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

infrastructure/local-ai/gpu-kubernetes-operations/SKILL.md

@@ -0,0 +1,58 @@
+---
+name: gpu-kubernetes-operations
+description: Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# GPU Kubernetes Operations
+
+Run resilient and cost-efficient GPU clusters for production AI workloads.
+
+## Key Capabilities
+
+- NVIDIA device plugin and GPU operator lifecycle
+- MIG partitioning for multi-workload efficiency
+- GPU-aware autoscaling (KEDA/cluster autoscaler)
+- Node health checks and proactive remediation
+
+## Cluster Baseline
+
+- Dedicated GPU node pools with taints and tolerations
+- Runtime class and driver/toolkit compatibility checks
+- Local SSD or high-throughput network storage for model weights
+- DCGM metrics exported to Prometheus
+
+## Scheduling Patterns
+
+- Use node affinity by GPU type (A10/L4/A100/H100).
+- Separate latency-critical inference from batch training.
+- Pin model replicas with anti-affinity for availability.
+- Reserve headroom for failover and rolling updates.
+
+## Autoscaling Strategy
+
+- Scale on queue depth + GPU utilization, not CPU alone.
+- Warm spare replicas for large model cold-start mitigation.
+- Cap burst scaling to avoid quota exhaustion.
+
+## Reliability Checks
+
+- ECC error and Xid monitoring
+- GPU memory pressure alerts
+- Driver mismatch detection during upgrades
+- Pod preemption impact analysis
+
+## Cost Optimization
+
+- Prefer MIG slices for smaller inference services.
+- Schedule batch jobs in off-peak windows.
+- Route low-priority traffic to cheaper model tiers.
+
+## Related Skills
+
+- [llm-inference-scaling](../llm-inference-scaling/) - Autoscale inference workloads
+- [model-serving-kubernetes](../../../devops/orchestration/model-serving-kubernetes/) - Production model serving patterns
+- [gpu-server-management](../../servers/gpu-server-management/) - Host-level GPU management fundamentals

infrastructure/local-ai/multi-tenant-llm-hosting/SKILL.md

@@ -0,0 +1,55 @@
+---
+name: multi-tenant-llm-hosting
+description: Design secure, multi-tenant LLM hosting platforms with tenant isolation, quotas, billing attribution, noisy-neighbor protection, and per-tenant policy controls.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# Multi-Tenant LLM Hosting
+
+Host many teams/customers on shared inference infrastructure without sacrificing security, performance, or cost governance.
+
+## Isolation Model
+
+- Strong tenant identity on every request
+- Per-tenant API keys and scoped model access
+- Namespace or workload isolation for high-risk tenants
+- Strict data retention and log partitioning controls
+
+## Noisy-Neighbor Controls
+
+- Per-tenant RPM/TPM limits
+- Concurrency caps and queue isolation
+- Fair scheduling with weighted priority classes
+- Backpressure and graceful degradation policies
+
+## Billing and Chargeback
+
+Track per-tenant:
+- prompt/completion/cached tokens,
+- model type and route,
+- latency and success rate,
+- cost with markup or internal transfer pricing.
+
+## Security Baseline
+
+- Encrypt data in transit and at rest.
+- Disallow cross-tenant cache leakage.
+- Restrict debug data access by role.
+- Audit all privileged administrative actions.
+
+## Operational Runbook
+
+1. Onboard tenant with policy template.
+2. Issue virtual key and quota profile.
+3. Validate observability and billing tags.
+4. Run tenant-specific load/safety tests.
+5. Enable production traffic with canary limits.
+
+## Related Skills
+
+- [llm-gateway](../../networking/llm-gateway/) - Key management and traffic routing
+- [llm-cost-optimization](../../../devops/ai/llm-cost-optimization/) - Cost controls and optimization tactics
+- [zero-trust](../../../security/network/zero-trust/) - Identity-centric network and access patterns

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

@@ -0,0 +1,54 @@
+---
+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