Skill UI

This package ensures full reproducibility for international macroeconomics research. It is designed for projects utilizing complex, multi-source, and often restricted data (such as IMF or central bank files). It mandates capturing the entire data lineage, pinning the execution environment (R, Python, Stata), and documenting access instructions, allowing any authorized replicator to rebuild the analysis from scratch.

Provides a comprehensive framework for generating structured, blameless post-mortem documents after a production outage or service degradation. It guides teams through reconstructing timelines, identifying root causes using methods like 5 Whys, quantifying impact, and generating concrete, actionable preventative measures to improve system resilience.

This guide provides comprehensive best practices for authors to enhance the reproducibility of technical research papers (especially in networking and computer science). It covers documenting proofs, pinning simulation setups, reporting parameters, and best practices for releasing code and datasets, transforming a paper from a claim into a verifiable scientific artifact.

A comprehensive guide for researchers submitting work to INTERSPEECH. It details how to package and manage various artifacts—including code, audio samples, and trained models—through the conference lifecycle. The document emphasizes maintaining anonymity during review, ensuring scientific reproducibility, managing complex data licenses, and adhering to ethical guidelines for voice data and synthetic media release.

A comprehensive guideline for establishing and documenting the reproducibility of complex sensor-systems artifacts. It covers necessary components such as firmware sources, detailed hardware Bills of Materials (BOMs) and board files, raw sensor traces, ground truth data, and calibrated measurements. Use this guide to ensure that scientific claims can be verified by a competent reader, detailing the exact dependencies on physical equipment and software toolchains.

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.

This skill provides a framework to organize and structurally review readiness evidence for major international standards, including ISO 13485 (Medical Devices), ISO 17025 (Testing Labs), and ISO 15189 (Medical Labs). It helps users define the scope, manage controlled documents, establish traceability, and compile preparatory material for substantive human expert review. It is explicitly a preparation tool, not an audit, certification, or legal determination system.

A comprehensive guide detailing the mandatory replication and data policy required by journals like JDE. It outlines how to build a self-contained, reproducible package containing master scripts, documented data provenance, pinned environments, and strategies for handling restricted or proprietary data, ensuring readiness for review and acceptance.

Provides comprehensive guidelines for meeting modern academic publishing standards, particularly those required by educational psychology journals. It outlines mandatory requirements for ensuring research reproducibility, including depositing data, materials, and analysis code in trusted repositories with persistent identifiers (DOIs), adhering to JARS reporting standards, and documenting preregistration status.

A comprehensive framework designed to ensure environmental economics manuscripts meet rigorous data-availability and reproducibility standards, especially for journals like JEEM. This package guides users to document the entire research pipeline, handling complex issues like data versioning, proprietary satellite/admin data, and multi-step spatial operations by establishing a credible end-to-end reproduction path.

Provides comprehensive guidelines for authors submitting empirical, quantitative, or theoretical growth papers. It details how to package data sources, code pipelines, calibration files, and historical/geospatial artifacts to meet stringent reproducibility standards for publishing journals.

This comprehensive guide details how to build a robust, credible, and shareable replication package for a Journal of Economic Geography (JEG) manuscript. It focuses heavily on documenting spatial data provenance, handling version control for boundaries and projections, and scripting complex spatial joins to preempt reproducibility concerns, even though JEG's policy is post-acceptance.