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Mirrord Quickstart

OrganizationPopular
metalbear-co
mirrord-quickstart

Guide users from zero to their first working mirrord session — check system requirements, install mirrord via CLI, VS Code, or IntelliJ, run a first session against a Kubernetes target, and verify the connection. Use when a user is new to mirrord, wants to install it, needs help running their first session, or wants to debug or test a local process against their Kubernetes cluster.

Overview

Publishermetalbear-co
Repositorymirrord
Skill namemirrord-quickstart
Stars
5.4K
Forks
220
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 metalbear-co on GitHub. Read the source before you install it.

Installation

Install the Mirrord Quickstart 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/metalbear-co/mirrord/tree/main/mirrord/mcp/corpus/skills/mirrord-quickstart
  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/metalbear-co/mirrord.git /tmp/mirrord
mkdir -p .claude/skills
cp -r /tmp/mirrord/mirrord/mcp/corpus/skills/mirrord-quickstart .claude/skills/mirrord-quickstart
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mirrord Quickstart 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 Mirrord Quickstart 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 Mirrord Quickstart 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.

Mirrord Quickstart Skill

Purpose

Help new users get mirrord running quickly:

  • Check system requirements
  • Install mirrord (CLI, VS Code, or IntelliJ)
  • Connect to their first Kubernetes target
  • Verify the connection works

Go one step at a time — don't overwhelm a new user with all options at once, and when they first connect, explain what just happened.

Critical First Steps

Step 1: Detect user's environment Ask or detect:

  • Operating system (macOS, Linux, Windows)
  • Preferred workflow (CLI, VS Code, IntelliJ)
  • Do they have kubectl configured?

Step 2: Verify requirements

bash
# Check kubectl access
kubectl cluster-info
kubectl get pods -A | head -5

If kubectl fails, help them configure it first.

Installation Paths

CLI (recommended for getting started)

Do not run remote install scripts that pipe a network download into a shell interpreter. Use only methods your organization approves.

Official guide: Follow mirrord installation documentation for supported options (package managers, pinned release binaries with checksum verification, etc.).

Summary for the agent:

  • macOS / Linux: Point the user to the official docs for Homebrew, apt, or pinned binary install steps — do not invent or paste one-liners that fetch and execute remote scripts.
  • Windows: Point the user to the official docs for supported installers.

Security: Prefer package managers or manually verified binaries from official release artifacts. Never execute installation by piping downloaded content into a shell.

Verify installation:

bash
mirrord --version

VS Code Extension

  1. Open VS Code Extensions (Cmd/Ctrl+Shift+X)
  2. Search "mirrord"
  3. Install "mirrord" by MetalBear
  4. Look for mirrord icon in status bar

IntelliJ Plugin

  1. Open Settings → Plugins → Marketplace
  2. Search "mirrord"
  3. Install and restart IDE
  4. Find mirrord in navigation toolbar

First Session

CLI approach

bash
# List available targets
mirrord ls

# Run a local process with mirrord
mirrord exec --target pod/<pod-name> -- <your-command>

# Example: Node.js app
mirrord exec --target pod/api-server-7c8d9 -- node app.js

# Example: Python app
mirrord exec --target pod/backend-abc123 -- python main.py

If mirrord ls returns no targets, check that the kubeconfig context points at the right cluster (kubectl config current-context) and list pods in the expected namespace (kubectl get pods -n <namespace>).

IDE approach

  1. Enable mirrord (click status bar icon / toolbar button)
  2. Select target pod when prompted
  3. Run/debug your application normally
  4. mirrord intercepts and connects automatically

Verification

After running, verify the connection:

  1. Check logs - mirrord's startup progress (agent started, connected to the target) prints before your app's own output:
bash
mirrord exec --target pod/<pod-name> -- <your-command>
  1. Test environment - Remote env vars should be available locally:
bash
mirrord exec --target pod/<pod-name> -- env | grep -i database
  1. Test network - Send a request to the remote service; it should reach the local process (check the local process's output):
bash
curl http://<remote-service-url>

What's Next

Once the first session works, point users to:

  • mirrord ui — launches a local dashboard showing every active session on the machine, with a live event stream (HTTP requests, file operations, DNS, outgoing connections). See Local UI.
  • mirrord up — for debugging several microservices together from one mirrord-up.yaml (compose-style). Point to the mirrord-up skill.
  • Browser extension — routes requests made from Chrome to the local process, for testing an HTTP-filtered session from a browser instead of curl. See Debugging from Browser.
  • mirrord for Teams features (DB branching, queue splitting, preview environments, concurrent steal on one target) need the Operator. On a cluster with no license, an AI agent can offer the user a seven-day Enterprise trial and start it once they agree: point to the mirrord-operator skill, "Agent-started trial".

Common First-Timer Issues

IssueSolution
"kubectl not found"Install kubectl and configure cluster access
"No pods found"Check namespace: kubectl get pods -n <namespace>
"Permission denied"Check RBAC permissions for your kubectl context
"Agent failed to start"Ensure cluster runs Linux kernel 4.20+

Frequently asked questions

What does the Mirrord Quickstart AI skill do?

Guide users from zero to their first working mirrord session — check system requirements, install mirrord via CLI, VS Code, or IntelliJ, run a first session against a Kubernetes target, and verify the connection. Use when a user is new to mirrord, wants to install it, needs help running their first session, or wants to debug or test a local process against their Kubernetes cluster.

Why use Mirrord Quickstart on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/metalbear-co/mirrord/tree/main/mirrord/mcp/corpus/skills/mirrord-quickstart. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mirrord Quickstart?

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 Mirrord Quickstart?

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

Is the Mirrord Quickstart AI skill free?

Yes. It is published on GitHub by metalbear-co 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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