5889d3428b
- Added 44 external skills from obra/superpowers, ui-ux-pro-max-skill, claude-codex-settings - Added 8 autonomous agents (commit-creator, pr-creator, pr-reviewer, etc.) - Added 23 slash commands for Git/GitHub, setup, and plugin development - Added hooks for code formatting, notifications, and validation - Added MCP configurations for Azure, GCloud, Supabase, MongoDB, etc. - Added awesome-openclaw-skills registry (3,002 skills referenced) - Updated comprehensive README with full documentation Sources: - github.com/obra/superpowers (14 skills) - github.com/nextlevelbuilder/ui-ux-pro-max-skill (1 skill) - github.com/fcakyon/claude-codex-settings (29 skills, 8 agents, 23 commands) - github.com/VoltAgent/awesome-openclaw-skills (registry) - skills.sh (reference) - buildwithclaude.com (reference)
149 lines
3.4 KiB
Markdown
149 lines
3.4 KiB
Markdown
---
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name: gcloud-usage
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description: This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
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---
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# GCP Observability Best Practices
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## Structured Logging
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### JSON Log Format
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Use structured JSON logging for better queryability:
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```json
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{
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"severity": "ERROR",
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"message": "Payment failed",
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"httpRequest": { "requestMethod": "POST", "requestUrl": "/api/payment" },
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"labels": { "user_id": "123", "transaction_id": "abc" },
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"timestamp": "2025-01-15T10:30:00Z"
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}
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```
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### Severity Levels
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Use appropriate severity for filtering:
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- **DEBUG:** Detailed diagnostic info
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- **INFO:** Normal operations, milestones
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- **NOTICE:** Normal but significant events
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- **WARNING:** Potential issues, degraded performance
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- **ERROR:** Failures that don't stop the service
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- **CRITICAL:** Failures requiring immediate action
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- **ALERT:** Person must take action immediately
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- **EMERGENCY:** System is unusable
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## Log Filtering Queries
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### Common Filters
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```
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# By severity
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severity >= WARNING
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# By resource
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resource.type="cloud_run_revision"
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resource.labels.service_name="my-service"
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# By time
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timestamp >= "2025-01-15T00:00:00Z"
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# By text content
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textPayload =~ "error.*timeout"
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# By JSON field
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jsonPayload.user_id = "123"
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# Combined
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severity >= ERROR AND resource.labels.service_name="api"
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```
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### Advanced Queries
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```
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# Regex matching
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textPayload =~ "status=[45][0-9]{2}"
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# Substring search
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textPayload : "connection refused"
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# Multiple values
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severity = (ERROR OR CRITICAL)
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```
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## Metrics vs Logs vs Traces
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### When to Use Each
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**Metrics:** Aggregated numeric data over time
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- Request counts, latency percentiles
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- Resource utilization (CPU, memory)
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- Business KPIs (orders/minute)
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**Logs:** Detailed event records
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- Error details and stack traces
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- Audit trails
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- Debugging specific requests
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**Traces:** Request flow across services
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- Latency breakdown by service
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- Identifying bottlenecks
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- Distributed system debugging
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## Alert Policy Design
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### Alert Best Practices
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- **Avoid alert fatigue:** Only alert on actionable issues
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- **Use multi-condition alerts:** Reduce noise from transient spikes
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- **Set appropriate windows:** 5-15 min for most metrics
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- **Include runbook links:** Help responders act quickly
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### Common Alert Patterns
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**Error rate:**
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- Condition: Error rate > 1% for 5 minutes
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- Good for: Service health monitoring
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**Latency:**
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- Condition: P99 latency > 2s for 10 minutes
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- Good for: Performance degradation detection
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**Resource exhaustion:**
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- Condition: Memory > 90% for 5 minutes
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- Good for: Capacity planning triggers
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## Cost Optimization
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### Reducing Log Costs
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- **Exclusion filters:** Drop verbose logs at ingestion
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- **Sampling:** Log only percentage of high-volume events
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- **Shorter retention:** Reduce default 30-day retention
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- **Downgrade logs:** Route to cheaper storage buckets
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### Exclusion Filter Examples
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```
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# Exclude health checks
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resource.type="cloud_run_revision" AND httpRequest.requestUrl="/health"
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# Exclude debug logs in production
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severity = DEBUG
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```
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## Debugging Workflow
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1. **Start with metrics:** Identify when issues started
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2. **Correlate with logs:** Filter logs around problem time
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3. **Use traces:** Follow specific requests across services
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4. **Check resource logs:** Look for infrastructure issues
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5. **Compare baselines:** Check against known-good periods
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