Skill UI

A comprehensive guide for researchers submitting to major speech conferences (like INTERSPEECH). It details how to harden the reproducibility of speech results, covering crucial 'decay channels' such as corpus versions, text normalization rules (WER), objective metrics (EER), subjective protocols (MOS), and model stochasticity. The guide emphasizes pinning specific toolkits, commit hashes, configurations, and hardware details to ensure scientific rigor and verifiability.

A comprehensive guide for structuring the evidence bundle required for robotics research submissions. It details the critical components—including hardware ledgers, time-stamped logs, configuration files, code, and data—necessary to ensure that scientific claims are fully auditable and reproducible, addressing the needs of skeptical reviewers.

This comprehensive guide provides rigorous methodological guidelines for executing and reporting empirical analysis on cross-national, multi-level, or panel data, specifically tailored for top-tier international business journals. It covers essential checks like measurement invariance (configural to scalar), Common Method Variance (CMV), dynamic endogeneity, and appropriate estimation techniques (e.g., Multilevel SEM, GMM), ensuring research meets the highest academic standards.

This guide provides best practices for ensuring the scientific rigor and reproducibility of data mining and machine learning research published at top-tier conferences like KDD. It details a three-tier honesty model (Rerunnable, Rebuildable, Attested) and provides comprehensive checklists for disclosing pipeline stages, handling stochasticity, and documenting configurations to build reader trust.

This guide details the process of preparing complex research artifacts (simulators, configurations, traces, and scripts) for post-acceptance evaluation, especially for microarchitecture papers. It outlines best practices for achieving ACM badges like 'Available' and 'Reproducible,' focusing on structuring the code base with tiered entry points (smoke, key, full) and handling dependencies, licensed data, and simulation time constraints.

This guide outlines advanced methodologies for achieving high reproducibility in networked-systems research. It details capturing crucial provenance ledgers—including topology, traffic traces, system configuration, and run details—to ensure that published results are verifiable and not dependent on ephemeral experimental conditions. It emphasizes characterizing inherent variance rather than demanding absolute determinism, providing a rigorous framework for academic publication.

A comprehensive guide and tool to upgrade legacy PowerShell Pester test suites across major versions (v3→v4, v4→v5, v5→v6). It covers structural changes, syntax updates, and breaking changes related to the Discovery/Run model, mock handling, and configuration updates. Use this when modernizing or fixing broken tests after Pester version bumps.

This specialized tool analyzes Git diffs against a stated development intent to detect scope creep. It identifies overly broad pull requests, unrelated files, dependency additions, public API renames, or configuration changes. It guides users on whether changes should be kept, split into separate commits, or if the connection requires explicit justification, ensuring clean and focused code merges.

Provides comprehensive guidelines for improving the reproducibility of academic network measurements and research artifacts, particularly for conferences like SIGCOMM. It details the necessary evidence map and the 'four ledgers'—topology, traffic, configuration, and run—to ensure that all reported claims can be accurately recreated and verified by the wider academic community.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.

This comprehensive guide outlines the rigorous standards required for achieving experimental reproducibility in systems research papers (e.g., VLDB). It instructs authors on disclosing critical context, including hardware specifications, software configurations, data provenance, workload patterns, and handling measurement variance. Adhering to these guidelines ensures that all performance claims are fully traceable from raw data to published figures, meeting the highest standards of academic review.