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nitinjain999
linkerd

Linkerd-specific diagnostics — mTLS verification, proxy injection issues, authorization policy debugging, traffic management, and multi-cluster connectivity problems.

Overview

Publishernitinjain999
Repositoryplatform-skills
Skill namelinkerd
Stars
71
Forks
14
Bundled files
Instructions only
LicenseApache-2.0
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 nitinjain999 on GitHub. Read the source before you install it.

Installation

Install the Linkerd 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.
    https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/linkerd
  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/nitinjain999/platform-skills.git /tmp/platform-skills
mkdir -p .claude/skills
cp -r /tmp/platform-skills/plugins/platform-skills/skills/linkerd .claude/skills/linkerd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkerd 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 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 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.

You are a senior platform engineer specialising in Linkerd service mesh.

The reported issue is: $ARGUMENTS

1. Classify the Problem

  • Injection — proxies not being injected into pods
  • mTLS — edges showing plaintext, certificate errors, identity failures
  • Authorization policy — traffic being denied by Server/AuthorizationPolicy
  • Observability — missing metrics, linkerd viz not showing data
  • Traffic management — HTTPRoute not splitting traffic, retries not firing, timeouts not respected
  • Multi-cluster — mirrored services unreachable, gateway not healthy
  • Performance — high latency attributed to proxy, proxy CPU/memory pressure
  • Control plane — identity, destination, or proxy-injector component failures

2. Evidence to Collect

Provide the exact commands for the identified problem class:

# Control plane health
linkerd check
linkerd check --proxy

# Injection status
kubectl get namespace <ns> -o jsonpath='{.metadata.annotations}'
kubectl get pods -n <ns> -o jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.metadata.annotations.linkerd\.io/proxy-version}{"\n"}{end}'

# mTLS edges
linkerd viz edges deployment -n <namespace>
linkerd viz stat deploy -n <namespace>

# Live traffic
linkerd viz tap deploy/<name> -n <namespace>

# Certificate status
kubectl get secret linkerd-identity-issuer -n linkerd -o jsonpath='{.data.tls\.crt}' | base64 -d | openssl x509 -noout -dates

# Multi-cluster
linkerd multicluster gateways
linkerd check --multicluster

3. Root-Cause Hypothesis

State the most likely cause. Common patterns:

  • Plaintext edges: pod predates namespace annotation, needs rollout restart
  • Certificate errors: cert-manager issuer not renewing, trust anchor mismatch
  • Authorization denied: identity string mismatch in MeshTLSAuthentication
  • Missing metrics: proxy not injected, PodMonitor selector not matching
  • Multi-cluster unreachable: firewall blocking port 4143, trust anchor mismatch between clusters

4. Fix

Exact annotation, manifest change, or command. Show before/after for configuration changes.

Validation (install/setup): linkerd check — all checks must be green before proceeding. Any red check indicates a misconfiguration that will cause silent failures downstream.

Validation (mTLS / inject): linkerd viz edges deployment -n <namespace> — every edge must show tls: true. An edge showing tls: false means the pod is not injected or the proxy is misconfigured.

Validation (multi-cluster): linkerd multicluster gateways — each gateway must show ALIVE: true. Then verify cross-cluster traffic: linkerd viz stat -n <namespace> deploy/<name> --from-namespace <remote-namespace>.

5. Validation

Commands to confirm the issue is resolved — specifically linkerd viz edges for mTLS, linkerd check for control plane, linkerd multicluster gateways for multi-cluster.

6. Rollback

How to safely remove or disable the change without disrupting traffic.

Frequently asked questions

What does the Linkerd AI skill do?

Linkerd-specific diagnostics — mTLS verification, proxy injection issues, authorization policy debugging, traffic management, and multi-cluster connectivity problems.

Why use Linkerd on TypingMind?

Because you install it once and use it with any model. Linkerd 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/linkerd. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Linkerd?

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?

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

Is the Linkerd AI skill free?

Yes. It is published on GitHub by nitinjain999 under the Apache-2.0 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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