PaperBanana Skills for Claude Code

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Gallery

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Skills in this Marketplace

| Skill | Scope | Description | Version | |-------|-------|-------------|---------| | paperbanana | user | Academic diagrams, plots, slides, and quality evaluation | v4.0.0 | | paperbanana-slide-deck | project | Full slide deck orchestration (RDIV workflow) + 150+ style presets | v1.1.0 |

Feature Matrix

| Capability | Status | Details | |------------|--------|---------| | Methodology diagrams | ✅ | Text → publication-quality figure in 30s | | Statistical plots | ✅ | CSV/JSON data → auto-styled academic plot | | Presentation slides | ✅ | Markdown → 4K slide with 150+ style presets | | Multi-venue styles | ✅ New | --venue neurips\|icml\|acl\|ieee\|custom | | PDF input | ✅ New | --input paper.pdf --pages 3-5 | | 6-item quality eval | ✅ New | Binary checklist: completeness, layout, annotation, color, legibility, hallucination | | Autoresearch loop | ✅ New | Automated prompt self-optimization with keep/revert | | Error handling | ✅ New | Critic UNREVIEWED status, provider fallback chains, retry filtering | | 5 VLM providers | ✅ | Gemini, Claude, OpenAI, Bedrock, OpenRouter | | Auto-refine | ✅ | --auto loops until Critic is satisfied | | Run continuation | ✅ | --continue with --feedback for iterative refinement | | Dynamic aspect ratio | ✅ | 8 Imagen ratios, Planner auto-recommends |

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What's New in v4.0

Eval-First Quality System

A 6-item binary checklist evaluator that measures academic figure quality without human reference images:

| Check | Question | Pass Criteria | |-------|----------|--------------| | Completeness | All input concepts represented? | Every key concept has a visual element | | Layout | Logical flow direction? | Clear L→R, T→B, or radial flow | | Annotation | All components labeled? | Every visual element has text | | Color Restraint | ≤3 primary colors? | Academic palette discipline | | Legibility | Readable at 50% zoom? | Text survives PDF column layout | | No Hallucination | Zero unlabeled concepts? | Nothing invented beyond input |

Baseline: 76% → 100% after prompt optimization. Color restraint was the bottleneck (33% → 100%).

Autoresearch Self-Optimization

Automated prompt mutation loop inspired by Karpathy's autoresearch:

Mutate prompt → Generate figures → Evaluate checklist → Keep or Revert → Repeat

• One mutation per round (isolation principle)
• Targets weakest checklist dimension automatically
• Versioned prompt snapshots + JSONL changelog
• Stop condition: 3 consecutive rounds at 90%+ or 20 rounds max

Multi-Venue Academic Styles

/paperbanana generate method.txt "Architecture overview" --venue neurips

Built-in style guides for NeurIPS, ICML, ACL, IEEE — each with venue-specific color palettes, layout conventions, and typography.

Robust Error Handling

| Failure Type | Behavior | |-------------|----------| | Image API failure | Retry 3× → fallback provider chain → report | | Critic JSON parse failure | Never silently approve — mark UNREVIEWED, retry once | | Rate limit (429) | Exponential backoff, skip non-transient errors | | Plot code injection | AST-based import blocklist (os, subprocess, socket blocked) |

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Quick Start

# 1. Install PaperBanana
git clone https://github.com/llmsresearch/paperbanana.git
cd paperbanana && pip install -e ".[google]"

# 2. Add the marketplace & install skills
claude plugin marketplace add PlutoLei/paperbanana-skill
claude plugin install paperbanana@paperbanana-skills
claude plugin install paperbanana-slide-deck@paperbanana-skills --scope project  # optional

# 3. Generate your first figure
# /paperbanana A 4-layer CNN with batch normalization for image classification

Note: This repository contains Claude Code skill definitions (SKILL.md files). The underlying Python package lives at llmsresearch/paperbanana.

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Why PaperBanana?

| Pain Point | Traditional | With PaperBanana | |------------|------------|------------------| | Methodology figures | Hours in PowerPoint / TikZ | One sentence, 30 seconds | | Statistical plots | matplotlib boilerplate | Describe your intent, auto-styled | | Style consistency | Manual effort per figure | Critic agent enforces palette | | Quality assurance | Eyeball it | 6-item binary checklist, automated | | Venue compliance | Read style guide, guess | --venue neurips handles it |

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Pipeline Architecture

The pipeline runs iteratively: the Critic evaluates each output against academic quality criteria and either accepts it or sends revision instructions back to the Planner. Parse failures are handled safely — never silently approved.

Slide Deck Orchestrator

End-to-end presentation creation: analyze content → select from 23 visual styles → generate outlines → batch-generate 4K slides → merge to PPTX/PDF.

