Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.
npx skills add https://github.com/wshobson/agents --skill linkerd-patterns
Production patterns for Linkerd service mesh - the lightweight, security-first service mesh for Kubernetes.
┌─────────────────────────────────────────────┐
│ Control Plane │
│ ┌─────────┐ ┌──────────┐ ┌──────────────┐ │
│ │ destiny │ │ identity │ │ proxy-inject │ │
│ └─────────┘ └──────────┘ └──────────────┘ │
└─────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────┐
│ Data Plane │
│ ┌─────┐ ┌─────┐ ┌─────┐ │
│ │proxy│────│proxy│────│proxy│ │
│ └─────┘ └─────┘ └─────┘ │
│ │ │ │ │
│ ┌──┴──┐ ┌──┴──┐ ┌──┴──┐ │
│ │ app │ │ app │ │ app │ │
│ └─────┘ └─────┘ └─────┘ │
└─────────────────────────────────────────────┘
| Resource | Purpose |
| ----------------------- | ------------------------------------ |
| ServiceProfile | Per-route metrics, retries, timeouts |
| TrafficSplit | Canary deployments, A/B testing |
| Server | Define server-side policies |
| ServerAuthorization | Access control policies |
# Install CLI
curl --proto '=https' --tlsv1.2 -sSfL https://run.linkerd.io/install | sh
# Validate cluster
linkerd check --pre
# Install CRDs
linkerd install --crds | kubectl apply -f -
# Install control plane
linkerd install | kubectl apply -f -
# Verify installation
linkerd check
# Install viz extension (optional)
linkerd viz install | kubectl apply -f -
# Automatic injection for namespace
apiVersion: v1
kind: Namespace
metadata:
name: my-app
annotations:
linkerd.io/inject: enabled
---
# Or inject specific deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
annotations:
linkerd.io/inject: enabled
spec:
template:
metadata:
annotations:
linkerd.io/inject: enabled
apiVersion: linkerd.io/v1alpha2
kind: ServiceProfile
metadata:
name: my-service.my-namespace.svc.cluster.local
namespace: my-namespace
spec:
routes:
- name: GET /api/users
condition:
method: GET
pathRegex: /api/users
responseClasses:
- condition:
status:
min: 500
max: 599
isFailure: true
isRetryable: true
- name: POST /api/users
condition:
method: POST
pathRegex: /api/users
# POST not retryable by default
isRetryable: false
- name: GET /api/users/{id}
condition:
method: GET
pathRegex: /api/users/[^/]+
timeout: 5s
isRetryable: true
retryBudget:
retryRatio: 0.2
minRetriesPerSecond: 10
ttl: 10s
apiVersion: split.smi-spec.io/v1alpha1
kind: TrafficSplit
metadata:
name: my-service-canary
namespace: my-namespace
spec:
service: my-service
backends:
- service: my-service-stable
weight: 900m # 90%
- service: my-service-canary
weight: 100m # 10%
# Define the server
apiVersion: policy.linkerd.io/v1beta1
kind: Server
metadata:
name: my-service-http
namespace: my-namespace
spec:
podSelector:
matchLabels:
app: my-service
port: http
proxyProtocol: HTTP/1
---
# Allow traffic from specific clients
apiVersion: policy.linkerd.io/v1beta1
kind: ServerAuthorization
metadata:
name: allow-frontend
namespace: my-namespace
spec:
server:
name: my-service-http
client:
meshTLS:
serviceAccounts:
- name: frontend
namespace: my-namespace
---
# Allow unauthenticated traffic (e.g., from ingress)
apiVersion: policy.linkerd.io/v1beta1
kind: ServerAuthorization
metadata:
name: allow-ingress
namespace: my-namespace
spec:
server:
name: my-service-http
client:
unauthenticated: true
networks:
- cidr: 10.0.0.0/8
apiVersion: policy.linkerd.io/v1beta2
kind: HTTPRoute
metadata:
name: my-route
namespace: my-namespace
spec:
parentRefs:
- name: my-service
kind: Service
group: core
port: 8080
rules:
- matches:
- path:
type: PathPrefix
value: /api/v2
- headers:
- name: x-api-version
value: v2
backendRefs:
- name: my-service-v2
port: 8080
- matches:
- path:
type: PathPrefix
value: /api
backendRefs:
- name: my-service-v1
port: 8080
# On each cluster, install with cluster credentials
linkerd multicluster install | kubectl apply -f -
# Link clusters
linkerd multicluster link --cluster-name west \
--api-server-address https://west.example.com:6443 \
| kubectl apply -f -
# Export a service to other clusters
kubectl label svc/my-service mirror.linkerd.io/exported=true
# Verify cross-cluster connectivity
linkerd multicluster check
linkerd multicluster gateways
# Live traffic view
linkerd viz top deploy/my-app
# Per-route metrics
linkerd viz routes deploy/my-app
# Check proxy status
linkerd viz stat deploy -n my-namespace
# View service dependencies
linkerd viz edges deploy -n my-namespace
# Dashboard
linkerd viz dashboard
# Check injection status
linkerd check --proxy -n my-namespace
# View proxy logs
kubectl logs deploy/my-app -c linkerd-proxy
# Debug identity/TLS
linkerd identity -n my-namespace
# Tap traffic (live)
linkerd viz tap deploy/my-app --to deploy/my-backend
linkerd check after changesAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take wshobson/linkerd-patterns from the repository into ~/.claude/skills for personal
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