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Elk Stack Agent Skill

Deploy and manage the ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis. Configure log pipelines, create visualizations, and implement log-based monitoring. Use when centralizing logs, implementing search functionality, or building log analytics platforms.

3k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
511
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill elk-stack

What comes with it

2 109 bytes besides the instruction
references/elasticsearch-queries.md

The instruction itself

28 sections, as written by the author

ELK Stack

Centralize and analyze logs with Elasticsearch, Logstash, and Kibana.

When to Use This Skill

Use this skill when:

  • Centralizing logs from multiple sources
  • Building log search and analytics platforms
  • Creating log-based dashboards and alerts
  • Implementing full-text search for logs
  • Processing and transforming log data

Prerequisites

  • Docker or server infrastructure
  • Sufficient disk space for log storage
  • Network access from log sources

Docker Deployment

# docker-compose.yml
version: '3.8'

services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - "ES_JAVA_OPTS=-Xms1g -Xmx1g"
    ports:
      - "9200:9200"
    volumes:
      - elasticsearch-data:/usr/share/elasticsearch/data

  logstash:
    image: docker.elastic.co/logstash/logstash:8.11.0
    volumes:
      - ./logstash/pipeline:/usr/share/logstash/pipeline
      - ./logstash/config:/usr/share/logstash/config
    ports:
      - "5044:5044"
      - "5000:5000"
    depends_on:
      - elasticsearch

  kibana:
    image: docker.elastic.co/kibana/kibana:8.11.0
    ports:
      - "5601:5601"
    environment:
      - ELASTICSEARCH_HOSTS=http://elasticsearch:9200
    depends_on:
      - elasticsearch

  filebeat:
    image: docker.elastic.co/beats/filebeat:8.11.0
    user: root
    volumes:
      - ./filebeat/filebeat.yml:/usr/share/filebeat/filebeat.yml:ro
      - /var/lib/docker/containers:/var/lib/docker/containers:ro
      - /var/run/docker.sock:/var/run/docker.sock:ro
    depends_on:
      - logstash

volumes:
  elasticsearch-data:

Elasticsearch Configuration

Index Templates

PUT _index_template/logs-template
{
  "index_patterns": ["logs-*"],
  "template": {
    "settings": {
      "number_of_shards": 1,
      "number_of_replicas": 1,
      "index.lifecycle.name": "logs-policy"
    },
    "mappings": {
      "properties": {
        "@timestamp": { "type": "date" },
        "message": { "type": "text" },
        "level": { "type": "keyword" },
        "service": { "type": "keyword" },
        "host": { "type": "keyword" },
        "trace_id": { "type": "keyword" }
      }
    }
  }
}

Index Lifecycle Management

PUT _ilm/policy/logs-policy
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "rollover": {
            "max_size": "50GB",
            "max_age": "1d"
          }
        }
      },
      "warm": {
        "min_age": "7d",
        "actions": {
          "shrink": { "number_of_shards": 1 },
          "forcemerge": { "max_num_segments": 1 }
        }
      },
      "cold": {
        "min_age": "30d",
        "actions": {
          "freeze": {}
        }
      },
      "delete": {
        "min_age": "90d",
        "actions": {
          "delete": {}
        }
      }
    }
  }
}

Logstash Pipeline

Basic Pipeline

# logstash/pipeline/main.conf
input {
  beats {
    port => 5044
  }
  
  tcp {
    port => 5000
    codec => json_lines
  }
}

filter {
  # Parse JSON logs
  if [message] =~ /^\{/ {
    json {
      source => "message"
    }
  }
  
  # Parse timestamp
  date {
    match => ["timestamp", "ISO8601", "yyyy-MM-dd HH:mm:ss"]
    target => "@timestamp"
  }
  
  # Add environment tag
  mutate {
    add_field => { "environment" => "production" }
  }
  
  # Grok pattern for nginx logs
  if [type] == "nginx" {
    grok {
      match => {
        "message" => '%{IPORHOST:client_ip} - %{USER:user} \[%{HTTPDATE:timestamp}\] "%{WORD:method} %{URIPATHPARAM:request} HTTP/%{NUMBER:http_version}" %{NUMBER:status} %{NUMBER:bytes}'
      }
    }
  }
}

output {
  elasticsearch {
    hosts => ["elasticsearch:9200"]
    index => "logs-%{+YYYY.MM.dd}"
  }
}

Advanced Filtering

filter {
  # Parse application logs
  grok {
    match => {
      "message" => "%{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} \[%{DATA:service}\] %{GREEDYDATA:log_message}"
    }
  }
  
  # Extract trace ID from message
  if [log_message] =~ /trace_id=/ {
    grok {
      match => { "log_message" => "trace_id=%{UUID:trace_id}" }
    }
  }
  
