Skill 审计报告
designing-data-intensive-applications 审计报告.
Apply Martin Kleppmann-inspired data-system rules when designing reliability, scalability, consistency, replication, transactions, streams, or schema evolution.
OpenAgentSkill 信任评分
OpenAgentSkill 信任评分
Trust Score 帮助 Agent 在安装前判断一个 Skill 是否足以进入候选清单。
GitHub 采用度
通过86
2.8K 个 GitHub Stars
Star/Fork 活跃度
通过83
2.8K 个 Star,490 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过88
距上次推送 1 个月
许可证清晰度
通过86
MIT
README/SKILL.md 完整度
信息76
公开元数据需要更完整的 README/SKILL.md 上下文
依赖与运行时风险
通过90
公开元数据中未发现主要依赖风险提示
安装可用性
通过92
npx skills add ciembor/agent-rules-books --skill designing-data-intensive-applications
安装命令安全性
通过92
标准软件包或运行时安装路径
权限范围
通过88
数据库访问
仓库证据
通过86
https://github.com/ciembor/agent-rules-books/tree/main/designing-data-intensive-applications
审查状态
信息66
可用 AI 审查数据
Agent 验证结果
信息54
暂未有 Agent 结果数据
检查项
安装与采用审查
安装路径
92
npx skills add ciembor/agent-rules-books --skill designing-data-intensive-applications
仓库
88
https://github.com/ciembor/agent-rules-books/tree/main/designing-data-intensive-applications
许可证
86
MIT
维护
88
距上次推送 1 个月
AI 审查
55
No critical security risks found; the skill contains static reference material and does not instruct code execution, downloads, secret access, or destructive actions.
README/SKILL.md 完整度
84
Usable description available
依赖风险
90
公开元数据中未发现主要依赖风险提示
安装命令安全性
92
标准软件包或运行时安装路径
权限范围
88
数据库访问
Star/Fork 活跃度
83
2.8K 个 Star,490 个 Fork; 当前元数据中没有议题活跃度信息
采用度
88
2.8K 个 GitHub Stars
警告
- No critical security risks found; the skill contains static reference material and does not instruct code execution, downloads, secret access, or destructive actions.
- SKILL.md is quite thin and mostly points to supporting files; the core workflow, expected output format, and limitations are not fully stated in SKILL.md itself.
- The relationship between the .mini.md, .md, and .nano.md files could be clearer, especially regarding when to escalate from one to another.
- The imperative 'OBEY' framing is unusual and could be mistaken for an injection-style instruction, though the content itself is benign.
- Quality score needs review
方法
本报告综合公开元数据、AI 审查输出、仓库活跃度、安装就绪度、OpenAgentSkill 事件、质量评分、信任检查和 Agent 安全门槛;它不是完整的源代码安全审计。
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