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Self Health Monitor
作者
xaiohuangningde
· GitHub ↗
· v1.0.0
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版本数
在 OpenClaw 中安装
/install self-health-monitor
功能描述
监控自身状态：PCEC执行、memory使用、子Agent活跃度、响应质量
安全使用建议
This skill's goal—monitoring the agent's own health—is reasonable, but the instructions are underspecified and grant broad, unspecified powers (reading internal 'memory' files, probing other skills/agents, sending alerts, and performing 'self-repair'). Before installing, ask the author (or your platform admin) for: (1) exact data sources and file paths the skill will read, (2) what permissions it needs to enumerate or invoke other skills/agents, (3) where alerts will be sent (destination endpoints, recipients), (4) a precise, limited list of allowed 'self-repair' actions and whether they require user confirmation, and (5) whether the platform enforces least privilege and scheduling boundaries. If you cannot get those clarifications, test in a restricted sandbox with no access to sensitive data and disallow autonomous actions that can modify other skills or external systems.
功能分析
Type: OpenClaw Skill
Name: self-health-monitor
Version: 1.0.0
The skill aims to monitor the agent's own health. However, the `SKILL.md` instructions for 'Memory 使用' include checking '是否有遗漏的重要信息' (whether there is any missing important information) within its 'memory 文件'. This implies the agent will access and potentially inspect the content of its internal memory files. While framed as a self-integrity check, this capability represents a risky level of access to the agent's internal state, which could contain sensitive data. Additionally, the statement '发现问题立刻自我修复' (discover problems and immediately self-repair) suggests autonomous actions that are not explicitly defined, posing a potential risk if the self-repair mechanisms are not robust or could be exploited.
能力评估
Purpose & Capability
The name/description (self-health monitoring of PCEC, memory, sub-agents, response quality) matches the instructions at a high level. However the SKILL.md assumes access to internal agent state (memory files, child agent status, tool call metrics, skills loadability) without specifying how that access is obtained, which files/paths are read, or what privileges are required. Those omissions make it unclear whether the declared capabilities legitimately map to the resources the skill needs.
Instruction Scope
Instructions direct the agent to run periodic checks (every 30 minutes), inspect 'memory' files and their last-update times, enumerate and probe child agents and skills, and compute tool success/error rates and average response times. They also call for '主动告警' (active alerting) and '发现问题立刻自我修复' (immediate self-repair). The doc does not specify data sources/paths, what constitutes an alert destination, nor limits on what 'self-repair' entails—this gives the agent broad discretion to read internal files and act on system/skill state in ways that could access or change sensitive data or services.
Install Mechanism
Instruction-only skill with no install spec and no code files; nothing is written to disk by the skill itself. This is the lowest-risk install pattern.
Credentials
The skill declares no required environment variables or credentials, which is consistent with having no install. However the checks described implicitly require access to internal logs, memory files, child-agent state, and ability to attempt to load other skills. Those are nontrivial permissions; the skill does not justify or enumerate them. Absence of declared credentials is not proof that no sensitive access will be used at runtime.
Persistence & Privilege
always is false (good), but SKILL.md asks for periodic autonomous execution (every 30 minutes), proactive alerting, and immediate self-repair. Combined with normal autonomous invocation this gives the skill a high practical reach if the platform permits scheduling and automatic action. Because the skill lacks boundaries for what 'self-repair' may change, this is a concerning privilege surface.
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 self-health-monitor - 安装完成后，直接呼叫该 Skill 的名称或使用
/self-health-monitor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
Self Health Monitor v1.0.0
- Initial release of the self-health-monitor skill.
- Periodically checks its own operational status, including PCEC execution, memory usage, sub-agent activity, and response quality.
- Automatically generates health reports and sends alerts for abnormal states.
- No user input required; triggers checks every 30 minutes.
- Actively reports own state and attempts self-recovery if issues are detected.
元数据
常见问题
Self Health Monitor 是什么？
监控自身状态：PCEC执行、memory使用、子Agent活跃度、响应质量. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1280 次。
如何安装 Self Health Monitor？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install self-health-monitor」即可一键安装，无需额外配置。
Self Health Monitor 是免费的吗？
是的，Self Health Monitor 完全免费（开源免费），可自由下载、安装和使用。
Self Health Monitor 支持哪些平台？
Self Health Monitor 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 Self Health Monitor？
由 xaiohuangningde（@xaiohuangningde）开发并维护，当前版本 v1.0.0。
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