Plotting Agent (Step 2)

Faithful implementation of the Plotting Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 2 and App. F.1 p.45).

Cost: ~20, 30 LLM calls. The paper uses PaperBanana (Zhu et al., 2026) as the default backbone with a closed-loop VLM-critique refinement. This skill expresses that loop in host-agent terms: you (the host agent) generate matplotlib code with your own LLM, render via your Bash/Python tool, optionally critique the rendered PNG with your vision model, redraw, and finally caption.

Inputs

• workspace/outline.json, specifically the plotting_plan array
• workspace/inputs/idea.md and workspace/inputs/experimental_log.md, the source data
• workspace/inputs/figures/, optional pre-existing figures (PlotOn mode)

Outputs

• workspace/figures/<figure_id>.png, one PNG per plotting_plan entry (300 DPI, sized to the requested aspect ratio)
• workspace/figures/captions.json, {figure_id: caption_text} map

Workflow

Per figure (executed independently per figure_id)

1. Read the figure spec from outline.json:

   {
     "figure_id": "fig_main_results",
     "title": "Main Results on Dataset X",
     "plot_type": "plot",
     "data_source": "experimental_log.md",
     "objective": "Visual summary (Grouped Bar Chart) demonstrating ...",
     "aspect_ratio": "5:4"
   }
   

2. Few-shot retrieval (visual planning): pick the matching pattern from references/chart-patterns.md (for plot_type=="plot") or references/diagram-patterns.md (for plot_type=="diagram").

3. Extract data: parse idea.md and/or experimental_log.md (data_source field tells you which) to obtain the numeric values or conceptual entities the figure needs. For experimental_log.md, the ## 2. Raw Numeric Data section contains markdown tables.

4. Render: