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在 OpenClaw 中安装
/install pharma-ai
功能描述
智能药物发现AI助手，提供分子毒性预测、ADMET评估和虚拟筛选功能。 基于Python科学计算核心(RDKit + scikit-learn)和Node.js Skill包装。 Use when: - 需要预测分子的hERG心脏毒性、肝毒性或Ames致突变性 - 需要评估分子的溶解度、代谢稳定性等ADMET性质...
安全使用建议
This package mostly looks like a local Node+Python prediction tool, but do NOT install blindly. Key concerns to resolve before use: 1) The manifest/README reference pre-trained model files (python-core/models/*.pkl) that are not included — verify the models are present from a trusted source or the skill will fail. 2) The skill requires python3 and native Python packages (RDKit) which must be installed separately; RDKit has non-trivial native dependencies—test in an isolated environment. 3) The Python bridge spawns local processes and runs bundled Python code — review the Python files and any model-loading logic for safety and provenance. 4) The model metric claims (two models with ROC-AUC=1.000) are suspiciously perfect; ask the author for provenance, training data, and validation details before trusting predictions for critical decisions. If you cannot verify the models' origin and integrity, run the skill in a sandbox or decline to install.
功能分析
Type: OpenClaw Skill
Name: pharma-ai
Version: 1.0.0
The pharma-ai skill bundle is a legitimate tool for molecular toxicity prediction and drug discovery. It uses a Node.js wrapper to call a Python core (RDKit/scikit-learn) via a secure child process bridge using JSON-serialized arguments. The documentation files (HEADLESS_LOGIN.md, PUBLISH_GUIDE.md) provide standard instructions for platform authentication and deployment, and no evidence of data exfiltration, malicious execution, or prompt injection was found in the code or instructions.
能力评估
Purpose & Capability
The name/description, TypeScript code, and Python core align with a molecular-toxicity/ADMET prediction skill. However the bundle advertises pre-trained models (e.g. herg_model.pkl, hepatotoxicity_model.pkl, ames_model.pkl) and references additional docs, but those model files and some referenced docs are not present in the provided file manifest. The package also does not declare runtime requirements (python3, RDKit) or list model files as required assets — this is disproportionate to the claimed out-of-the-box capability.
Instruction Scope
Runtime instructions and code confine behaviour to local computation: the Node layer spawns a local python3 process and passes JSON; the Python script loads local model files, computes descriptors with RDKit, and returns JSON. There are no network calls, no attempts to read arbitrary system files, and no exfiltration endpoints in the code. That said, documentation files include publishing/login guidance referencing ClawHub tokens (for publishing) which is unrelated to runtime prediction and should not be confused with required credentials.
Install Mechanism
There is no install spec and the skill expects heavy native Python packages (RDKit, scikit-learn, numpy, joblib). The bundle does not ship platform installers or the model artifacts; running it will require manual installation of RDKit (which has non-trivial native dependencies). The absence of packaged models means either the skill will fail at runtime or the author expects models to be fetched/added outside the bundle (no code for fetching exists). This mismatch increases operational risk.
Credentials
The skill does not request environment variables, credentials, or config paths for runtime operation (none declared). The only place tokens are mentioned is in publishing docs (HEADLESS_LOGIN.md / PUBLISH_GUIDE.md) describing how to publish the skill to ClawHub; those are documentation for authors and are not used by the runtime code. No other unrelated credentials are requested.
Persistence & Privilege
The skill does not request always:true and does not attempt to modify other skills or global agent configuration. It runs as a local Node/Python process when invoked. Autonomous invocation is allowed (platform default) but does not combine with other privilege red flags in the bundle.
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 pharma-ai - 安装完成后，直接呼叫该 Skill 的名称或使用
/pharma-ai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
v1.0.0
Initial release: Molecular toxicity prediction, ADMET assessment, and virtual screening
元数据
常见问题
PharmaAI 是什么？
智能药物发现AI助手，提供分子毒性预测、ADMET评估和虚拟筛选功能。 基于Python科学计算核心(RDKit + scikit-learn)和Node.js Skill包装。 Use when: - 需要预测分子的hERG心脏毒性、肝毒性或Ames致突变性 - 需要评估分子的溶解度、代谢稳定性等ADMET性质... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 280 次。
如何安装 PharmaAI？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install pharma-ai」即可一键安装，无需额外配置。
PharmaAI 是免费的吗？
是的，PharmaAI 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
PharmaAI 支持哪些平台？
PharmaAI 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 PharmaAI？
由 xxxrobot（@xxxrobot）开发并维护，当前版本 v1.0.0。
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