Total 50,611 skills, DevOps & Cloud Services has 3060 skills
Showing 12 of 3060 skills
Production readiness checklist for Gamma integration. Use when preparing to deploy Gamma integration to production, or auditing existing production setup. Trigger with phrases like "gamma production", "gamma prod ready", "gamma go live", "gamma deployment checklist", "gamma launch".
Post-pipeline retrospective — parse logs, score process quality, find waste patterns, suggest skill/script patches. Use after pipeline completes or when user says "retro", "evaluate pipeline", "what went wrong", "pipeline review", "check pipeline logs".
Prometheus/Grafana metrics analysis and PromQL queries. Use when investigating latency, error rates, resource usage, or any time-series metrics.
Kubernetes debugging patterns. Use for pod crashes, CrashLoopBackOff, OOMKilled, ImagePullBackOff, scheduling failures, deployment issues.
Systematic incident investigation methodology. Use when investigating production issues, service degradation, errors, latency spikes, or outages.
Troubleshoot Tailscale connectivity or access internal services via Tailscale hostnames.
Guide for implementing Grafana Tempo - a high-scale distributed tracing backend for OpenTelemetry traces. Use when configuring Tempo deployments, setting up storage backends (S3, Azure Blob, GCS), writing TraceQL queries, deploying via Helm, understanding trace structure, or troubleshooting Tempo issues on Kubernetes.
Manage Helm values across environments with override precedence, multi-environment configurations, and secret management. Covers values files, --set, --set-string, values schema validation. Use when user mentions Helm values, environment-specific configs, values.yaml, --set overrides, or Helm configuration.
Check production health: Sentry errors, Vercel logs, health endpoints, GitHub CI/CD. Outputs structured findings. Use log-production-issues to create issues. Invoke for: production diagnostics, error audit, health status, CI failures.
QCSD Verification phase swarm for CI/CD pipeline quality gates using regression analysis, flaky test detection, quality gate enforcement, and deployment readiness assessment. Consumes Development outputs (SHIP/CONDITIONAL/HOLD decisions, quality metrics) and produces signals for Production monitoring.
End-to-end npm release workflow with verification gates and hardcoded-version protection
Partition-first log analysis methodology. Use for log searches, error analysis, pattern finding across Datadog, CloudWatch, or Kubernetes logs.