plotting-agentlisted

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".
Ar9av/PaperOrchestra · ★ 549 · AI & Automation · score 81

Install: claude install-skill Ar9av/PaperOrchestra

# 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`: ```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-pa