plotting-agent

name	plotting-agent
description	Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".
allowed_agents	["writing"]

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:

   If PAPERBANANA_PATH is set — use the PaperBanana backbone (Zhu et al., 2026). It runs a Retriever → Planner → Stylist → Visualizer → Critic loop and is especially good for plot_type == "diagram". See references/paperbanana-cookbook.md for setup (needs a Gemini API key).

   python skills/plotting-agent/scripts/paperbanana_render.py \
       --figure-id <figure_id> \
       --caption   "<objective from figure spec>" \
       --content-file workspace/inputs/idea.md \
       --task      <diagram|plot> \
       --aspect-ratio <aspect_ratio> \
       --out       workspace/figures/<figure_id>.png
   

   Otherwise — write a matplotlib script and run it via your Bash tool, or use the bundled helper:

   python skills/plotting-agent/scripts/render_matplotlib.py \
       --spec spec.json \
       --out workspace/figures/<figure_id>.png
   

   The script must apply the academic style from chart-patterns.md, use the correct pixel size from aspect-ratios.md, save at 300 DPI, and call plt.close() after savefig.

5. VLM critique loop (optional, only if your host has vision):

   • Reload the rendered PNG as a multimodal input to your LLM.
   • Critique it against the figure's objective from the outline. Look for: visual artifacts, mislabeled axes, illegible text, color clashes, misleading scaling, missing legend, overlapping labels.
   • If problems are found, regenerate the matplotlib script with corrections and re-render. Cap at 3 critique iterations per figure.
   • This is the closed-loop refinement step the paper inherits from PaperBanana. See references/plotting-pipeline.md for the full loop description.
   • If your host has no vision input, skip this step entirely. The figure will still render correctly, just without iterative refinement.

6. Generate the caption using the verbatim Caption Generation prompt at references/caption-prompt.md. Inputs to the caption prompt:

   • task_name — the section the figure belongs to (e.g., "Methodology", "Experiments")
   • raw_content — the surrounding section text (or content_bullets from the section_plan if the section isn't drafted yet)
   • description — the objective field from the figure spec
   • figure_desc — a 1-sentence description of what the rendered figure actually shows (from your VLM critique pass, or from the script's plan if no vision)

   Write the caption to workspace/figures/captions.json keyed by figure_id. Captions must NOT contain Figure N: or Caption N: prefixes — the LaTeX template handles numbering. Plain text only, no markdown.

Conceptual diagrams

For plot_type == "diagram", prefer PaperBanana when available — its Retriever grounds the Planner in real published paper diagrams. If PAPERBANANA_PATH is unset, follow references/diagram-patterns.md. Patterns include block diagrams, system overviews, flowcharts, and algorithm-as-graph. The bundled helper:

python skills/plotting-agent/scripts/render_diagram.py \
    --spec diagram_spec.json \
    --out workspace/figures/<figure_id>.png

handles the simple cases (boxes-and-arrows). For complex Fig-1-style overview diagrams, write matplotlib patches code yourself.

Hard rules

• 300 DPI for every figure. Lower DPI gets rejected at the LaTeX compile step on conference templates.
• Aspect ratio is exact. The figure spec's aspect_ratio is one of 12 enumerated strings. Use the pixel targets in references/aspect-ratios.md.
• Hide top and right spines for plots. (Diagrams: no spines at all.)
• Muted academic colors only. The palette is in chart-patterns.md. Never use matplotlib defaults (too saturated for print).
• No 3D, no pie charts, no decorative visuals. The paper's evaluators penalize these.
• Every figure MUST have a caption in captions.json. The Section Writing Agent will fail-stop if a caption is missing for any figure referenced from the outline.
• No Figure N: prefix in captions — LaTeX adds it.
• Never describe data you didn't plot. The Plotting Agent must not hallucinate axes, baselines, or trends. Source-of-truth is experimental_log.md or idea.md.

Pre-existing figures (PlotOn mode)

If workspace/inputs/figures/ is non-empty, check whether any pre-existing file matches a figure_id in the outline (by filename prefix). If so, copy it into workspace/figures/ as-is and still generate a caption using the caption prompt. Only generate from scratch the figure_ids that have no pre-existing counterpart.

Resources

• references/caption-prompt.md — verbatim Caption Generation prompt from App. F.1
• references/plotting-pipeline.md — the full few-shot → render → critique → caption loop
• references/chart-patterns.md — matplotlib style + chart type recipes
• references/diagram-patterns.md — conceptual diagram recipes
• references/aspect-ratios.md — pixel targets for each of the 12 allowed ratios at 300 DPI
• references/paperbanana-cookbook.md — NEW PaperBanana setup, usage, cost notes, attribution
• scripts/render_matplotlib.py — render a JSON plot spec → PNG (matplotlib fallback)
• scripts/render_diagram.py — render a JSON diagram spec → PNG (matplotlib fallback)
• scripts/paperbanana_render.py — NEW PaperBanana backbone wrapper (reads PAPERBANANA_PATH from env)