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academic-figure-generation

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).

jxtse/scientific-research-skills

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含まれるファイル（2）

SKILL.md5.1 KB
scripts/generate.py7.2 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

Clone PaperBanana somewhere convenient:

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

Configure configs/model_config.yaml — set the image model and the matching API key. Two common setups:

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:

Method text: the relevant section of the paper describing the approach (./method.md or ./method.tex).
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

Flag	Default	Notes
`--paperbanana-root`	(required)	Path to your PaperBanana checkout
`--method-file`	(required)	Method section as a text/markdown file
`--caption`	(required)	Target figure caption
`--out-dir`	(required)	Where PNGs land
`--candidates`	`3`	Independent diagram candidates
`--max-concurrent`	`2`	Cap concurrent runs (be gentle on quota)
`--exp-mode`	`demo_full`	Full pipeline (Planner+Stylist+Visualizer+Critic). Use `demo_planner_critic` to skip Stylist, or `vanilla` for single-shot.
`--aspect-ratio`	`16:9`	One of `21:9`, `16:9`, `3:2`, `1:1`
`--max-critic-rounds`	`2`	Critique → revise loops (early-exits if critic says "No changes needed")

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

Type	When to use	Key elements
Pipeline / Flowchart	Sequential processing	Boxes + arrows, L→R or T→B
Architecture	System overview	Nested boxes, clear module boundaries
Comparison	Before/after, baseline vs proposed	Side-by-side panels
Ablation	Component contributions	Bar charts, highlighted rows
Framework	High-level conceptual overview	Abstract shapes, minimal detail

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.

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概要と使いどころ

literature-search

無料

Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, Gemini deep research, AMiner, Google Scholar) with adaptive engine selection based on query type. Returns ranked, deduplicated results with metadata (authors, venue, year, citations, abstract, PDF link). Use when the user asks to find papers / literature / publications / preprints / references on a topic, search for related work, look up a specific paper by title or DOI or arXiv ID, find papers by an author, find recent SOTA / state-of-the-art work, survey a research area, or run a deep / comprehensive literature search with synthesis.

日本語の概要は準備中です。原文の説明を表示しています。