Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PlutoLei/paperbanana-skill --skill paperbananagit clone --depth 1 https://github.com/PlutoLei/paperbanana-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/plutolei/paperbanana-skill/paperbanana)<a href="https://agentmods.dev/skills/plutolei/paperbanana-skill/paperbanana"><img src="https://agentmods.dev/badge/skills/plutolei/paperbanana-skill/paperbanana/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/plutolei/paperbanana-skill/paperbanana"><img src="https://agentmods.dev/badge/skills/plutolei/paperbanana-skill/paperbanana.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.05439 |
| Opus 5 | $0.00000 | $0.02720 |
| Sonnet 5 | $0.00000 | $0.01088 |
| Haiku 4.5 | $0.00000 | $0.00544 |
Grade A, and why
paperbanana scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to paperbanana — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: paperbanana description: Use when user needs academic diagrams, methodology figures, statistical plots, or presentation slides from text descriptions or data files. Also use for evaluating generated figures against references. argument-hint: [generate|plot|slide|slide-batch|evaluate|data|setup] [description or file path] allowed-tools: Read, Write, Bash, Glob, Grep, AskUserQuestion
PaperBanana - Academic Illustration Generator
Multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic) for publication-quality academic diagrams, statistical plots, and presentation slides.
API key: Set provider keys in PaperBanana project's .env file.
Timeout: 300000 (5 min) for all generation commands.
Commands
All commands run from project root: cd <paperbanana_dir> && python -m paperbanana.cli <cmd>
Command Selection Decision Tree
Route user requests to the right subcommand before looking up parameters:
| User intent | Signal words | Subcommand |
|---|---|---|
| 方法论/架构/流程图 from text or PDF | "method figure", "架构图", "流程图", "methodology", "pipeline diagram", "论文配图" | generate |
| Statistical plot from data file | "plot", "curve", "bar chart", "scatter", "heatmap", has CSV/JSON | plot |
| Single presentation slide | "slide", "一张幻灯片", "封面图", single prompt file | slide |
| Batch slide generation | "all slides", "批量生成", "N 张幻灯片", prompts/ directory |
slide-batch |
| Compare generated vs human reference | "evaluate", "对比", "与参考图对比" | evaluate |
| Manage reference dataset | "download dataset", "清缓存" | data |
| First-time provider config | "setup", "配置 API key" | setup |
Ambiguous input: If user provides just a description with no subcommand signal, default to generate (see Argument Parsing table for details).
Out-of-scope: Pure code generation (matplotlib/seaborn script) is NOT paperbanana's job — those go to matplotlib / scientific-visualization skills. Paperbanana is for AI-driven image generation + critique loops.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 355 lines · 0 tokens per session scan A 04df3970aadd
paperbanana is a skill published in the GitHub repository PlutoLei/paperbanana-skill (47 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,439 tokens. A static security scan graded it A with 0 findings. It is 100% identical to paperbanana, differing in 0 lines, and is treated as a copy.
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