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Linkerd Patterns

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wshobson
linkerd-patterns

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.

Overview

Publisherwshobson
Repositoryagents
Skill namelinkerd-patterns
Stars
39.8K
Forks
4.2K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wshobson on GitHub. Read the source before you install it.

Installation

Install the Linkerd Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/cloud-infrastructure/skills/linkerd-patterns .claude/skills/linkerd-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkerd Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkerd Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkerd Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Linkerd Patterns

Production patterns for Linkerd service mesh - the lightweight, security-first service mesh for Kubernetes.

When to Use This Skill

  • Setting up a lightweight service mesh
  • Implementing automatic mTLS
  • Configuring traffic splits for canary deployments
  • Setting up service profiles for per-route metrics
  • Implementing retries and timeouts
  • Multi-cluster service mesh

Core Concepts

1. Linkerd Architecture

┌─────────────────────────────────────────────┐
│                Control Plane                 │
│  ┌─────────┐ ┌──────────┐ ┌──────────────┐ │
│  │ destiny │ │ identity │ │ proxy-inject │ │
│  └─────────┘ └──────────┘ └──────────────┘ │
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│                 Data Plane                   │
│  ┌─────┐    ┌─────┐    ┌─────┐             │
│  │proxy│────│proxy│────│proxy│             │
│  └─────┘    └─────┘    └─────┘             │
│     │           │           │               │
│  ┌──┴──┐    ┌──┴──┐    ┌──┴──┐            │
│  │ app │    │ app │    │ app │            │
│  └─────┘    └─────┘    └─────┘            │
└─────────────────────────────────────────────┘

2. Key Resources

ResourcePurpose
ServiceProfilePer-route metrics, retries, timeouts
TrafficSplitCanary deployments, A/B testing
ServerDefine server-side policies
ServerAuthorizationAccess control policies

Templates

Template 1: Mesh Installation

bash
# 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 -

Template 2: Inject Namespace

yaml
# 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

Template 3: Service Profile with Retries

yaml
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

Template 4: Traffic Split (Canary)

yaml
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%

Template 5: Server Authorization Policy

yaml
# 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

Template 6: HTTPRoute for Advanced Routing

yaml
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

Template 7: Multi-cluster Setup

bash
# 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

Monitoring Commands

bash
# 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

Debugging

bash
# 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

Best Practices

Do's

  • Enable mTLS everywhere - It's automatic with Linkerd
  • Use ServiceProfiles - Get per-route metrics and retries
  • Set retry budgets - Prevent retry storms
  • Monitor golden metrics - Success rate, latency, throughput

Don'ts

  • Don't skip check - Always run linkerd check after changes
  • Don't over-configure - Linkerd defaults are sensible
  • Don't ignore ServiceProfiles - They unlock advanced features
  • Don't forget timeouts - Set appropriate values per route

Frequently asked questions

What does the Linkerd Patterns AI skill do?

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.

Why use Linkerd Patterns on TypingMind?

Because you install it once and use it with any model. Linkerd Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkerd Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/cloud-infrastructure/skills/linkerd-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Linkerd Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkerd Patterns?

As many as you like. As long as a model supports skills, you can use Linkerd Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkerd Patterns AI skill free?

Yes. It is published on GitHub by wshobson under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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