Query Prometheus metrics to diagnose service issues via PromQL.
npx skills add https://github.com/XiaoMi/mone --skill prometheus-skill
Please help me install this Agent Skill. Skill: prometheus-skill Source: https://github.com/XiaoMi/mone/tree/main/jcommon/skills/prometheus-skill Command: npx skills add https://github.com/XiaoMi/mone --skill prometheus-skill
This Skill helps you diagnose service issues by querying Prometheus metrics and analyzing resource usage across your applications.
Run Prometheus queries with the provided scripts, for example: uv run prometheus.py 'sum(rate(container_cpu_user_seconds_total{image!="",application="your_app"}[30s])) by(application) * 100' Then use the output to determine if the service health is degraded.
High-intent search queries and answers about installing and using this skill.
Query Prometheus metrics using PromQL to diagnose service health by retrieving CPU usage, memory usage, load, and heap metrics. Execute prometheus.py with PrometheusUtil to fetch the latest values and compare them against baselines to assess whether resource consumption indicates degraded service performance.
Prometheus monitoring supports CPU usage, CPU quota, memory usage, memory limits, load average, HeapUsed, and HeapMax metrics. These metrics apply to application-labeled services in Prometheus-monitored environments and enable rapid identification of resource bottlenecks causing latency or performance issues.
Yes, automated monitoring checks query Prometheus metrics to assess service health by pulling current CPU, memory, load, and heap values via PromQL. The Skill constructs PromQL queries, executes them through PrometheusUtil, and generates a concise health report for rapid issue diagnosis.
Yes, this Skill requires services labeled with application metadata within a Prometheus-monitored environment. The Skill queries existing Prometheus instances using PromQL and applies only to environments where metrics are already collected and accessible.
PromQL queries retrieve live CPU and memory metrics, allowing you to compare current resource usage against historical baselines. This comparison reveals whether elevated CPU or memory consumption correlates with service latency, enabling targeted performance diagnosis without manual log inspection.
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