Add 10 in-depth SEO-focused DevOps, security, and AI infra skills

infrastructure/networking/ai-inference-service-mesh/SKILL.md

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+---
+name: ai-inference-service-mesh
+description: Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# AI Inference Service Mesh
+
+Apply Istio/Linkerd mesh controls to secure and optimize east-west AI traffic across inference microservices.
+
+## Why Mesh for AI
+
+- Enforce mTLS between gateway, retriever, reranker, and model services
+- Apply fine-grained traffic policies without app code changes
+- Run progressive delivery for model-serving backends
+- Observe latency hops for retrieval + generation chains
+
+## Core Patterns
+
+### Security
+- mTLS strict mode cluster-wide
+- AuthorizationPolicy per service account
+- Egress policies for approved model endpoints only
+
+### Traffic Management
+- Canary by header or percentage for new model versions
+- Retry budgets tuned for long-running streaming requests
+- Circuit breakers to protect overloaded inference backends
+
+### Resilience
+- Outlier detection on failing pods
+- Locality-aware routing in multi-zone clusters
+- Failover to secondary cluster/provider
+
+## Observability
+
+- Capture distributed traces across the full AI request path
+- Emit service-level and route-level p95/p99 latency
+- Segment metrics by model and tenant labels
+
+## Pitfalls to Avoid
+
+- Aggressive timeouts that break streaming responses
+- Blanket retries that amplify expensive generation calls
+- Missing identity boundaries between tenant-facing and internal services
+
+## Related Skills
+
+- [service-mesh](../service-mesh/) - Foundational mesh concepts
+- [llm-gateway](../llm-gateway/) - North-south API gateway controls
+- [opentelemetry](../../../devops/observability/opentelemetry/) - End-to-end tracing and metrics