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Openship Config

CommunityPopular
fcakyon
openship-config

This skill should be used when the user asks to "create openship.json", "configure an OpenShip deployment", "make a repo deployable on OpenShip", or fix "openship config validate" errors.

Overview

Publisherfcakyon
Repositoryclaude-codex-settings
Skill nameopenship-config
Stars
1.1K
Forks
109
Bundled files
6
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.

  • 6 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 Config 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-config .claude/skills/openship-config
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openship Config 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 Config 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 Config 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.

Design openship.json

Use OpenShip's upstream configuration workflow: inspect the repo, declare only necessary overrides, then validate. Auto-detection supplies omitted fields. Prefer self-hosted deployment unless the user selects cloud.

Design the configuration

  1. Inspect package scripts, lockfiles, framework settings, Dockerfiles, compose files, workspace layout, and an existing openship.json.
  2. Choose a single app, compose services, or detected monorepo apps. Do not combine those shapes casually. monorepo.apps overrides detected apps by root directory. It does not discover new applications.
  3. Read fields for names and meanings and the bundled JSON schema for editor validation. Use compose guidance for service topology, ports, dependencies, and persistent volumes.
  4. Start with openship config init only when the file is absent. Preserve an existing config and add only values that need to override detection. Keep its $schema set to https://openship.io/openship.schema.json.
  5. Run openship --json config validate using the deployment's CLI version. Fix errors and review warnings: unknown keys can be ignored by the runtime even though the editor schema rejects them.

A minimal server config can be:

json
{
  "$schema": "https://openship.io/openship.schema.json",
  "port": 3000,
  "runtime": "docker",
  "domains": ["app.example.com"]
}

Self-hosted details

  • Self-hosted defaults to unlimited resources in the bundled release. Declare caps only when intended. Service-level caps override project caps field by field. Check target capacity separately from local config validation.
  • Use named persistent volumes for state that must survive an application deploy. Expose only the services intended to receive public traffic.
  • Keep actual secret values out of committed JSON. secret: true controls storage handling after ingestion. It does not encrypt a credential in a Git file. Read environment variables for instance-managed values.
  • openship.json expresses desired configuration. It does not prove an existing domain has moved or the running deployment has changed. The companion openship-deploy skill handles deployment and live verification.

Source and version

The workflow and field reference derive from the official upstream config skill. upstream.json records the release and links to its CLI validator, runtime parser, types, parser tests, and deployment preparation code. Consult those sources when docs disagree or a behavior depends on the installed version. The parser and actual deployment implementation decide runtime behavior. The JSON schema serves editor validation.

Read the configuration guide for additional examples. Do not copy example credentials into a repository.

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 Config AI skill do?

This skill should be used when the user asks to "create openship.json", "configure an OpenShip deployment", "make a repo deployable on OpenShip", or fix "openship config validate" errors.

Why use Openship Config on TypingMind?

Because you install it once and use it with any model. Openship Config 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 Config 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-config. 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 Config?

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 Config?

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

Is the Openship Config 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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