Skill UI

Create truthful, accessible, publication-ready scientific figures using Matplotlib, Seaborn, or Plotly. Ensures data integrity, proper encoding, accessibility compliance, and journal export planning for research publications. Includes guidelines for uncertainty displays, color/contrast review, and metadata validation.

This guide provides comprehensive strategic advice for authors targeting the IEEE Visualization Conference (IEEE VIS). It covers conference positioning, scope fitting, required evidence (visual encoding, evaluation), and helps authors re-frame their work from journal or arXiv formats into a selective, high-impact conference narrative. Use it when determining if a research manuscript is appropriate for IEEE VIS.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

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.

Validates 1EdTech/IMS Global OneRoster v1.1 and v1.2 CSV roster sets for manifest integrity, bulk vs delta strictness, foreign key references, and encoding sanitization. Use for pre-flight checks before SIS/Clever/ClassLink ingestion.