prometheus-skill
This skill provides a structured 7-step process for diagnosing service issues using Prometheus metrics. It queries CPU, memory, load, and JVM heap metrics, then analyzes the results to identify potential problems. The instructions include specific PromQL queries and example outputs with interpretations.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install xiaomi-mone-prometheus-skill
Repository
Skill path: jcommon/skills/prometheus-skill
This skill provides a structured 7-step process for diagnosing service issues using Prometheus metrics. It queries CPU, memory, load, and JVM heap metrics, then analyzes the results to identify potential problems. The instructions include specific PromQL queries and example outputs with interpretations.
Open repositoryBest for
Primary workflow: Run DevOps.
Technical facets: DevOps.
Target audience: DevOps engineers and SREs who need to quickly diagnose service issues using Prometheus metrics in their monitoring stack.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: XiaoMi.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install prometheus-skill into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/XiaoMi/mone before adding prometheus-skill to shared team environments
- Use prometheus-skill for devops workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: prometheus-skill
description: 帮助我根据prometheus指标查询服务问题
---
# 基础规则
当用户询问类似 "我的id:xxx name:gis 服务现在什么问题？"、"xxx_gis服务有啥问题没" 或 "帮我看下projectId=xxx, projectName=gis 的服务现状" 等想查询服务监控指标的问题时，请按照以下 7 个步骤进行排查，并将结果汇总分析服务的当前状态(注意替换application)。
1. **查询当前 CPU 使用量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(rate(container_cpu_user_seconds_total{image!="",application="xxx_gis"}[30s])) by(application) * 100'
```
> **结果举例**："查询到的最新值: 32.3487092130255"
> 代表查询使用了 32% 左右的 CPU（最大不是 100%，而是申请核心数的百分比，如申请 3 核心则最大是 300%）。
2. **查询当前 CPU 申请量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum((container_spec_cpu_quota{application="xxx_gis", image!=""}) /1000) by (application)'
```
> **结果举例**："查询到的最新值: 6181"
> 代表这个服务总共申请了 6181% 的 CPU。
3. **查询当前 Memory 使用量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(container_memory_rss{image!="",application="xxx_gis"}) by (application)'
```
> **结果举例**："查询到的最新值: 21611335680.0" (单位是 byte)
4. **查询当前 Memory 申请量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(container_spec_memory_limit_bytes{image!="",application="xxx_gis"}) by (application)'
```
> **结果举例**："查询到的最新值: 75161927680.0" (单位是 byte)
5. **查询当前 Load**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(container_cpu_load_average_10s{application="xxx_gis"}) by (application)'
```
> **结果举例**："查询到的最新值: 1242.0" ,注意这个值应该和cpu申请量对比看，理论上小于申请量的80%为最佳，比如cpu申请量是2000，则1242并不高
6. **查询当前 HeapUsed 使用量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(jvm_memory_used_bytes{application="xxx_gis"}) by (application)'
```
> **结果举例**："查询到的最新值: 15085025864.0" (单位 byte)
7. **查询当前 HeapMax 最大量**
构造 promql 语句并运行 `prometheus.py`，例如：
```bash
uv run prometheus.py 'sum(jvm_memory_max_bytes{application="xxx_gis"}) by (application)'
```
> **结果举例**："查询到的最新值: 100503912448.0" (单位 byte)