Skill UI

A comprehensive guide to implementing production-grade reliability patterns for Snowflake environments. Covers advanced techniques like Time Travel for point-in-time recovery, cross-region database replication, account failover groups for disaster recovery, and application-level resilience coding (e.g., connection failover logic). Essential for building fault-tolerant, highly available data pipelines.

Plan experiments before data collection: choose designs, randomize, block, and generate reproducible DOE layouts to avoid confounding and pseudoreplication.

This guide outlines the mandatory steps and components required to assemble a robust, reproducible data and code replication package for submissions to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures compliance with the stringent AEA Data and Code Availability Policy, covering data provenance, master scripts, and version control. This package must be built iteratively, not at the acceptance stage.

A comprehensive workflow router designed for authors submitting manuscripts to the American Economic Journal: Applied Economics (AEJ: Applied). This skill guides the user through every stage of the academic writing process—from initial topic selection and literature positioning to causal identification, robustness checks, polishing, data replication package preparation, and final submission/rebuttal. It acts as a navigational guide, ensuring all critical steps required by top-tier economics journals are addressed in the correct sequence.

A comprehensive guide for assembling a fully reproducible research package for macroeconomics papers targeting the American Economic Journal (AEJ: Macro). It ensures compliance with the AEA Data and Code Availability Policy by documenting raw data, simulation/calibration code, estimation routines, and visualization steps, guaranteeing that results can be independently verified by reviewers.

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This package assists researchers in assembling a compliant data and code deposit for AEJ: Economic Policy manuscripts. It ensures adherence to the AEA Data and Code Availability Policy by structuring the openICPSR deposit, generating the required README, and managing restricted data access paths. It is critical for passing the mandated pre-publication reproducibility check.

A specialized guide for responding to a 'conditional accept' decision from AER: Insights. It teaches authors how to draft a concise, decisive revision plan within a tight 8-week window. Focuses on strict adherence to formatting rules, maintaining the original scope, and finalizing the replication package.

This guide outlines the comprehensive package structure required for submitting manuscripts to American Economic Review: Insights (AER: Insights). It ensures authors meet the AEA Data and Code Availability Policy, providing all necessary code, raw data access, and documentation (including a master script and exhibit-to-code map) to allow the data editor to verify the reproducibility of every result before publication. It is crucial for passing the final publication gate.

Provides a detailed guide on the mandatory pre-publication replication and verification process for American Journal of Political Science (AJPS). It instructs authors on building a robust, reproducible package—including code, data, and documentation—that an independent third party can re-run to confirm all numerical findings match the main text before publication.

A comprehensive guide for authors submitting to the American Journal of Sociology (AJS). It outlines best practices for data documentation, ensuring research provenance, and ethically sharing findings in both quantitative and ethnographic contexts. It emphasizes creating a thorough, checkable evidentiary trail appropriate to the claim, while clarifying AJS's non-mandatory policy on replication deposits and adherence to confidentiality standards.

This skill audits academic literature reviews (like Annual Reviews of Psychology) to ensure they maintain scholarly rigor, balance, and objectivity. It checks for uneven coverage across different labs or theories, ensures evidence is weighted by design strength and replication status (not mere vote-counting), and flags potential self-promotion or bias. Use this when scrutinizing a high-stakes scientific review for academic fairness.