Using Medusa Cloud logo

Using Medusa Cloud

Organization
medusajs
using-medusa-cloud

Manages Medusa Cloud resources through the Cloud CLI (mcloud). Use when deploying, debugging deployments, managing environments, environment variables, or any Medusa Cloud operation. CRITICAL for mcloud commands, deployment failures, build logs, Cloud setup, and CI/CD workflows.

Overview

Publishermedusajs
Repositorymedusa-agent-skills
Skill nameusing-medusa-cloud
Stars
218
Forks
27
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Using Medusa Cloud 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/medusajs/medusa-agent-skills.git /tmp/medusa-agent-skills
mkdir -p .claude/skills
cp -r /tmp/medusa-agent-skills/plugins/medusa-cloud/skills/using-medusa-cloud .claude/skills/using-medusa-cloud
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Medusa Cloud 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 Using Medusa Cloud 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 Using Medusa Cloud 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.

Managing Medusa Cloud Resources

Operational guide for AI agents managing Medusa Cloud infrastructure through the mcloud CLI. Covers setup, deployments, debugging, environments, and variables.

Constraints

  • Always pass --json when parsing CLI output. Plaintext output is for humans and may change without warning.
  • Confirm context before mutating. Run mcloud whoami --json before any state change.
  • Read before you write. Run a get or list before any delete, redeploy, or trigger-build.
  • Use --yes for destructive operations. delete commands (including variables delete) require --yes in non-interactive mode.
  • Variable changes need a deploy to apply. variables set/delete don't rebuild or redeploy: redeploy for runtime changes, trigger-build for build changes.
  • Production environments cannot be deleted. mcloud environments delete errors on production by design.
  • Never pass --reveal unless the user explicitly asks. Secret values appear in terminal scrollback and logs.
  • --json and --follow are incompatible. Use bounded time windows (--from/--to) with --json for programmatic log ingestion.

CRITICAL: Load Reference Files When Needed

Load these references based on what you're doing:

  • Setting up the CLI? → MUST load setup.md first
  • Debugging a failed deployment? → MUST load debugging-deployments.md first
  • Managing environments or variables? → MUST load environments-and-variables.md first

Minimum requirement: Load at least one reference file before executing multi-step workflows.

Quick Reference

Authentication Check

Always verify auth and scope before mutating state:

bash
mcloud whoami --json | jq -e '.auth.kind != "none" and .organization.id != null'

Exit code 0 = authenticated and scoped. Non-zero = stop and ask the user.

Set Context Once

bash
mcloud use \
  --organization org_123 \
  --project proj_123 \
  --environment production

CRITICAL: mcloud use without flags is interactive and fails in CI/Docker/piped input. Always pass flags.

Deployment Status Routing

Route on backend_status (or storefront_status):

StatusMeaningLogs to check
build-failedBuild step failedmcloud deployments build-logs <id>
deployment-failedRuntime crashed after buildmcloud logs --deployment <id>
timed-outExceeded time budgetBoth: build-logs first, then runtime logs

Redeployment Decision

CommandWhen to use
mcloud environments redeploy <env>Fix is environment-side (variable change, infra) — reruns existing build
mcloud environments trigger-build <env>Fix is in source code on the tracked branch — starts new build

Common Pitfalls

  • TTY-only commands. mcloud login, mcloud use (without flags), and delete without --yes require a TTY. They fail in CI, Docker, or piped input.
  • MCLOUD_TOKEN precedence. When set, file-based credentials are ignored and mcloud login is rejected. Unset it to switch accounts.
  • Personal vs org access keys. Personal keys require --organization; org keys are pre-scoped.
  • organizations list requires personal auth. Org access keys return 401 on this command.
  • Build IDs vs deployment IDs. depl_* = deployment ID; anything else = build ID (resolved to latest deployment). mcloud logs --deployment accepts both; other commands take build IDs only.
  • mcloud local build has no --json. It streams plaintext and reports success via its exit code (0 = success). Requires Docker and must run inside the project's Git repo. Use it to reproduce build-failed failures locally — see debugging-deployments.md.

Reference Files

setup.md                       - CLI installation, authentication, context setup
debugging-deployments.md       - Build/deployment failure recipes and log analysis
environments-and-variables.md  - Environment lifecycle and variable management

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Using Medusa Cloud AI skill do?

Manages Medusa Cloud resources through the Cloud CLI (mcloud). Use when deploying, debugging deployments, managing environments, environment variables, or any Medusa Cloud operation. CRITICAL for mcloud commands, deployment failures, build logs, Cloud setup, and CI/CD workflows.

Why use Using Medusa Cloud on TypingMind?

Because you install it once and use it with any model. Using Medusa Cloud 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 Using Medusa Cloud in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/medusajs/medusa-agent-skills/tree/main/plugins/medusa-cloud/skills/using-medusa-cloud. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Using Medusa Cloud?

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 Using Medusa Cloud?

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

Is the Using Medusa Cloud AI skill free?

It is published on GitHub by medusajs. Check the repository for licensing terms. 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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