Skill UI

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.

A comprehensive guide for authors submitting visualization papers to major conferences like IEEE VIS. It outlines the strict rules for partitioning content, ensuring the core argument remains judgeable from the main body while leveraging supplemental materials (video, code, full data) to demonstrate interaction and reproducibility. Essential reading for academic researchers in the field.

This guide outlines the methodology for preparing research artifacts for rigorous academic reproducibility evaluations, such as those run by VLDB/SIGMOD. It details the required components—including prototype code, input data, workloads, and analysis scripts—to structure a comprehensive package. The goal is to ensure that any external evaluator can independently and flawlessly rebuild and validate all claimed results, maximizing the chances of winning reproducibility awards.

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.

Provides comprehensive guidelines for packaging and submitting research artifacts (datasets, models, code) for major conferences. It details the difference between mere 'existence' (badge verification) and true academic 'reproducibility,' covering archival strategies, web data licensing, takedown resilience, and minimal viable package structure for long-term scholarly impact.

A comprehensive guide for preparing replication and transparency packages for World Development manuscripts. It provides checklists and best practices for both quantitative (code reproducibility, data provenance, de-identification) and qualitative (evidence-to-claim trail, methods appendix) research. It addresses ethical constraints and the unique challenges of double-anonymized review.

This comprehensive guide outlines best practices for packaging research artifacts, including code, proprietary data logs, and models, for submission to top-tier academic venues. It addresses the crucial need for verifiability and reproducibility, providing strategies for handling limited space, proprietary datasets, and the absence of formal artifact grading systems, ensuring high academic credibility.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.

This skill maps chronic conditions extracted from clinical notes to CMS-HCC V28 categories. It estimates the Risk Adjustment Factor (RAF) score, which is crucial for Medicare Advantage payments. It serves as decision support for coders by identifying candidate HCCs, applying complex coding hierarchies, and checking for MEAT documentation evidence for human validation.

Provides decision support for mapping diagnoses and procedures extracted by OpenMed to standardized ICD-10-CM and ICD-10-PCS codes. This skill suggests candidate codes along with detailed clinical rationale, helping human coders narrow down options, route findings to specific chapters, and pre-fill encounter records. Note that the output is advisory and requires mandatory validation by a certified medical coder.

A highly specialized, downstream skill for advanced threat analysis. This tool is used to recover hidden configurations, decode C2 (Command and Control) parameters, unpack staged payloads, and extract Indicators of Compromise (IOCs) from complex malware samples. It focuses on identifying the exact decode chains—including compression, encoding, and crypto steps—and linking recovered fields to observed behavioral outcomes within a sandbox environment.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).