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Log Aggregation

  • 403 installs
  • 305 repo stars
  • Updated March 4, 2026
  • aj-geddes/useful-ai-prompts

log-aggregation is a Claude agent skill that designs centralized structured logging, collectors, retention policies, and search workflows for developers who need production observability across apps and infrastructure.

About

log-aggregation is an agent skill from useful-ai-prompts that helps developers centralize application and infrastructure logs for production observability. The skill covers structured logging conventions, log collectors, retention policies, and searchable storage so on-call engineers can trace errors across services. Developers reach for log-aggregation when microservices or serverless functions emit scattered stdout logs, when compliance requires retention windows, or when debugging production incidents needs correlated request traces instead of SSH grepping. The skill walks through choosing aggregation backends, defining log schemas, wiring collectors, and configuring retention and access patterns suitable for staging and production environments.

  • Structured JSON logging
  • Collector and shipper setup
  • Index retention policies
  • Correlation with traces and metrics
  • Cost-aware log volume control

Log Aggregation by the numbers

  • 403 all-time installs (skills.sh)
  • Ranked #298 of 1,435 DevOps & CI/CD skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs403
repo stars305
Last updatedMarch 4, 2026
Repositoryaj-geddes/useful-ai-prompts

How do you centralize application logs for production?

Centralize application and infrastructure logs with structured logging, collectors, retention, and search for production observability.

Who is it for?

Backend and platform engineers standing up or refactoring centralized logging for APIs, containers, or serverless workloads in production.

Skip if: Developers who only need local debug logging in a single process without collectors, retention, or production search requirements.

When should I use this skill?

Production logs are scattered across hosts, retention or compliance is undefined, or incident response needs searchable centralized logs.

What you get

Structured logging schema, collector configuration, retention policy, and searchable centralized log store.

  • Logging schema
  • Collector configuration
  • Retention and search setup

Files

SKILL.mdMarkdownGitHub ↗

Log Aggregation

Table of Contents

Overview

Build comprehensive log aggregation systems to collect, parse, and analyze logs from multiple sources, enabling centralized monitoring, debugging, and compliance auditing.

When to Use

  • Centralized log collection
  • Distributed system debugging
  • Compliance and audit logging
  • Security event monitoring
  • Application performance analysis
  • Error tracking and alerting
  • Historical log retention
  • Real-time log searching

Quick Start

Minimal working example:

# docker-compose.yml - ELK Stack setup
version: "3.8"

services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.5.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    ports:
      - "9200:9200"
    volumes:
      - elasticsearch_data:/usr/share/elasticsearch/data
    healthcheck:
      test: curl -s http://localhost:9200 >/dev/null || exit 1
      interval: 10s
      timeout: 5s
      retries: 5

  logstash:
    image: docker.elastic.co/logstash/logstash:8.5.0
    volumes:
      - ./logstash.conf:/usr/share/logstash/pipeline/logstash.conf
    ports:
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
ELK Stack ConfigurationELK Stack Configuration
Logstash Pipeline ConfigurationLogstash Pipeline Configuration
Filebeat ConfigurationFilebeat Configuration
Kibana Dashboard and AlertsKibana Dashboard and Alerts
Loki Configuration (Kubernetes)Loki Configuration (Kubernetes)
Log Aggregation Deployment ScriptLog Aggregation Deployment Script

Best Practices

✅ DO

  • Parse and structure log data
  • Use appropriate log levels
  • Add contextual information
  • Implement log retention policies
  • Set up log-based alerting
  • Index important fields
  • Use consistent timestamp formats
  • Implement access controls

❌ DON'T

  • Store sensitive data in logs
  • Log at DEBUG level in production
  • Send raw unstructured logs
  • Ignore storage costs
  • Skip log parsing
  • Lack monitoring of log systems
  • Store logs forever
  • Log PII without encryption

Related skills

FAQ

What does log-aggregation help developers set up?

log-aggregation guides centralized application and infrastructure logging with structured formats, collectors, retention policies, and search so production teams can trace errors and correlate events across services.

When should a team use log-aggregation?

log-aggregation fits when stdout logs are scattered across services, on-call needs searchable history, or compliance requires defined retention for application and infrastructure events in production.

DevOps & CI/CDmonitoringinfra

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