  # GeoIP lookup
  if [client_ip] {
    geoip {
      source => "client_ip"
      target => "geoip"
    }
  }
  
  # Drop debug logs in production
  if [level] == "DEBUG" and [environment] == "production" {
    drop {}
  }
  
  # Enrich with lookup
  translate {
    field => "status"
    destination => "status_description"
    dictionary => {
      "200" => "OK"
      "404" => "Not Found"
      "500" => "Internal Server Error"
    }
  }
}

Filebeat Configuration

# filebeat/filebeat.yml
filebeat.inputs:
  - type: container
    paths:
      - '/var/lib/docker/containers/*/*.log'
    processors:
      - add_docker_metadata:
          host: "unix:///var/run/docker.sock"

  - type: log
    enabled: true
    paths:
      - /var/log/nginx/*.log
    tags: ["nginx"]
    fields:
      type: nginx

output.logstash:
  hosts: ["logstash:5044"]

logging.level: info
logging.to_files: true
logging.files:
  path: /var/log/filebeat
  name: filebeat
  keepfiles: 7

Elasticsearch Queries

Basic Queries

// Search all logs
GET logs-*/_search
{
  "query": {
    "match_all": {}
  }
}

// Search by keyword
GET logs-*/_search
{
  "query": {
    "match": {
      "message": "error"
    }
  }
}

// Filter by field
GET logs-*/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "level": "ERROR" } },
        { "range": { "@timestamp": { "gte": "now-1h" } } }
      ],
      "filter": [
        { "term": { "service": "api-gateway" } }
      ]
    }
  }
}

Aggregations

// Count by log level
GET logs-*/_search
{
  "size": 0,
  "aggs": {
    "log_levels": {
      "terms": { "field": "level" }
    }
  }
}

// Error rate over time
GET logs-*/_search
{
  "size": 0,
  "aggs": {
    "errors_over_time": {
      "date_histogram": {
        "field": "@timestamp",
        "fixed_interval": "5m"
      },
      "aggs": {
        "error_count": {
          "filter": { "term": { "level": "ERROR" } }
        }
      }
    }
  }
}

Kibana Setup

Index Patterns

  • Go to Stack Management → Index Patterns
  • Create pattern: logs-*
  • Set time field: @timestamp

Saved Searches

Create saved searches for common queries:

  • level:ERROR - All errors
  • service:api-gateway AND level:ERROR - API gateway errors
  • response_time:>1000 - Slow requests

Visualizations

Common visualization types:

  • Line Chart: Error rate over time
  • Pie Chart: Distribution by log level
  • Data Table: Top error messages
  • Metric: Total error count

Dashboard Example

Create dashboard with:

  • Total log count (Metric)
  • Error rate trend (Line chart)
  • Logs by service (Pie chart)
  • Recent errors (Data table)
  • Log stream (Discover panel)

Alerting

Watcher (X-Pack)

PUT _watcher/watch/error_alert
{
  "trigger": {
    "schedule": { "interval": "5m" }
  },
  "input": {
    "search": {
      "request": {
        "indices": ["logs-*"],
        "body": {
          "query": {
            "bool": {
              "must": [
                { "match": { "level": "ERROR" } },
                { "range": { "@timestamp": { "gte": "now-5m" } } }
              ]
            }
          }
        }
      }
    }
  },
  "condition": {
    "compare": { "ctx.payload.hits.total.value": { "gt": 100 } }
  },
  "actions": {
    "notify_slack": {
      "webhook": {
        "scheme": "https",
        "host": "hooks.slack.com",
        "port": 443,
        "method": "post",
        "path": "/services/xxx",
        "body": "{\"text\": \"High error rate detected: {{ctx.payload.hits.total.value}} errors in last 5 minutes\"}"
      }
    }
  }
}

Common Issues

Issue: High Disk Usage

Problem: Elasticsearch consuming too much disk

Solution: Implement ILM policies, reduce retention

Issue: Slow Searches

Problem: Queries taking too long

Solution: Optimize index settings, add more shards, use filters

Issue: Log Parsing Failures

Problem: Logs not parsed correctly

Solution: Test grok patterns, check for log format changes

Issue: Memory Pressure

Problem: Elasticsearch OOM errors

Solution: Increase heap size (max 50% of RAM), limit field data

Best Practices

  • Implement index lifecycle management
  • Use index templates for consistent mappings
  • Parse logs at ingestion time
  • Limit stored fields to reduce storage
  • Use data streams for time-series data
  • Monitor cluster health
  • Implement proper security (X-Pack)
  • Regular index maintenance
  • loki-logging - Alternative logging stack
  • prometheus-grafana - Metrics monitoring
  • audit-logging - Compliance logging

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