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Install in OpenClaw
/install paperbanana
Description
Generate publication-quality academic diagrams from paper methodology text
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install paperbanana - After installation, invoke the skill by name or use
/paperbanana - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release: multi-agent academic diagram generation from paper methodology text
Metadata
Frequently Asked Questions
What is PaperBanana?
Generate publication-quality academic diagrams from paper methodology text. It is an AI Agent Skill for Claude Code / OpenClaw, with 599 downloads so far.
How do I install PaperBanana?
Run "/install paperbanana" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is PaperBanana free?
Yes, PaperBanana is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does PaperBanana support?
PaperBanana is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created PaperBanana?
It is built and maintained by dwzhu-pku (@dwzhu-pku); the current version is v0.1.0.
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