Oraclaw Graph
技能 活跃AI 智能体所需的网络智能。为任何已连接事物的图提供 PageRank、社区检测(Louvain)、关键路径和瓶颈分析。
为 AI 智能体提供高级网络分析能力,使他们能够理解连接数据的结构、影响和关键组成部分。
功能
- PageRank 分数计算
- Louvain 社区检测
- 关键路径分析
- 瓶颈节点识别
- 决策图分析
使用场景
- 查找网络中最具影响力的节点
- 将相关项目聚类分组
- 确定点之间的关键路径
- 识别工作流中的瓶颈节点
非目标
- 通用数据可视化
- 实时网络监控
- 图操作(添加/删除节点/边)
文档
- warning:配置和参数参考SKILL.md 列出了 `ORACLAW_API_KEY` 作为必需的环境变量,但没有说明如何获取它或其必需的范围,也没有详细说明其他配置参数。
安全
- warning:秘密管理SKILL.md 指定 `ORACLAW_API_KEY` 是必需的环境变量,但 README 和 SKILL.md 没有详细说明如何获取此密钥或其范围,增加了错误配置或处理不当的风险。
安装
- warning:安装说明虽然提供了 MCP 服务器和 REST API 的安装说明,但提到了需要 `ORACLAW_API_KEY`,但没有提供如何获取该密钥或其必要范围的清晰步骤。
实用性
- info:使用示例尽管 README 提供了 REST API 和 SDK 的示例,并且 SKILL.md 列出了节点/边类型,但在技能上下文中没有 `analyze_decision_graph` 工具的端到端、可直接复制粘贴的示例。
- info:边缘情况SKILL.md 提到了节点和边类型以及基本要求,但没有明确记录格式错误的输入或未满足的依赖项的失败模式或恢复步骤。
安装
npx skills add Whatsonyourmind/oraclaw通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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