banana-paper-illustration

name	banana-paper-illustration
description	Generate paper-style illustrations with the Banana/Jiekou text-to-image API when the user wants a visual abstract, teaser figure, concept illustration, method overview art, or publication-style research image from a paper abstract, method description, caption, or technical note. Use this skill for image-first scientific figures, not for precise charts, tables, or editable box-arrow diagrams.

Banana Paper Illustration

Generate publication-style research illustrations with the Banana text-to-image API.

Use this skill when the user wants:

• a paper teaser image
• a visual abstract
• a polished concept illustration for a method or system
• a paper figure that should look more visual than a draw.io block diagram

Do not use this skill when the user needs:

• exact plots or charts
• precise tables
• patent black-and-white line drawings
• editable architecture diagrams with strict node/edge control

For editable structure-first diagrams, prefer drawio-architecture-diagram.

Workflow

1. Decide Whether Image Generation Is Appropriate

Use this skill only if the target figure is image-first.

Good fits:

• visual summaries
• conceptual figures
• research covers
• system atmosphere figures
• hybrid method overview illustrations with limited labels

Bad fits:

• algorithm flowcharts with exact topology
• benchmark plots
• diagrams requiring deterministic box placement

2. Build The Figure Intent

Extract from the user's source:

• the paper or method title
• the central scientific story
• 3-6 visual elements
• what must be foreground vs background
• whether arrows, stages, agents, or feedback loops should appear
• what style is expected

If the source is long, compress it before prompting. Do not dump raw sections into the API.

3. Choose A Figure Mode

Use one of these modes:

• teaser: visually strong paper opener with one dominant composition
• method-overview: a semi-structured method illustration with a few modules or arrows
• visual-abstract: balanced high-level summary for the whole paper
• system-concept: system or architecture concept art with technical atmosphere

4. Generate A Prompt

Use the bundled prompt logic in:

• scripts/generate_banana_illustration.py

Read these references as needed:

• references/api.md
• references/prompt-patterns.md
• references/scientific-palettes.md

The script can:

• accept a direct prompt
• derive a prompt from a source file
• auto-select a palette by domain when --palette is not specified
• apply a scientific palette preset such as vivid-academic or okabe-ito
• save the final resolved prompt to a sibling .prompt.txt file
• call the API and write the returned image to disk

5. Call The API

Use the script instead of hand-writing the request each time:

python3 scripts/generate_banana_illustration.py \
  --source-file paper_method.md \
  --mode method-overview \
  --palette vivid-academic \
  --output paper_figure.png

The script reads the API key from:

1. BANANA_API_KEY
2. API_KEY

Do not hard-code secrets into the skill or output files.

6. Review And Iterate

After generation, review:

• whether the scientific story is clear
• whether the composition matches the intended figure type
• whether the image uses too much unreadable micro-text
• whether key modules or agents are visually distinct
• whether the output looks like a paper figure instead of generic concept art

If the result is too loose:

• simplify the figure story
• reduce the number of modules
• add stronger composition instructions
• switch to drawio-architecture-diagram if exact structure matters more than visual richness

Output Contract

By default, produce:

1. Figure Intent

State:

• chosen figure mode
• why image generation fits better than draw.io for this request

2. Prompt Summary

Return:

• a short summary of the final prompt direction
• the main scientific elements being visualized
• the key style constraints

3. Generated Files

Return:

• image path
• prompt file path
• source file path if one was created

4. Risks

Call out the main failure risks, such as:

• unreadable text inside the image
• structure drift
• too much artistic freedom
• weak faithfulness to the paper

Practical Rules

• Prefer light or white backgrounds unless the user explicitly wants a dark poster style.
• Prefer sparse labels; text rendering inside images is fragile.
• Keep the scientific story narrow. One figure should express one main idea.
• For method illustrations, ask for a structured composition but do not expect pixel-perfect topology.
• If the user needs strict module placement, switch to draw.io instead of fighting the image model.

Validation

Before finalizing, verify:

• the prompt is specific enough to encode the main scientific story
• the requested mode matches the actual figure need
• the output path is explicit
• the API key is read from environment variables only

Failure Handling

If the API key is missing:

• stop with a clear error naming the supported environment variables

If the API returns non-image content:

• save or print the diagnostic response
• do not pretend the image succeeded

If the result is too diagram-like but still inaccurate:

• recommend switching to drawio-architecture-diagram