Skill UI

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

This guide provides comprehensive technical instructions for optimizing Palantir Foundry costs. Learn to use incremental transforms to drastically reduce compute rebuilds, right-size Spark profiles based on data volume, and migrate from expensive API polling to efficient, event-driven webhooks, ensuring both cost efficiency and system stability.

This guide provides comprehensive instructions on managing performance and concurrency within Snowflake. It covers key concepts like warehouse sizing, multi-cluster configuration, and detecting query queuing issues. Users learn how to right-size resources and implement application-level rate limiting to prevent throttling and optimize data warehouse throughput for complex analytics workloads.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.

This guide details the complex requirements for writing the 'Related Work' section for CIKM submissions. It emphasizes the need for 'three-front coverage' (Retrieval, Mining, and Knowledge Management/Databases) and provides specific instructions on writing the 'boundary-work paragraph.' It also covers critical academic hygiene, such as avoiding venue misattribution, maintaining recency baselines, and accurately stating novel contributions.

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 assists authors planning to submit to Games and Economic Behavior (GEB) by detailing best practices for data and code sharing. It clarifies that while data deposition is strongly encouraged for reproducibility, it is not a mandatory requirement. It outlines what to prepare, including raw data, analysis scripts, experimental instructions, and proper availability statements.

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.

A comprehensive guide for authors submitting to the Journal of Corporate Finance (JCF). It details how to comply with Elsevier's Option C data availability requirements, focusing on drafting the data availability statement and structuring the research archive. It provides best practices for handling licensed, proprietary data (e.g., Compustat, CRSP) by recommending the sharing of construction code and access instructions, rather than raw data deposits.

A comprehensive guide for assembling a complete and reproducible materials package for experimental economics research, specifically for JEBO submissions. It details required components, including participant instructions, z-Tree/oTree code, anonymized raw data, detailed analysis scripts, and environment specifications, ensuring rigorous academic reproducibility.

This guide assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

This guide provides detailed instructions on structuring the online appendix for high-impact academic manuscripts. It emphasizes the critical balance between maintaining a self-contained, readable main text and providing comprehensive, verifiable supporting material. Learn how to properly place derivations, robustness checks, and data construction details to enhance the scientific rigor and credibility of your research.