Back to Skills

configuring-service-meshes

jeremylongshore
Updated 2 days ago
8 views
712
74
712
View on GitHub
Metaaidesign

About

This skill generates production-ready configurations for Istio and Linkerd service meshes tailored to your microservices infrastructure. It automates setup with a security-first approach and implements service mesh best practices. Use it when you need to configure a service mesh, set up Istio/Linkerd, or manage microservices communication.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus
Git CloneAlternative
git clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/configuring-service-meshes

Copy and paste this command in Claude Code to install this skill

Documentation

Overview

This skill enables Claude to generate configurations and setup code for service meshes like Istio and Linkerd. It simplifies the process of deploying and managing microservices by automating the configuration of essential service mesh components.

How It Works

  1. Requirement Gathering: Claude identifies the specific service mesh (Istio or Linkerd) and infrastructure requirements from the user's request.
  2. Configuration Generation: Based on the requirements, Claude generates the necessary configuration files, including YAML manifests and setup scripts.
  3. Code Delivery: Claude provides the generated configurations and setup code to the user, ready for deployment.

When to Use This Skill

This skill activates when you need to:

  • Configure Istio for a microservices application.
  • Configure Linkerd for a microservices application.
  • Generate service mesh configurations based on specific infrastructure requirements.

Examples

Example 1: Setting up Istio

User request: "Configure Istio for my Kubernetes microservices deployment with mTLS enabled."

The skill will:

  1. Generate Istio configuration files with mTLS enabled.
  2. Provide the generated YAML manifests and setup instructions.

Example 2: Configuring Linkerd

User request: "Setup Linkerd on my existing microservices cluster, focusing on traffic splitting and observability."

The skill will:

  1. Generate Linkerd configuration files for traffic splitting and observability.
  2. Provide the generated YAML manifests and setup instructions.

Best Practices

  • Security: Always prioritize security configurations, such as mTLS, when configuring service meshes.
  • Observability: Ensure that the service mesh is configured for comprehensive observability, including metrics, tracing, and logging.
  • Traffic Management: Use traffic management features like traffic splitting and canary deployments to manage application updates safely.

Integration

This skill can be integrated with other DevOps tools and plugins in the Claude Code ecosystem to automate the deployment and management of microservices applications. For example, it can work with a Kubernetes deployment plugin to automatically deploy the generated configurations.

GitHub Repository

jeremylongshore/claude-code-plugins-plus
Path: backups/skills-migration-20251108-070147/plugins/devops/service-mesh-configurator/skills/service-mesh-configurator
aiautomationclaude-codedevopsmarketplacemcp

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill