Agent skill

python-micrometer-gcp-cloud-monitoring

Exports Micrometer metrics to GCP Cloud Monitoring (Stackdriver) for GKE deployments. Use when setting up metrics export in Kubernetes environments, configuring Workload Identity, managing metric prefixes and resource labels, or correlating metrics with GCP services. Critical for GKE-based microservices with centralized observability in Google Cloud.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/python-micrometer-gcp-cloud-monitoring

SKILL.md

Micrometer GCP Cloud Monitoring Integration

Quick Start

Export metrics to Cloud Monitoring in 3 steps:

1. Add dependencies:

xml
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-stackdriver</artifactId>
</dependency>
<dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>spring-cloud-gcp-starter-metrics</artifactId>
</dependency>

2. Configure export:

yaml
management:
  metrics:
    export:
      stackdriver:
        enabled: true
        project-id: ${GCP_PROJECT_ID}
        resource-type: k8s_container
        step: 1m
        resource-labels:
          cluster_name: ${GKE_CLUSTER_NAME}
          namespace_name: ${NAMESPACE}
          pod_name: ${POD_NAME}

3. Grant IAM permission:

bash
gcloud projects add-iam-policy-binding PROJECT_ID \
  --member="serviceAccount:SERVICE_ACCOUNT@PROJECT_ID.iam.gserviceaccount.com" \
  --role="roles/monitoring.metricWriter"

See references/workload-identity-setup.md for complete Workload Identity setup.

Table of Contents

  1. When to Use
  2. Configuration
  3. Testing
  4. Supporting Files
  5. Requirements
  6. Anti-Patterns

When to Use

Use this skill when you need to:

  • Export metrics to GCP Cloud Monitoring for GKE deployments
  • Configure Workload Identity for secure pod authentication
  • Set resource labels for filtering in Cloud Console
  • Correlate metrics with GKE, Pub/Sub, Cloud SQL services
  • Create dashboards and alerts in Cloud Monitoring

When NOT to use:

  • Prometheus-only environments (use Prometheus registry)
  • Before basic Micrometer setup (use python-micrometer-metrics-setup)
  • Local development (use Prometheus locally)
  • Without Workload Identity (requires GKE 1.12+)

Configuration

Basic Configuration

yaml
# application.yml
management:
  metrics:
    export:
      stackdriver:
        enabled: ${STACKDRIVER_ENABLED:true}
        project-id: ${GCP_PROJECT_ID}
        step: 1m  # Export frequency
        resource-type: k8s_container  # GKE resource type

        # Resource labels (appear on every metric)
        resource-labels:
          cluster_name: ${GKE_CLUSTER_NAME}
          namespace_name: ${NAMESPACE}
          pod_name: ${POD_NAME}
          container_name: ${CONTAINER_NAME:app}
          environment: ${ENVIRONMENT:production}
          region: ${GCP_REGION:europe-west2}

    # Common tags (filterable in queries)
    tags:
      application: ${spring.application.name}
      version: ${BUILD_VERSION:unknown}

    # Histogram buckets for latency metrics
    distribution:
      percentiles-histogram:
        http.server.requests: true
      slo:
        http.server.requests: 10ms,50ms,100ms,200ms,500ms,1s,2s,5s

Kubernetes Deployment Environment Variables

yaml
# deployment.yaml
env:
  - name: GCP_PROJECT_ID
    value: "my-project"
  - name: GKE_CLUSTER_NAME
    value: "my-cluster"
  - name: NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
  - name: POD_NAME
    valueFrom:
      fieldRef:
        fieldPath: metadata.name
  - name: CONTAINER_NAME
    value: "app"

Custom Metric Configuration

java
@Configuration
public class CloudMonitoringConfig {

    @Bean
    public MeterRegistryCustomizer<MeterRegistry> commonTags() {
        return registry -> registry.config().commonTags(
            "service", "my-api",
            "component", "backend"
        );
    }
}

Testing

Verify Export in Logs

bash
# Check pod logs for metric export success
kubectl logs -f deployment/my-api -n my-namespace

# Look for: "Successfully published X metrics to Stackdriver"

Query Metrics via CLI

bash
# List exported metrics
gcloud monitoring metrics-descriptors list \
  --filter="metric.type:custom.googleapis.com"

View in Cloud Console

  1. Open GCP Console → Cloud Monitoring → Metrics Explorer
  2. Select resource: "Kubernetes Container"
  3. Filter by cluster
  4. View metric: custom.googleapis.com/...

Supporting Files

File Purpose
references/workload-identity-setup.md Complete Workload Identity configuration guide
references/dashboards-and-alerting.md Create dashboards and alert policies with Terraform

Requirements

  • Spring Boot 2.1+ with Actuator
  • micrometer-registry-stackdriver dependency
  • GCP Project with Cloud Monitoring API enabled
  • GKE cluster with Workload Identity enabled
  • IAM role: roles/monitoring.metricWriter
  • Java 11+

Anti-Patterns

yaml
# ❌ Missing resource labels (hard to filter)
resource-labels:
  environment: production

# ✅ Complete resource labels
resource-labels:
  cluster_name: my-cluster
  namespace_name: my-namespace
  pod_name: ${POD_NAME}

# ❌ High cardinality resource labels (memory issues)
resource-labels:
  pod_ip: ${POD_IP}       # Changes constantly
  user_id: ${USER_ID}     # Unbounded

# ✅ Stable, bounded labels
resource-labels:
  cluster_name: my-cluster
  environment: production

See Also

  • python-micrometer-cardinality-control - Manage metric cardinality
  • python-micrometer-business-metrics - Create business metrics
  • python-micrometer-metrics-setup - Initial Micrometer setup

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results