Log Analysis

When you're staring at a wall of application logs trying to figure out why something broke at 3am, this walks you through structured logging patterns that actually parse well. It covers when to use different log levels, setting up JSON formatted logs for tools like Elasticsearch, and includes practical query examples for common debugging scenarios. The emphasis on structured over unstructured logging is spot on, because grep only gets you so far when you need to correlate events across distributed services. Includes references for log aggregation tools and patterns for security incidents, performance issues, and user action auditing.

skills.sh installs: 674
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill log-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Logs are critical for debugging and monitoring. Effective log analysis quickly identifies issues and enables root cause analysis.

When to Use

Troubleshooting errors
Performance investigation
Security incident analysis
Auditing user actions
Monitoring application health

Quick Start

Minimal working example:

// Good: Structured logs (machine-readable)
logger.info({
  level: 'INFO',
  timestamp: '2024-01-15T10:30:00Z',
  service: 'auth-service',
  user_id: '12345',
  action: 'user_login',
  status: 'success',
  duration_ms: 150,
  ip_address: '192.168.1.1'
});

// Bad: Unstructured logs (hard to parse)
console.log('User 12345 logged in successfully in 150ms from 192.168.1.1');

SKILL.md on GitHub

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