version: "1.0.0" name: academic-figure-generation description: > Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).

Academic Figure Generation

Thin CLI wrapper around PaperBanana (a.k.a. PaperVizAgent), a multi-agent figure-generation pipeline for academic papers.

The skill provides exactly one script: scripts/generate.py. It feeds your method text + caption into PaperBanana and writes N candidate PNGs. Model selection and API keys come from PaperBanana's own configs/model_config.yaml — the wrapper does not override them.

One-time setup

1. Clone PaperBanana somewhere convenient:

``bash git clone https://github.com/dwzhu-pku/PaperBanana.git ~/PaperBanana cd ~/PaperBanana uv venv && uv pip install -r requirements.txt ``

1. Configure `configs/model_config.yaml` — set the image model and

the matching API key. Two common setups:

```yaml defaults: image_model_name: "gemini-3-pro-image-preview" # or "openai/gpt-5.4-image-2" model_name: "gemini-3.1-pro-preview" # text model for Planner/Stylist/Critic

api_keys: google_api_key: "..." # required for Gemini models openrouter_api_key: "" # required for openai/gpt-5.4-image-2 ```

Use Gemini if you have a Google AI key; use GPT-Image-2 via OpenRouter if you have an OpenRouter key. Pick one — there's nothing else to wire up.

Workflow

Step 1: Gather inputs

You need:

1. Method text: the relevant section of the paper describing the

approach (./method.md or ./method.tex).

1. Figure caption: the target caption, e.g. `"Figure 1: Overview of

our framework"`.

If the user only gives a vague request, ask:

• What aspect of the method should the figure focus on?
• Style? (block diagram, flowchart, pipeline, architecture, comparison)
• Venue / column width? (ACL ≤ 7.5", NeurIPS single-column 5.5")

Step 2: Generate

~/PaperBanana/.venv/bin/python scripts/generate.py \
  --paperbanana-root ~/PaperBanana \
  --method-file ./method.md \
  --caption "Figure 1: Overview of our framework" \
  --out-dir ./figures/v1 \
  --candidates 3 \
  --aspect-ratio 16:9

Step 3: Present & iterate

• Show all candidates to the user.
• Common refinements: color scheme, layout, label text, font size.
• Re-run with a tweaked caption or more candidates.

Step 4: Export

• PNGs are written as candidate_0.png, candidate_1.png, … in --out-dir.
• For camera-ready PDFs: magick candidate_0.png candidate_0.pdf.

Style guidelines

• Color: consistent, colorblind-friendly palette
• Fonts: match the paper's body font (Times for ACL/EMNLP,

Helvetica/Arial for many ML venues)

• Labels: concise; no full sentences inside the diagram
• Arrows: solid for data flow, dashed for optional / feedback loops
• Whitespace: don't overcrowd — reviewers skim figures in seconds

Common figure types

Troubleshooting

• `429 RESOURCE_EXHAUSTED` on Gemini: monthly Google AI Studio

spending cap hit. Raise it at <https://ai.studio/spend> or switch image_model_name to openai/gpt-5.4-image-2 and set OPENROUTER_API_KEY.

• `OpenRouter Client not initialized`: OPENROUTER_API_KEY not in env

and openrouter_api_key not in yaml.

• No PNGs in output dir: check out_dir/results.json for the raw

per-candidate response and any error messages.

• Long latency (>5 min): most wall time is the image model. Lower

--candidates or use --exp-mode vanilla for faster iteration.

Links

• PaperBanana repo: <https://github.com/dwzhu-pku/PaperBanana>
• PaperVizAgent (Google Research version of the same project): <https://github.com/google-research/papervizagent>