Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
| name | service-mesh-observability |
| description | Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication. |
Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
ā Observability ā
āāāāāāāāāāāāāāāāāāā¬āāāāāāāāāāāāāāāāāā¬āāāāāāāāāāāāāāāāāā¤
ā Metrics ā Traces ā Logs ā
ā ā ā ā
ā ā¢ Request rate ā ā¢ Span context ā ā¢ Access logs ā
ā ā¢ Error rate ā ā¢ Latency ā ā¢ Error details ā
ā ā¢ Latency P50 ā ā¢ Dependencies ā ā¢ Debug info ā
ā ā¢ Saturation ā ā¢ Bottlenecks ā ā¢ Audit trail ā
āāāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāāāā“āāāāāāāāāāāāāāāāāā
| Signal | Description | Alert Threshold |
|---|---|---|
| Latency | Request duration P50, P99 | P99 > 500ms |
| Traffic | Requests per second | Anomaly detection |
| Errors | 5xx error rate | > 1% |
| Saturation | Resource utilization | > 80% |
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
Schedule and publish social media posts across 13 platforms (X, LinkedIn, Instagram, Facebook Pages, TikTok, Discord, Telegram, YouTube, Reddit, WordPress, Pinterest) via the SocialClaw API. Use when the user wants to publish, schedule, or manage social media content programmatically. Requires SOCIALCLAW_API_KEY.
Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing accessible design patterns.
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
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Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or building event-sourced systems.