Datasource Health Check
SkillMonitoring & opsChecks the connectivity, data latency, and metric collection health of a Prometheus data source.
Use Datasource Health Check in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Datasource Health Check and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Datasource Health Check 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/datasource-check/SKILL.md and read by Ahel’s review.
当用户询问数据源状态、Prometheus 是否正常、数据是否有延迟时,使用此工作流。
步骤
1. 列出数据源
查看当前所有已配置的数据源:
list_datasources()
关注 enabled 字段,只对已启用的数据源检查。
2. 检查数据源可用性
对每个数据源执行一个轻量级查询,验证其响应能力:
query_metrics(datasource="<datasource_name>", promql="up")
如果返回空值或报错,说明该数据源不可达。
3. 检查数据延迟
查询最新数据点的时间戳与当前时间的差距:
time() - max by (instance) (node_boot_time_seconds)
更通用的延迟检查:
time() - max by (job) (scrape_duration_seconds)
如果数据延迟超过 5 分钟,标记为异常。
4. 检查 target 状态
检查 up 指标,确认有多少 target 在线:
count by (job) (up == 1)
对比:
count by (job) (up == 0)
5. 结果汇总
对每个数据源给出:
- 状态:正常 / 延迟 / 不可达
- Target 在线率:正常 target / 总 target
- 数据延迟:X 分钟
如果发现数据源不可达或大量 target 下线,使用 push_report 通知管理员。
Signals
- GitHub stars
- 124
- Forks
- 44
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
datasource-check- Source
- github.com/kubehan/promai
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