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在 OpenClaw 中安装
/install paperbanana
功能描述
Generate publication-quality academic diagrams from paper methodology text
安全使用建议
This package appears to implement what it claims, but it is incomplete and has mismatched metadata. Before installing or running:
- Do not provide your OpenRouter or Google API key to this package until you verify the source.
- Inspect the missing files (agents/, utils/, requirements.txt, configs/) — the run.py imports many modules that are not included in the manifest; request the complete source or the official repository URL.
- Be aware run.py will download data from Hugging Face and make network calls to model/image APIs (your input text and any model-generated data will be sent to those services).
- If you must try it, run in an isolated environment (VM/container) and use a limited, low-budget API key with billing caps or a test account.
- Check configs/model_config.template.yaml and any code in the agents/ modules for unexpected external endpoints or telemetry before supplying secrets.
- If the publisher/source cannot be verified or the missing code is not provided, avoid using the skill with sensitive or proprietary text.
功能分析
Type: OpenClaw Skill
Name: paperbanana
Version: 0.1.0
The PaperBanana skill is a multi-agent framework designed to generate academic diagrams from text descriptions, aligned with its stated research purpose. The code in run.py serves as a CLI wrapper that orchestrates several agents and includes a legitimate function to download the PaperBananaBench dataset from HuggingFace (dwzhu/PaperBananaBench). No evidence of data exfiltration, malicious execution, or prompt injection was found in the provided files.
能力评估
Purpose & Capability
The described purpose (turn method text into publication-quality diagrams) matches the runtime behavior in run.py (multi-agent pipeline, image generation). However the registry metadata claims no required environment variables or binaries while SKILL.md and run.py clearly expect model provider API keys (OPENROUTER_API_KEY or GOOGLE_API_KEY), Python runtime, and other project files (configs, agents, utils). That mismatch between metadata and actual instructions is incoherent.
Instruction Scope
SKILL.md instructs pip installing requirements, setting API keys, and possibly downloading a dataset from Hugging Face. The included run.py will copy model config templates, download datasets via huggingface_hub, import many modules under agents/ and utils/ (PlannerAgent, VisualizerAgent, etc.) which are not present in the packaged files. The code calls external model/image APIs (OpenRouter/Gemini) which implies network calls and transmission of input text to third-party services. There is no instruction or code here that reads unrelated system secrets, but the package is incomplete so the full runtime behavior of the missing modules cannot be verified.
Install Mechanism
No install spec is provided in the registry (instruction-only), but SKILL.md instructs 'uv pip install -r requirements.txt' and run.py assumes presence of additional project files (configs, requirements.txt). Those referenced files are not included in the manifest. The lack of packaged dependencies but expectation of network installs and external packages is a packaging/integrity risk.
Credentials
Requesting an API key for an LLM/image provider (OpenRouter or Google/Gemini) is proportionate to a diagram-generation skill. However the registry metadata declared no required env vars while the README and runtime expect credentials — an inconsistency. SKILL.md also suggests storing API keys in configs/model_config.yaml (writing credentials to disk), which users should consider carefully.
Persistence & Privilege
The skill does not request always: true and does not assert elevated or persistent system privileges. It will create files under its work_dir (configs, downloaded datasets, output images) but does not modify other skills or system-wide settings in the provided code.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install paperbanana - 安装完成后，直接呼叫该 Skill 的名称或使用
/paperbanana触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
Initial release: multi-agent academic diagram generation from paper methodology text
元数据
常见问题
PaperBanana 是什么？
Generate publication-quality academic diagrams from paper methodology text. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 599 次。
如何安装 PaperBanana？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install paperbanana」即可一键安装，无需额外配置。
PaperBanana 是免费的吗？
是的，PaperBanana 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
PaperBanana 支持哪些平台？
PaperBanana 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 PaperBanana？
由 dwzhu-pku（@dwzhu-pku）开发并维护，当前版本 v0.1.0。
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