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/prometheus-monitoring

Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.

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useful-ai-prompts
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Install
$ npx -y skills add aj-geddes/useful-ai-prompts --skill prometheus-monitoring --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/prometheus-monitoring

Context preview

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Set up Prometheus monitoring for applications with custom metrics, scraping configurations, and service discovery. Use when implementing time-series metrics collection, monitoring applications, or building observability infrastructure.

SKILL.md

prometheus-monitoring.SKILL.md
name: prometheus-monitoring
description: >
  Set up Prometheus monitoring for applications with custom metrics, scraping
  configurations, and service discovery. Use when implementing time-series
  metrics collection, monitoring applications, or building observability
  infrastructure.

Prometheus Monitoring

Table of Contents

  • [Overview](#overview)
  • [When to Use](#when-to-use)
  • [Quick Start](#quick-start)
  • [Reference Guides](#reference-guides)
  • [Best Practices](#best-practices)

Overview

Implement comprehensive Prometheus monitoring infrastructure for collecting, storing, and querying time-series metrics from applications and infrastructure.

When to Use

  • Setting up metrics collection
  • Creating custom application metrics
  • Configuring scraping targets
  • Implementing service discovery
  • Building monitoring infrastructure

Quick Start

Minimal working example:

# /etc/prometheus/prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s
  external_labels:
    cluster: production

alerting:
  alertmanagers:
    - static_configs:
        - targets: ["localhost:9093"]

rule_files:
  - "/etc/prometheus/alert_rules.yml"

scrape_configs:
  - job_name: "prometheus"
    static_configs:
      - targets: ["localhost:9090"]

  - job_name: "node"
    static_configs:
      - targets: ["localhost:9100"]

  - job_name: "api-service"
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [Prometheus Configuration](references/prometheus-configuration.md) | Prometheus Configuration | | [Node.js Metrics Implementation](references/nodejs-metrics-implementation.md) | Node.js Metrics Implementation | | [Python Prometheus Integration](references/python-prometheus-integration.md) | Python Prometheus Integration | | [Alert Rules](references/alert-rules.md) | Alert Rules | | [Docker Compose Setup](references/docker-compose-setup.md) | Docker Compose Setup |

Best Practices

✅ DO

  • Use consistent metric naming conventions
  • Add comprehensive labels for filtering
  • Set appropriate scrape intervals (10-60s)
  • Implement retention policies
  • Monitor Prometheus itself
  • Test alert rules before deployment
  • Document metric meanings

❌ DON'T

  • Add unbounded cardinality labels
  • Scrape too frequently (< 10s)
  • Ignore metric naming conventions
  • Create alerts without runbooks
  • Store raw event data in Prometheus
  • Use counters for gauge-like values
Read more
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488 production-ready AI prompts, all following a standardized template with validated quality gates. Transform ChatGPT, Claude, and other AI assistants into expert consultants.

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