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Openship Deploy

CommunityPopular
fcakyon
openship-deploy

This skill should be used when the user asks to "deploy with OpenShip", "manage a self-hosted OpenShip instance", "debug an OpenShip deployment", or manage its domains, storage, backups, or upgrades through the CLI.

Overview

Publisherfcakyon
Repositoryclaude-codex-settings
Skill nameopenship-deploy
Stars
1.1K
Forks
109
Bundled files
14
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 14 bundled files

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

  • Open source

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

Installation

Install the Openship Deploy 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/fcakyon/claude-codex-settings.git /tmp/claude-codex-settings
mkdir -p .claude/skills
cp -r /tmp/claude-codex-settings/plugins/openship-skills/skills/openship-deploy .claude/skills/openship-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openship Deploy 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 Openship Deploy 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 Openship Deploy 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.

Deploy and operate OpenShip

Prefer the CLI and an existing self-hosted instance. Use a cloud or desktop workflow when the user requests it. Keep application deployments separate from installing or upgrading the OpenShip control plane.

Choose the instance and command

  • Check openship --version and openship context list. Select the intended self-hosted context before a remote operation. Never assume the active context points to the user's server.
  • Use native commands with the global --json flag. Read CLI access for context and authentication details. Do not print stored tokens.
  • Check the installed command's --help. Use openship api for routes without a working command, after checking the matching implementation. In v0.7.2, service exec and server ssh are stubs despite appearing in help. A successful help command does not prove an operation works.
  • upstream.json records the bundled release, exact source URLs, and hashes. If the installed CLI or server differs, verify the relevant source at that version before relying on a flag or API contract.

Deploy an application

  1. Inspect the repository, existing project link, build entry point, service topology, persistent volumes, and current domain ownership. Use projects and services for commands.
  2. Use the companion openship-config skill to author a small openship.json. When only this ZIP is installed, use the official config guide. Keep repeatable build and routing choices in the repository. Keep actual credentials in the instance's environment settings.
  3. Run openship --json config validate, then use deployment commands for the appropriate repository or folder deploy. A valid file does not prove its image builds or that the destination has sufficient capacity.
  4. Check deployment completion, the active deployment, service health, logs, and the public URL. Report the deployed revision and any checks still pending. Use edge and monitoring for diagnosis.

Read only the guide needed for the operation:

  • Self-hosted GitHub App: repository authorization and automatic deployment.
  • Domains: ownership, DNS, ports, and HTTPS. Reconcile existing ownership before moving a hostname.
  • Persistent storage: volumes and data that must survive redeployment.

Maintain the self-hosted instance

Use self-host infrastructure for servers and backups, and running OpenShip for lifecycle commands. Inspect how the instance was installed and whether it has a custom updater before selecting an upgrade command.

Use the user's existing authorization for the requested operation. Preparing a deployment is not authorization to replace a running instance or restore over its data.

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 Openship Deploy AI skill do?

This skill should be used when the user asks to "deploy with OpenShip", "manage a self-hosted OpenShip instance", "debug an OpenShip deployment", or manage its domains, storage, backups, or upgrades through the CLI.

Why use Openship Deploy on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/openship-skills/skills/openship-deploy. 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 Openship Deploy?

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 Openship Deploy?

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

Is the Openship Deploy AI skill free?

Yes. It is published on GitHub by fcakyon 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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