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/monitoring-expert

@efebc44
by jeffallanjeffallan/claude-skills12k stars
1,124

Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.

Use this Skill: https://skilld.dev/gh/jeffallan/claude-skills/monitoring-expert

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referencesdashboards.md

≈953 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Dashboards

RED Method (Request-focused)

Rate     - Requests per second
Errors   - Failed requests per second
Duration - Response time distribution
# Rate
sum(rate(http_requests_total[5m]))

# Errors
sum(rate(http_requests_total{status=~"5.."}[5m]))
  /
sum(rate(http_requests_total[5m]))

# Duration (p95)
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

USE Method (Resource-focused)

Utilization - % time resource is busy
Saturation  - Queue depth, backlog
Errors      - Error events
# CPU Utilization
100 - (avg(rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# Memory Saturation
node_memory_SwapTotal_bytes - node_memory_SwapFree_bytes

# Disk Errors
rate(node_disk_io_time_weighted_seconds_total[5m])

Dashboard Structure

┌─────────────────────────────────────────────────────────────┐
│                    SERVICE OVERVIEW                         │
│  Request Rate │ Error Rate │ p50 Latency │ p99 Latency     │
├─────────────────────────────────────────────────────────────┤
│                    REQUEST METRICS                          │
│  [Graph: Requests/s by endpoint]                           │
│  [Graph: Error rate over time]                             │
├─────────────────────────────────────────────────────────────┤
│                    LATENCY METRICS                          │
│  [Heatmap: Latency distribution]                           │
│  [Graph: p50, p95, p99 over time]                          │
├─────────────────────────────────────────────────────────────┤
│                    INFRASTRUCTURE                           │
│  CPU │ Memory │ Disk │ Network                             │
└─────────────────────────────────────────────────────────────┘

Key Panels

Stat Panel (Single Value)

# Current RPS
sum(rate(http_requests_total[5m]))

# Error percentage
sum(rate(http_requests_total{status=~"5.."}[5m]))
  /
sum(rate(http_requests_total[5m])) * 100

Time Series

# Requests by status
sum by (status) (rate(http_requests_total[5m]))

# Latency percentiles
histogram_quantile(0.50, rate(http_request_duration_seconds_bucket[5m]))
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

Table

# Top endpoints by error rate
topk(10,
  sum by (path) (rate(http_requests_total{status=~"5.."}[5m]))
  /
  sum by (path) (rate(http_requests_total[5m]))
)

Business Metrics Dashboard

# Orders per minute
sum(rate(orders_created_total[5m])) * 60

# Revenue (if tracked)
sum(increase(order_value_dollars_sum[1h]))

# Active users (gauge)
active_users_total

Quick Reference

Method Focus Metrics
RED Services Rate, Errors, Duration
USE Resources Utilization, Saturation, Errors
Panel Type Use Case
Stat Single KPI
Time Series Trends over time
Heatmap Latency distribution
Table Top N, details
Gauge Current vs threshold

Source: SKILL.md on GitHub

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Signed by skilld at efebc44. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 months ago.

Steadyupdated 5 months ago
Other metadata
metadata
{
  "author": "https://github.com/Jeffallan",
  "version": "1.1.0",
  "domain": "devops",
  "triggers": "monitoring, observability, logging, metrics, tracing, alerting, Prometheus, Grafana, DataDog, APM, performance testing, load testing, profiling, capacity planning, bottleneck",
  "role": "specialist",
  "scope": "implementation",
  "output-format": "code",
  "related-skills": "devops-engineer, debugging-wizard, architecture-designer"
}

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