Skill UI

A comprehensive guide for economists and researchers on determining if their work concerning monetary policy, banking, credit, or macro-finance is a suitable fit for the Journal of Money, Credit and Banking (JMCB). It teaches how to structure arguments, identify clear policy stakes, and differentiate the unique scope of JMCB from similar academic outlets.

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.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

This skill is a comprehensive guide for evaluating whether a manuscript in ecology or evolutionary biology is appropriately positioned for Nature Ecology & Evolution. It helps authors check for conceptual novelty, methodological rigor (e.g., macroecology, phylogenetics), and broad scientific significance. Use this tool when determining the best target journal or restructuring the narrative to emphasize general, impactful scientific advances.

This guide assists researchers in determining whether a manuscript's topic, methodology, and theoretical contribution are appropriate for a specific theory-building management journal. It provides detailed criteria for evaluating conceptual relationships, suitable methods (SEM, experiment, case study), and appropriate re-routing suggestions for papers that are misaligned with the target journal's scope (e.g., pure math models, macro policy evaluation).

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

This guide provides rigorous best practices for developing and presenting theoretical economic models for top-tier journals. It emphasizes framing results with a clear economic payoff, maintaining minimal model setups, and ensuring that complex, full proofs are relegated to the online appendix. The goal is to ensure the primary economic insight remains highly visible and actionable, rather than being buried in algebraic machinery.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

Scans your local machine's Git history to locate abandoned or unfinished side projects. It performs a deep 'autopsy' on these repositories, determining their causes of death (e.g., deployment fear, killed by newer ideas) and identifying personal developmental patterns. The tool then surfaces the most promising candidate for resurrection, helping you finally ship it.

This tool assists researchers in assembling and rigorously validating a complete replication package for academic submission. It checks the master script, pinned environment, data plan, and methodology against specific journal policies, ensuring that every reported number is reproducible and deterministic, preventing 'my machine' errors.

This comprehensive guide details the methodology for reverse engineering macOS and Mach-O binaries. It covers analyzing application bundles, deciphering Objective-C/Swift symbols, examining code signing and hardened runtime features, and performing both static (using tools like Ghidra, IDA) and dynamic analysis (using lldb, Frida) to aid in malware analysis and platform security research.

Assesses Art. 50 transparency duties under the EU AI Act for a provider/deployer. Covers chatbot disclosure, synthetic-content marking, emotion-recognition notices, machine-readable marks, exceptions, deadlines. Produces mini-report with compliance checklist and gap flags.