Skill UI

A comprehensive guide to deploying and configuring osquery across diverse endpoints (Windows, macOS, Linux). This tool allows security teams to gather deep, SQL-based insights into endpoint state, including running processes, network connections, system configuration, and installed software. It is essential for threat hunting, compliance auditing, and building centralized fleet visibility.

This package guides researchers on assembling a comprehensive deposit to meet the high standards of Experimental Economics (ESA) journals. It is crucial for academic submissions, ensuring that the materials—including participant instructions, experiment software, raw data, and master analysis code—are complete enough for others to both reproduce the statistical findings and replicate the entire economic experiment.

This guide outlines the mandatory open data and code requirements for publishing in Global Change Biology (GCB). It details that primary and secondary data, along with supporting code/software, must be archived in public, DOI-minting repositories (e.g., Dryad, Zenodo) prior to acceptance. It emphasizes that 'available on request' is not accepted and provides a structured checklist for ensuring reproducibility and proper metadata submission.

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide for econometricians submitting manuscripts to top journals. It details how to structure analyses for full reproducibility, including using master scripts, pinning software versions, and exporting results to plain-text formats. Coverage includes advanced robust inference techniques such as HAC, cluster bootstrapping, and weak-IV diagnostics, ensuring the work satisfies rigorous archival standards.

A comprehensive guide for authors submitting to the Journal of Software (JOS), clarifying the current status and best practices for including reusable code and data artifacts. It distinguishes JOS's approach from mandatory international conference badging systems, focusing instead on making reproducibility a strong, proactive addition to your submission.

A comprehensive guide for maintaining consistency and completeness of references for PNAS Nexus submissions. It details the critical differences between the format-neutral requirements at submission time and the final numbered style required for publication, emphasizing the proper citation of datasets, software, and preprints.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

A comprehensive guide for authors preparing methodological papers for submission to SMR. It outlines strict standards for creating a usable, reproducible package, including structured code, master scripts with fixed seeds, and formal data/code availability statements, ensuring the method can be run by others.

A comprehensive guide for authors submitting to Sociological Methods & Research (SMR). It routes authors through the rigorous, multi-stage process required for publication, covering methodological contribution, derivation, simulation studies, empirical illustration, and ensuring full software reproducibility. This tool helps authors structure their manuscript to meet high academic standards.

A comprehensive guide for assembling a reproducible data and code package for publishing economic manuscripts, specialized for World Bank Economic Review (WBER) standards. It details best practices for data availability statements, handling restricted microdata, pinning software versions, and ensuring full transparency throughout the analysis pipeline.

Stress-test core claims for Journal of Systems Engineering submissions: parameter sensitivity, initial conditions, alternative models and metrics, extreme scenarios, Monte Carlo repetitions, random seeds, software environments, data/code statements, and explicit failure boundaries.