Automates publication-quality plots and LaTeX tables from experiment data. Handles the data-driven figures in a typical ML paper: training curves, bar charts, comparison tables, heatmaps, multi-panel grids. The scope is clear upfront: it won't generate architecture diagrams or qualitative result grids, those are manual. It reads your figure plan from PAPER_PLAN.md, creates individual Python scripts with consistent matplotlib styling (300 DPI, serif fonts, clean spines), runs them all, and outputs LaTeX snippets ready to paste. Supports Chinese commands like "画图" alongside English. The workflow is sensible: parse plan, set up shared style config, auto-select chart type based on data pattern, generate scripts, verify outputs, bundle LaTeX includes. Honest about covering roughly 60% of figures in a real paper, leaving hero figures and diagrams to you.
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill paper-figure