripts to top-tier venues like SIAM Journal on Computing (SICOMP). It covers required standards for algorithmic design, computational complexity, formal proofs, house style, and the high bar for theoretical contribution necessary for foundational academic publishing.

A comprehensive guide for authors considering submission to SIAM Review. This skill evaluates whether a manuscript fits the journal's expository, broad-readership mandate for applied and computational mathematics. It outlines requirements for different sections (e.g., Survey, Research Spotlights), covering accessibility, technical rigor, structure, and official submission checklists.

This guide provides a comprehensive framework for authors responding to peer reviews in major NLP/IR conferences. It covers strategies for active rebuttal periods, pre-emptive revisions when no window exists, and planning resubmissions after rejection. Key advice includes technical rebuttal tactics (e.g., addressing baselines, significance testing), maintaining professional tone, and efficiently using existing computational evidence to maximize acceptance chances.

A comprehensive guide for authors submitting to SODA (ACM-SIAM Symposium on Discrete Algorithms). It clarifies the rules for including computational evidence, distinguishing between proofs relying on computation, supporting implementations, and full experimental contributions. It emphasizes using verifiable certificates and structuring the submission for rigorous, independent review.

This skill provides a comprehensive guide for manuscript authors targeting the StarSem Conference on Computational Semantics (*SEM). It helps authors determine the conference fit, refine framing, establish the necessary evidence bar, and strategize submissions for topics related to meaning representation, semantic parsing, and inference. It is a specialized venue-selection and re-framing tool for advanced NLP/CS papers.

This guide clarifies the role of computation in theoretical computer science submissions, particularly for venues that prioritize theorems over empirical data. It distinguishes between legitimate forms of evidence—such as certified proof steps, constructive object discoveries, and illustrative examples—and inappropriate performance benchmarking. It helps researchers structure their work to align with theorem-based research, ensuring computational elements enhance the core theoretical contribution.

Guidelines for ensuring academic proofs are completely verifiable by third parties, especially when theorems are distributed across multiple documents (e.g., extended abstract and full version). It emphasizes single-source builds, preventing statement drift, and detailing computational determinism requirements for maximum rigor in theoretical computer science research.

A comprehensive guide for ensuring the scientific reproducibility of research papers that rely on dynamic web data, such as web crawls, platform APIs, or live system traffic. It defines three reproducibility regimes (Frozen, Decaying, Unreplayable) and provides detailed instructions on documenting data decay, temporal splits, environment configurations, and computational seeds to maintain the rigor of published findings.