paperbanana

name	paperbanana
description	Generate publication-quality academic diagrams and statistical plots from text descriptions using the paperbanana CLI. Supports methodology diagrams, statistical plots, diagram evaluation, and iterative refinement with user feedback. Use when the user asks to: create a diagram, generate a figure, make a plot from data, evaluate diagram quality, refine/improve a generated diagram, or anything related to academic illustration generation. Triggers on: "diagram", "figure", "plot", "paperbanana", "academic illustration", "methodology diagram", "evaluate diagram", "refine diagram", "improve figure".
allowed-tools	Bash, Read, Write, Edit, Glob, Grep

PaperBanana — Academic Diagram & Plot Generation

Generate publication-quality academic diagrams and statistical plots via the paperbanana CLI. Multi-agent pipeline: retrieval → planning → styling → visualization → critic refinement.

Environment Setup

Always source the env file before running any paperbanana command:

source ~/.config/paperbanana/env && paperbanana <command> [options]

The env file at ~/.config/paperbanana/env contains API keys and default model settings. Do NOT hardcode API keys in commands.

First-time setup

If the env file does not exist, create it:

mkdir -p ~/.config/paperbanana
cat > ~/.config/paperbanana/env << 'EOF'
export GOOGLE_API_KEY="your-google-api-key"
export VLM_MODEL="gemini-3-flash-preview"
EOF

Commands

1. generate — Methodology Diagrams

Create a diagram from a methodology text file.

source ~/.config/paperbanana/env && paperbanana generate \
  -i <text_file> -c "<caption>" \
  [-o <output.png>] [-n <iterations>] \
  [--vlm-provider <p>] [--vlm-model <m>] \
  [--image-provider <p>] [--image-model <m>] \
  [--config <config.yaml>]

Required: --input/-i (text/PDF file), --caption/-c (figure caption / communicative intent)

2. plot — Statistical Plots

source ~/.config/paperbanana/env && paperbanana plot \
  -d <data.csv> --intent "<description>" \
  [-o <output.png>] [-n <iterations>] [--vlm-provider <p>]

Required: --data/-d (CSV/JSON), --intent (what the plot should convey)

3. evaluate — Diagram Quality Assessment

Compare generated vs human reference using VLM-as-a-Judge. Scores: Faithfulness, Readability (primary), Conciseness, Aesthetics (secondary).

source ~/.config/paperbanana/env && paperbanana evaluate \
  -g <generated.png> -r <reference.png> \
  --context <source.txt> -c "<caption>" \
  [--vlm-provider <p>]

Output Management

All outputs MUST be saved under outputs/paperbanana/<SEMANTIC_NAME>/ in the current working directory.

Naming Convention

Before running any generation command, create a semantically named output directory:

mkdir -p outputs/paperbanana/<SEMANTIC_NAME>

<SEMANTIC_NAME> should be a concise, descriptive name derived from the task context. Examples:

• encoder_decoder_overview — for an encoder-decoder architecture diagram
• accuracy_comparison_bar — for a bar chart comparing accuracy
• training_pipeline_v2 — for a revised version of a training pipeline diagram
• cosmoflow_architecture — for a CosmoFlow architecture diagram

For refinement iterations, append a version suffix: <SEMANTIC_NAME>_v2, _v3, etc.

How to Use

Always pass -o pointing into the semantic directory:

# generate
source ~/.config/paperbanana/env && paperbanana generate \
  -i method.txt -c "caption" \
  -o outputs/paperbanana/encoder_decoder_overview/diagram.png

# plot
source ~/.config/paperbanana/env && paperbanana plot \
  -d results.csv --intent "intent" \
  -o outputs/paperbanana/accuracy_comparison_bar/plot.png

The CLI also creates its own outputs/run_<timestamp>/ directory with intermediate artifacts. After a successful run, copy the run directory contents into the semantic directory so everything is consolidated:

# Find the latest run and copy artifacts
LATEST_RUN=$(ls -td outputs/run_* | head -1)
cp -r "$LATEST_RUN"/* outputs/paperbanana/<SEMANTIC_NAME>/

Final Directory Structure

outputs/paperbanana/
├── encoder_decoder_overview/
│   ├── diagram.png               # Final output (via -o flag)
│   ├── final_output.png          # CLI's copy of final image
│   ├── metadata.json             # Run config, provider info, iteration count
│   ├── planning.json             # Retrieved examples, initial & optimized descriptions
│   ├── iter_1/
│   │   ├── details.json          # description + critique
│   │   └── diagram_iter_1.png
│   ├── iter_2/ ...
│   └── iter_3/ ...
├── encoder_decoder_overview_v2/  # After user feedback refinement
│   ├── diagram.png
│   ├── ...
└── accuracy_comparison_bar/
    ├── plot.png
    └── ...

