Skill UI

Generate publication-quality figures and tables from experiment data. Supports line plots, bar charts, scatter plots, heatmaps, box/violin plots, and multi-panel figures for academic papers. Uses consistent matplotlib styling, high-resolution PDF/PNG output, and can optionally review figures via Codex MCP.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

This comprehensive checklist guides authors through the final pre-submission review process for demanding academic journals. It ensures the manuscript is scientifically rigorous and complete, focusing on critical areas such as model-driven theoretical advancement, model comparison, data reproducibility (depositing code and verifying fit regeneration), and adherence to open science mandates. Use this guide before uploading to the journal's portal.