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Skill1.2k estrellas del repoactualizado 4d ago

gcloud-usage

The gcloud-usage skill provides structured guidance on Google Cloud Platform observability through logging best practices, query syntax, metrics versus logs versus traces distinctions, and alert policy design patterns. Use this skill when users ask about Cloud Logging queries, GCP metrics, trace analysis, debugging production issues on Google Cloud, or implementing GCP observability strategies.

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git clone --depth 1 https://github.com/fcakyon/claude-codex-settings /tmp/gcloud-usage && cp -r /tmp/gcloud-usage/plugins/gcloud-tools/skills/gcloud-usage ~/.claude/skills/gcloud-usage
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SKILL.md

# GCP Observability Best Practices

## Structured Logging

### JSON Log Format

Use structured JSON logging for better queryability:

```json
{
  "severity": "ERROR",
  "message": "Payment failed",
  "httpRequest": { "requestMethod": "POST", "requestUrl": "/api/payment" },
  "labels": { "user_id": "123", "transaction_id": "abc" },
  "timestamp": "2025-01-15T10:30:00Z"
}
```

### Severity Levels

Use appropriate severity for filtering:

- **DEBUG:** Detailed diagnostic info
- **INFO:** Normal operations, milestones
- **NOTICE:** Normal but significant events
- **WARNING:** Potential issues, degraded performance
- **ERROR:** Failures that don't stop the service
- **CRITICAL:** Failures requiring immediate action
- **ALERT:** Person must take action immediately
- **EMERGENCY:** System is unusable

## Log Filtering Queries

### Common Filters

```
# By severity
severity >= WARNING

# By resource
resource.type="cloud_run_revision"
resource.labels.service_name="my-service"

# By time
timestamp >= "2025-01-15T00:00:00Z"

# By text content
textPayload =~ "error.*timeout"

# By JSON field
jsonPayload.user_id = "123"

# Combined
severity >= ERROR AND resource.labels.service_name="api"
```

### Advanced Queries

```
# Regex matching
textPayload =~ "status=[45][0-9]{2}"

# Substring search
textPayload : "connection refused"

# Multiple values
severity = (ERROR OR CRITICAL)
```

## Metrics vs Logs vs Traces

### When to Use Each

**Metrics:** Aggregated numeric data over time

- Request counts, latency percentiles
- Resource utilization (CPU, memory)
- Business KPIs (orders/minute)

**Logs:** Detailed event records

- Error details and stack traces
- Audit trails
- Debugging specific requests

**Traces:** Request flow across services

- Latency breakdown by service
- Identifying bottlenecks
- Distributed system debugging

## Alert Policy Design

### Alert Best Practices

- **Avoid alert fatigue:** Only alert on actionable issues
- **Use multi-condition alerts:** Reduce noise from transient spikes
- **Set appropriate windows:** 5-15 min for most metrics
- **Include runbook links:** Help responders act quickly

### Common Alert Patterns

**Error rate:**

- Condition: Error rate > 1% for 5 minutes
- Good for: Service health monitoring

**Latency:**

- Condition: P99 latency > 2s for 10 minutes
- Good for: Performance degradation detection

**Resource exhaustion:**

- Condition: Memory > 90% for 5 minutes
- Good for: Capacity planning triggers

## Cost Optimization

### Reducing Log Costs

- **Exclusion filters:** Drop verbose logs at ingestion
- **Sampling:** Log only percentage of high-volume events
- **Shorter retention:** Reduce default 30-day retention
- **Downgrade logs:** Route to cheaper storage buckets

### Exclusion Filter Examples

```
# Exclude health checks
resource.type="cloud_run_revision" AND httpRequest.requestUrl="/health"

# Exclude debug logs in production
severity = DEBUG
```

## Debugging Workflow

1. **Start with metrics:** Identify when issues started
2. **Correlate with logs:** Filter logs around problem time
3. **Use traces:** Follow specific requests across services
4. **Check resource logs:** Look for infrastructure issues
5. **Compare baselines:** Check against known-good periods
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