Key Fields in Artifacts

planning.json:

• initial_description: Planner output
• optimized_description: Stylist output (used as input for iteration 1)

iter_N/details.json:

• description: The description used for this iteration's image generation
• critique.critic_suggestions: List of improvement suggestions
• critique.revised_description: Improved description for next iteration

Iterative Refinement with User Feedback

The CLI does not have a built-in --continue flag, so you must orchestrate feedback loops manually.

Workflow

1. Initial generation: Run paperbanana generate and show the resulting image to the user.

2. User gives feedback: e.g., "Make the arrows thicker" or "The encoder block should be more prominent."

3. Read the previous run's artifacts from the semantic directory:

   • Read outputs/paperbanana/<SEMANTIC_NAME>/planning.json to get the optimized description.
   • Read the latest outputs/paperbanana/<SEMANTIC_NAME>/iter_N/details.json to get the last revised description and critique.

4. Create an enhanced input file that incorporates the user's feedback:

   • Read the original input file.
   • Write a new temporary input file that appends the previous run's final description AND the user's feedback as explicit requirements.

   [Original methodology text]
   
   ---
   PREVIOUS GENERATION CONTEXT:
   The previous diagram used this description: [revised_description from last iteration]
   The critic noted: [critic_suggestions]
   
   USER FEEDBACK — The following changes MUST be reflected in the new diagram:
   - [user's feedback points]
   

5. Re-run generation into a new versioned semantic directory:

   mkdir -p outputs/paperbanana/<SEMANTIC_NAME>_v2
   source ~/.config/paperbanana/env && paperbanana generate \
     -i /tmp/pb_refined_input.txt \
     -c "Revised: <original caption>. Changes: <user feedback summary>" \
     -n 5 \
     -o outputs/paperbanana/<SEMANTIC_NAME>_v2/diagram.png
   # Copy run artifacts
   LATEST_RUN=$(ls -td outputs/run_* | head -1)
   cp -r "$LATEST_RUN"/* outputs/paperbanana/<SEMANTIC_NAME>_v2/
   

6. Show the new result and repeat if needed.

Feedback Example

# Step 1: User asks for a diagram
mkdir -p outputs/paperbanana/framework_overview
paperbanana generate -i method.txt -c "Framework overview" \
  -o outputs/paperbanana/framework_overview/diagram.png
LATEST_RUN=$(ls -td outputs/run_* | head -1)
cp -r "$LATEST_RUN"/* outputs/paperbanana/framework_overview/

# Step 2: User says "arrows are too thin, and make the encoder block blue"
# → Read outputs/paperbanana/framework_overview/planning.json and iter_3/details.json
# → Write /tmp/pb_refined_input.txt with original text + context + feedback
mkdir -p outputs/paperbanana/framework_overview_v2
paperbanana generate -i /tmp/pb_refined_input.txt \
  -c "Framework overview. Revised: thicker arrows, blue encoder block" \
  -n 5 -o outputs/paperbanana/framework_overview_v2/diagram.png
LATEST_RUN=$(ls -td outputs/run_* | head -1)
cp -r "$LATEST_RUN"/* outputs/paperbanana/framework_overview_v2/

Tips for Effective Refinement

• Increase iterations (-n 5 or higher) for refinement runs — the critic needs more rounds to satisfy specific requirements.
• Be explicit in the enhanced caption — mention the specific visual changes requested.
• Preserve the original context — always include the full original methodology text in the input.

Long-Running Tasks

Generation is slow (multi-agent + iterative refinement). Use pueue for background execution:

pueue group add paperbanana 2>/dev/null
pueue add -g paperbanana -- bash -c 'source ~/.config/paperbanana/env && paperbanana generate -i method.txt -c "caption" -o output.png'

Configuration

Edit ~/.config/paperbanana/env to change API keys or default models:

export GOOGLE_API_KEY="your-key"
export VLM_MODEL="gemini-3-flash-preview"

Run paperbanana setup for the interactive configuration wizard.