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Cloud Dev Environment

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elie222
cloud-dev-environment

Cursor Cloud VM setup and service startup instructions for local development

Overview

Publisherelie222
Repositoryinbox-zero
Skill namecloud-dev-environment
Stars
12.5K
Forks
1.6K
Bundled files
Instructions only
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 elie222 on GitHub. Read the source before you install it.

Installation

Install the Cloud Dev Environment 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/elie222/inbox-zero/tree/main/.claude/skills/cloud-dev-environment
  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/elie222/inbox-zero.git /tmp/inbox-zero
mkdir -p .claude/skills
cp -r /tmp/inbox-zero/.claude/skills/cloud-dev-environment .claude/skills/cloud-dev-environment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloud Dev Environment 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 Cloud Dev Environment 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 Cloud Dev Environment 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.

Cloud Development Environment

Services overview

  • Main app (apps/web): Next.js 16 app (Turbopack). Runs on port 3000.
  • PostgreSQL 16: Primary database. Runs on port 5432 via docker-compose.dev.yml.
  • Redis 7 + serverless-redis-http: Caching/rate-limiting. Redis on port 6380, HTTP proxy on port 8079.

Starting services

  1. Start Docker daemon if needed: sudo dockerd &>/dev/null &, then confirm with docker info (or sudo docker info if the client cannot reach the daemon yet).
  2. Start databases from repo root: docker compose -f docker-compose.dev.yml up -d. If you get permission denied on /var/run/docker.sock, run the same command with sudo docker compose instead of making the socket world-writable.
  3. Run Prisma migrations: cd apps/web && pnpm prisma:migrate:local (uses dotenv -e .env.local; do NOT use bare prisma migrate dev — it won't load .env.local).
  4. Start dev server: pnpm dev from repo root.

Environment file

The app reads apps/web/.env.local. Required non-obvious env vars beyond .env.example defaults:

  • DEFAULT_LLMS (e.g. openai:gpt-6-luna) — app crashes at startup without this.
  • GOOGLE_CLIENT_ID and GOOGLE_CLIENT_SECRET — hard-required by env.ts validation. Use Google emulator credentials for local dev (see below).
  • REDIS_HTTP_TOKEN must be set to dev_token to match the SRH_TOKEN default in docker-compose.dev.yml. UPSTASH_REDIS_TOKEN is still accepted.
  • MICROSOFT_WEBHOOK_CLIENT_STATE — required if MICROSOFT_CLIENT_ID is set.

Google emulator for local dev

Use the Google emulator instead of real OAuth credentials:

GOOGLE_CLIENT_ID=emulate-google-client.apps.googleusercontent.com
GOOGLE_CLIENT_SECRET=emulate-google-secret
GOOGLE_BASE_URL=http://localhost:4002

Start the emulator: docker compose -f docker-compose.dev.yml --profile google-emulator up -d.

Testing

  • pnpm test runs Vitest unit/integration tests (no DB or external services required).
  • pnpm lint runs Biome. Pre-existing lint warnings/errors in the repo are expected.
  • AI tests (pnpm test-ai) require a real LLM API key and are skipped by default.

Docker in this environment

The cloud VM is a Docker-in-Docker setup. Docker requires fuse-overlayfs storage driver and iptables-legacy. These are configured during initial setup. After snapshot restore, run sudo dockerd &>/dev/null & if Docker daemon is not running.

The VM may not have the Docker Compose v2 plugin pre-installed. If docker compose version fails, install it:

bash
sudo mkdir -p /usr/local/lib/docker/cli-plugins
sudo curl -fsSL https://github.com/docker/compose/releases/download/v2.29.2/docker-compose-linux-x86_64 -o /usr/local/lib/docker/cli-plugins/docker-compose
sudo chmod +x /usr/local/lib/docker/cli-plugins/docker-compose

Onboarding flow

After a fresh login via the Google emulator, the app forces an onboarding wizard. Some onboarding buttons require JavaScript click() calls rather than standard browser clicks (React event delegation quirk in headless/automation contexts).

Frequently asked questions

What does the Cloud Dev Environment AI skill do?

Cursor Cloud VM setup and service startup instructions for local development

Why use Cloud Dev Environment on TypingMind?

Because you install it once and use it with any model. Cloud Dev Environment 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 Cloud Dev Environment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elie222/inbox-zero/tree/main/.claude/skills/cloud-dev-environment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cloud Dev Environment?

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 Cloud Dev Environment?

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

Is the Cloud Dev Environment AI skill free?

It is published on GitHub by elie222. 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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