/log-aggregation
Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.
$ npx -y skills add aj-geddes/useful-ai-prompts --skill log-aggregation --agent claude-codeHow it fires
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/log-aggregation
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Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.
SKILL.md
log-aggregation.SKILL.mdname: log-aggregation
description: >
Implement centralized logging with ELK Stack, Loki, or Splunk for log
collection, parsing, storage, and analysis across infrastructure.
Log Aggregation
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
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:
| Guide | Contents | |---|---| | [ELK Stack Configuration](references/elk-stack-configuration.md) | ELK Stack Configuration | | [Logstash Pipeline Configuration](references/logstash-pipeline-configuration.md) | Logstash Pipeline Configuration | | [Filebeat Configuration](references/filebeat-configuration.md) | Filebeat Configuration | | [Kibana Dashboard and Alerts](references/kibana-dashboard-and-alerts.md) | Kibana Dashboard and Alerts | | [Loki Configuration (Kubernetes)](references/loki-configuration-kubernetes.md) | Loki Configuration (Kubernetes) | | [Log Aggregation Deployment Script](references/log-aggregation-deployment-script.md) | Log 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
Read more
name: log-aggregation description: > Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.
Log Aggregation
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
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:
| Guide | Contents | |---|---| | [ELK Stack Configuration](references/elk-stack-configuration.md) | ELK Stack Configuration | | [Logstash Pipeline Configuration](references/logstash-pipeline-configuration.md) | Logstash Pipeline Configuration | | [Filebeat Configuration](references/filebeat-configuration.md) | Filebeat Configuration | | [Kibana Dashboard and Alerts](references/kibana-dashboard-and-alerts.md) | Kibana Dashboard and Alerts | | [Loki Configuration (Kubernetes)](references/loki-configuration-kubernetes.md) | Loki Configuration (Kubernetes) | | [Log Aggregation Deployment Script](references/log-aggregation-deployment-script.md) | Log 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
488 production-ready AI prompts, all following a standardized template with validated quality gates. Transform ChatGPT, Claude, and other AI assistants into expert consultants.
Repo: aj-geddes/useful-ai-prompts
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