Alert Root Cause Analysis
SkillMonitoring & opsPerforms root cause analysis on active alerts, correlating metrics and inspection records.
Use Alert Root Cause Analysis in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Alert Root Cause Analysis skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by kubehan/promai in skills/alert-root-cause/SKILL.md and read by Ahel’s review.
当用户询问告警原因、触发根因、或要求分析某个告警时,使用此工作流。
前置条件
用户通常会说 "分析一下这个告警" 或 "为什么 CPU 告警了"。先使用已知的 metric_name 或从上下文推断。
步骤
1. 使用 analyze_alert 工具
这是分析告警的核心工具。直接传入指标名称:
analyze_alert(metric_name="node_cpu_seconds_total")
如果用户提到了具体实例,带上 instance:
analyze_alert(metric_name="node_memory_MemAvailable_bytes", instance="192.168.1.100:9100")
2. 查询关联指标
analyze_alert 返回后,根据结果补充查询关联指标:
如果是 CPU 告警,检查负载:
node_load1
如果是内存告警,检查 OOM:
(node_memory_Active_bytes / node_memory_MemTotal_bytes) * 100
如果是磁盘告警,检查 inode:
(node_filesystem_files_free{fstype!=""} / node_filesystem_files{fstype!=""}) * 100
3. 查看巡检记录
使用 list_reports 获取近期巡检报告:
list_reports(status="problem", page_size=5)
如果找到相关报告,用 get_report_detail 查看详情:
get_report_detail(report_id=123)
4. 汇总分析
- 对比告警触发前后的指标变化趋势
- 结合巡检报告中的异常发现
- 给出根因判断和处理建议
- 如果确认是严重问题,使用
push_report通知值班人员
常见场景示例
CPU 持续高负载
analyze_alert(metric_name="node_cpu_seconds_total")query_metrics(promql="topk(5, sum by (process) (rate(node_procs_running{}[5m])))")- 建议使用
exec确认是否有异常进程
Signals
- GitHub stars
- 124
- Forks
- 44
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
alert-root-cause- Source
- github.com/kubehan/promai
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