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Commands

| Command | Purpose | Example | |---------|---------|---------| | generate | Methodology diagrams | /paperbanana A transformer with sparse attention | | plot | Statistical plots | /paperbanana plot results.csv Bar chart of accuracy | | slide | Presentation slides | /paperbanana slide prompt.md | | slide-batch | Batch slides | /paperbanana slide-batch prompts/ | | evaluate | Compare gen vs reference | /paperbanana evaluate gen.png ref.png | | data | Manage datasets | /paperbanana data download | | setup | Setup wizard | /paperbanana setup |

# Generate with venue-specific style
/paperbanana generate method.txt "Overview of the proposed framework" --venue neurips --optimize

# Generate from PDF
/paperbanana generate paper.pdf "Architecture diagram" --pages 3-5

# Auto-refine until Critic is satisfied
/paperbanana generate method.txt "Pipeline overview" --auto

# Continue with feedback
/paperbanana generate --continue --feedback "Make the arrows thicker and add color coding"

# Custom provider and aspect ratio
/paperbanana generate method.txt "Wide pipeline" --vlm-provider anthropic --aspect-ratio 16:9

# Batch generate slides with style
/paperbanana slide-batch prompts/ --resolution 4k --style ml-ai --iterations 3

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Supported Providers

| Provider | VLM | Image Generation | Setup | |----------|-----|-----------------|-------| | Google Gemini | Flash / Pro | Imagen 3 | GOOGLE_API_KEY | | Anthropic Claude | Claude 4 | — | ANTHROPIC_API_KEY | | OpenAI | GPT-4o | DALL-E 3 | OPENAI_API_KEY | | AWS Bedrock | Claude / Nova | Nova Canvas | AWS credentials | | OpenRouter | Various | Various | OPENROUTER_API_KEY |

Retry policy: Transient errors (429, 5xx) retry with exponential backoff. Auth errors (401, 403) fail immediately — no wasted retries.

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Installation

Option A: Plugin marketplace (recommended)

claude plugin marketplace add PlutoLei/paperbanana-skill
claude plugin install paperbanana@paperbanana-skills
claude plugin install paperbanana-slide-deck@paperbanana-skills --scope project  # optional

Option B: Manual install

# paperbanana skill (user-level)
mkdir -p ~/.claude/skills/paperbanana
curl -o ~/.claude/skills/paperbanana/SKILL.md \
  https://raw.githubusercontent.com/PlutoLei/paperbanana-skill/master/plugins/paperbanana/skills/paperbanana/SKILL.md

# paperbanana-slide-deck skill (project-level, optional)
mkdir -p .claude/skills/paperbanana-slide-deck
curl -o .claude/skills/paperbanana-slide-deck/SKILL.md \
  https://raw.githubusercontent.com/PlutoLei/paperbanana-skill/master/plugins/paperbanana-slide-deck/skills/paperbanana-slide-deck/SKILL.md

PaperBanana package setup

git clone https://github.com/llmsresearch/paperbanana.git
cd paperbanana
pip install -e ".[google]"          # Gemini (default, free tier available)
# pip install -e ".[all]"           # All providers
python -m paperbanana.cli setup     # Interactive API key configuration

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Style Presets (23 available)

Use --style <name> with slide or slide-batch.

| Category | Styles | |----------|--------| | Academic | scientific, biotech, neuroscience, ml-ai, environmental | | Professional | corporate, minimal, notion, bold-editorial | | Creative | watercolor, sketch-notes, pixel-art, fantasy-animation | | Premium | tech-keynote, creative-bold, financial-elite | | Specialized | blueprint, chalkboard, dark-atmospheric, vintage, editorial-infographic, vector-illustration, intuition-machine |

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Evaluation Infrastructure

PaperBanana v4.0 includes a complete evaluation system for measuring and improving figure quality:

evaluation/
├── checklist.py          # 6-item binary pass/fail evaluator
├── judge.py              # VLM-as-Judge comparative evaluation
├── benchmark.py          # End-to-end benchmark harness
└── prompt_ablation.py    # A/B prompt comparison runner

scripts/
├── run_checklist_baseline.py   # Run checklist on existing outputs
└── autoresearch_loop.py        # Automated prompt optimization

Run your own baseline:

python scripts/run_checklist_baseline.py --output-dir outputs/ --report baseline.json

Run autoresearch optimization:

python scripts/autoresearch_loop.py --test-inputs data/checklist_test_set --max-rounds 10 --target 90

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Troubleshooting

| Problem | Solution | |---------|----------| | "API key not found" | Run setup or check .env in paperbanana directory | | "Image generation failed" | Check provider supports image gen (Claude VLM does not) | | "Critic parse error" | v4.0 marks output as UNREVIEWED instead of silent approval | | Output marked UNREVIEWED | Critic couldn't evaluate — review the figure manually | | Windows Unicode errors | Upgrade PaperBanana (git pull in project directory) | | Slow generation | Use --venue to skip Retriever, or reduce --iterations |

Contributing

Contributions welcome! See the Contributing Guide.

License

MIT