数据与研究

academic-plotting

orchestra-research/ai-research-skills

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

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安装命令

npx skills add https://github.com/orchestra-research/ai-research-skills --skill academic-plotting

SKILL.md

Academic Plotting for ML Papers

Generate publication-quality figures for ML/AI conference papers. Two distinct workflows:

  1. Diagram figures (architecture, system design, workflows, pipelines) — AI image generation via Gemini
  2. Data figures (line charts, bar charts, scatter plots, heatmaps, ablations) — matplotlib/seaborn

When to Use Which Workflow

Figure Type Tool Why
Architecture / system diagram Gemini (Workflow 1) Complex spatial layouts with boxes, arrows, labels
Workflow / pipeline / lifecycle Gemini (Workflow 1) Multi-step processes with connections
Bar chart, line plot, scatter matplotlib (Workflow 2) Precise numerical data, reproducible
Heatmap, confusion matrix matplotlib/seaborn (Workflow 2) Structured grid data
Ablation table as chart matplotlib (Workflow 2) Grouped bars or line comparisons
Pie / donut chart matplotlib (Workflow 2) Proportional data (use sparingly in ML papers)
Training curves matplotlib (Workflow 2) Loss/accuracy over steps/epochs