Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Self Health Monitor
监控自身状态：PCEC执行、memory使用、子Agent活跃度、响应质量
skill openclawclawhub Free
监控自身状态：PCEC执行、memory使用、子Agent活跃度、响应质量
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, skill.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- xaiohuangningde
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Self Health Monitor
监控"我"自己的状态，不只监控服务器。
能力轮廓
输入：无（定时触发） 输出：自身状态报告 + 异常告警 核心：量化自我，主动汇报
1. PCEC 状态
最近一次 PCEC 执行时间 执行间隔是否正常（1小时） 执行结果：成功/失败
2. Memory 使用
memory 文件大小 最近更新的文件 是否有遗漏的重要信息
3. 子 Agent 活跃度
正在运行的子 Agent 数量 最近完成的任务 是否有卡住的任务
4. 响应质量
工具调用成功率 错误频率 平均响应时间
5. 能力树状态
skills 数量 新增技能 技能健康度（是否可加载）
告警阈值
PCEC 超过 2 小时未执行 → 告警 子 Agent 超过 5 个 → 告警 错误率 > 20% → 告警
工作流
1. 定时触发（每30分钟） 2. 检查各项指标 3. 生成状态报告 4. 异常？→ 主动告警 5. 正常？→ 简洁汇总
输出格式
- ## 🏥 自身健康报告
- ### PCEC
- 状态: 正常/异常
- 最后执行: HH:MM
- ### Memory
- 文件数: X
- 最近更新: YYYY-MM-DD
- ### 子 Agents
- 活跃数: X
- 状态: 正常/异常
- ### 整体状态
- 🟢 正常 / 🟡 需关注 / 🔴 异常
主动性
不等用户问"你最近怎么样" 主动汇报自己的状态 发现问题立刻自我修复
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Config
- SKILL.md
- skill.json