No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mituan-ai · MCP Server · ★ 72
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PaperBanana-CN safe to install? View the security audit →
English · 简体中文 PaperBanana-CN generates methodology diagrams from research descriptions and statistical plots from CSV or JSON data. It uses the PaperBanana scientific workflow and adds separate VLM and image-service connections, a Chinese Studio interface, and explicit aspect-ratio and resolution controls. <img src="https://img.
| Stars | 72 |
| Forks | 6 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 57.1638194426598/100 |
| Open Issues | 7 |
| Last Updated | 2026-08-06 |
| Created | 2026-04-24 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
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PaperBanana-CN is Scientific figure workbench with independent VLM/image connections, Chinese UI, and unified size controls.. It is categorized as a MCP Server with 72 GitHub stars.
PaperBanana-CN is primarily written in Python. It covers topics such as academic-figures, bilingual, gradio.
You can find installation instructions and usage details in the PaperBanana-CN GitHub repository at github.com/mituan-ai/PaperBanana-CN. The project has 72 stars and 6 forks, indicating an active community.
PaperBanana-CN is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to PaperBanana-CN on Agent Skills Hub include chibi, claude-context-local, comfy-pilot. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: