Lightweight service mesh patterns for Kubernetes with automatic mTLS and zero-trust networking.
Works with
Covers installation, namespace injection, and core resources including ServiceProfile for per-route metrics, TrafficSplit for canary deployments, and Server/ServerAuthorization policies for access control
Includes templates for mesh setup, traffic splitting, retry configuration with budgets, multi-cluster linking, and HTTPRoute-based advanced routing
Provides monitoring and debugging comma
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlinkerd-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches linkerd-patterns from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate linkerd-patterns. Access via /linkerd-patterns in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
33.1K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
33.1K
stars
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:
✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Related Skills
grill-me
706mattpocock/skills
Productivitysame categorypremortem
218parcadei/continuous-claude-v3
Productivitysame categorydeslop
164cursor/plugins
Productivitysame categorytravel-planner
145ailabs-393/ai-labs-claude-skills
Productivitysame categorynutritional-specialist
141ailabs-393/ai-labs-claude-skills
Productivitysame categoryframer-motion
141pproenca/dot-skills
Productivitysame categoryReviews
4.6★★★★★35 reviews- LLuis Thompson★★★★★Dec 24, 2024
I recommend linkerd-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- PPratham Ware★★★★★Dec 8, 2024
linkerd-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- YYash Thakker★★★★★Nov 27, 2024
Registry listing for linkerd-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
- LLuis Chen★★★★★Nov 15, 2024
Keeps context tight: linkerd-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDhruvi Jain★★★★★Oct 18, 2024
linkerd-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
- LLuis Khanna★★★★★Oct 6, 2024
linkerd-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNia Patel★★★★★Sep 25, 2024
Solid pick for teams standardizing on skills: linkerd-patterns is focused, and the summary matches what you get after install.
- CCamila Wang★★★★★Sep 25, 2024
We added linkerd-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- OOshnikdeep★★★★★Sep 9, 2024
I recommend linkerd-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- GGanesh Mohane★★★★★Aug 28, 2024
Useful defaults in linkerd-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 35
1 / 4Discussion
Comments — not star reviews- No comments yet — start the thread.