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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/chaterm/terminal-skills --skill monitoring- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads monitoring automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
监控与告警
概述
Prometheus、Grafana、告警规则配置等技能。
Prometheus
基础查询（PromQL）
# 即时向量
http_requests_total
http_requests_total{job="api", status="200"}
# 范围向量
http_requests_total[5m]
# 偏移
http_requests_total offset 1h
# 聚合
sum(http_requests_total)
sum by (job) (http_requests_total)
sum without (instance) (http_requests_total)
# 速率
rate(http_requests_total[5m])
irate(http_requests_total[5m])
# 增量
increase(http_requests_total[1h])
# 直方图分位数
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))
常用查询
# CPU 使用率
100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
# 内存使用率
(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100
# 磁盘使用率
(1 - node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100
# 网络流量
rate(node_network_receive_bytes_total[5m])
rate(node_network_transmit_bytes_total[5m])
# HTTP 请求速率
sum(rate(http_requests_total[5m])) by (status)
# 错误率
sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))
# 延迟 P99
histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))
配置文件
# prometheus.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
alerting:
alertmanagers:
- static_configs:
- targets:
- alertmanager:9093
rule_files:
- "rules/*.yml"
scrape_configs:
- job_name: 'prometheus'
static_configs:
- targets: ['localhost:9090']
- job_name: 'node'
static_configs:
- targets: ['node1:9100', 'node2:9100']
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
告警规则
# rules/alerts.yml
groups:
- name: node
rules:
- alert: HighCPUUsage
expr: 100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
for: 5m
labels:
severity: warning
annotations:
summary: "High CPU usage on {{ $labels.instance }}"
description: "CPU usage is {{ $value }}%"
- alert: HighMemoryUsage
expr: (1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100 > 85
for: 5m
labels:
severity: warning
annotations:
summary: "High memory usage on {{ $labels.instance }}"
- alert: DiskSpaceLow
expr: (1 - node_filesystem_avail_bytes / node_filesystem_size_bytes) * 100 > 85
for: 5m
labels:
severity: critical
annotations:
summary: "Disk space low on {{ $labels.instance }}"
- name: application
rules:
- alert: HighErrorRate
expr: sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate"
description: "Error rate is {{ $value | humanizePercentage }}"
- alert: HighLatency
expr: histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) > 1
for: 5m
labels:
severity: warning
annotations:
summary: "High latency"
Alertmanager
配置
# alertmanager.yml
global:
smtp_smarthost: 'smtp.example.com:587'
smtp_from: 'alertmanager@example.com'
smtp_auth_username: 'alertmanager@example.com'
smtp_auth_password: 'password'
route:
group_by: ['alertname', 'severity']
group_wait: 30s
group_interval: 5m
repeat_interval: 4h
receiver: 'default'
routes:
- match:
severity: critical
receiver: 'pagerduty'
- match:
severity: warning
receiver: 'slack'
receivers:
- name: 'default'
email_configs:
- to: 'team@example.com'
- name: 'slack'
slack_configs:
- api_url: 'https://hooks.slack.com/services/xxx'
channel: '#alerts'
title: '{{ .GroupLabels.alertname }}'
text: '{{ range .Alerts }}{{ .Annotations.summary }}{{ end }}'
- name: 'pagerduty'
pagerduty_configs:
- service_key: 'xxx'
inhibit_rules:
- source_match:
severity: 'critical'
target_match:
severity: 'warning'
equal: ['alertname', 'instance']
Grafana
数据源配置
# provisioning/datasources/prometheus.yml
apiVersion: 1
datasources:
- name: Prometheus
type: prometheus
access: proxy
url: http://prometheus:9090
isDefault: true
editable: false
Dashboard JSON 示例
{
"dashboard": {
"title": "Node Metrics",
"panels": [
{
"title": "CPU Usage",
"type": "graph",
"targets": [
{
"expr": "100 - (avg by (instance) (irate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)",
"legendFormat": "{{ instance }}"
}
]
},
{
"title": "Memory Usage",
"type": "gauge",
"targets": [
{
"expr": "(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100"
}
]
}
]
}
}
常用面板查询
# CPU 使用率（时间序列）
100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)
# 内存使用（仪表盘）
(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100
# 请求速率（柱状图）
sum(rate(http_requests_total[5m])) by (status)
# 延迟热力图
sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
常见场景
场景 1：Kubernetes 监控
# ServiceMonitor
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: app-monitor
spec:
selector:
matchLabels:
app: myapp
endpoints:
- port: metrics
interval: 15s
path: /metrics
场景 2：自定义指标
# Python 应用
from prometheus_client import Counter, Histogram, start_http_server
REQUEST_COUNT = Counter('http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status'])
REQUEST_LATENCY = Histogram('http_request_duration_seconds', 'HTTP request latency', ['method', 'endpoint'])
@REQUEST_LATENCY.labels(method='GET', endpoint='/api').time()
def handle_request():
REQUEST_COUNT.labels(method='GET', endpoint='/api', status='200').inc()
# ...
start_http_server(8000)
场景 3：SLO 监控
# 可用性 SLO (99.9%)
1 - (sum(rate(http_requests_total{status=~"5.."}[30d])) / sum(rate(http_requests_total[30d])))
# 错误预算消耗
(1 - (sum(rate(http_requests_total{status=~"5.."}[7d])) / sum(rate(http_requests_total[7d])))) / 0.999
# 延迟 SLO (P99 < 500ms)
histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[30d])) by (le)) < 0.5
场景 4：告警静默
# 创建静默
amtool silence add alertname=HighCPUUsage instance=node1 --duration=2h --comment="Maintenance"
# 查看静默
amtool silence query
# 删除静默
amtool silence expire <silence-id>
故障排查
| 问题 | 排查方法 |
|---|---|
| 指标缺失 | 检查 scrape 配置、target 状态 |
| 告警不触发 | 检查规则语法、Alertmanager 配置 |
| 查询慢 | 优化 PromQL、增加采样间隔 |
| 存储满 | 调整 retention、清理旧数据 |
# 检查 Prometheus targets
curl http://prometheus:9090/api/v1/targets
# 检查告警规则
curl http://prometheus:9090/api/v1/rules
# 检查 Alertmanager 状态
curl http://alertmanager:9093/api/v1/status
# 测试 PromQL
curl 'http://prometheus:9090/api/v1/query?query=up'
FAQ
What does the monitoring skill do?
监控与告警
How do I install monitoring?
Run npx skills add https://github.com/chaterm/terminal-skills --skill monitoring in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/monitoring/ (personal) or .claude/skills/monitoring/ (project).
Which AI agents can use monitoring?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is monitoring safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains monitoring?
It is published by chaterm in chaterm/terminal-skills. It has 68 recorded installs.
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