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Ship

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poshan0126
ship

Commit, push, and create a PR for staged changes, with confirmation at each step. Use when your work is done and you want to ship.

Overview

Publisherposhan0126
Repositorydotclaude
Skill nameship
Stars
870
Forks
204
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 poshan0126 on GitHub. Read the source before you install it.

Installation

Install the Ship 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/poshan0126/dotclaude/tree/main/skills/ship
  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/poshan0126/dotclaude.git /tmp/dotclaude
mkdir -p .claude/skills
cp -r /tmp/dotclaude/skills/ship .claude/skills/ship
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ship 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 Ship 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 Ship 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.

Ship the current changes through commit, push, and PR creation. Confirm with the user before each step using the AskUserQuestion tool.

Step 1: Scan

Current scan state (injected read-only below — no need to re-run these to gather it):

  • Status: !git -C "${CLAUDE_PROJECT_DIR:-.}" status --short 2>/dev/null || echo "(no git repo or no changes)"
  • Change summary: !git -C "${CLAUDE_PROJECT_DIR:-.}" diff --stat HEAD 2>/dev/null || echo "(no diff)"
  • Recent commit style: !git -C "${CLAUDE_PROJECT_DIR:-.}" log --oneline -5 2>/dev/null || echo "(no commits yet)"

For the full patch of a specific file before committing, run git diff -- <file> on demand (kept out of the injection to hold the render small).

  • Present a clear summary to the user:
    • Files modified
    • Files added
    • Files deleted
    • Untracked files
  • If there are no changes, tell the user and stop

Step 2: Stage & Commit

  • Propose which files to stage. Never stage these:
    • Secrets: .env*, *.pem, *.key, credentials.json
    • Lock files: package-lock.json, yarn.lock, pnpm-lock.yaml (unless intentionally updated)
    • Generated: *.gen.ts, *.generated.*, *.min.js, *.min.css
    • Build output: dist/, build/, .next/, __pycache__/
    • Dependencies: node_modules/, vendor/, .venv/
    • OS/editor: .DS_Store, Thumbs.db, *.swp, .idea/, .vscode/settings.json
  • Draft a commit message based on the changes, matching the repo's existing commit style
  • ASK the user to confirm or edit: show the exact files to stage and the proposed commit message
  • Only after confirmation: stage the files and create the commit
  • If the commit fails (e.g., pre-commit hook), fix the issue and try again with a NEW commit

Step 3: Push

  • Check if the current branch has an upstream remote
  • If not, propose creating one with git push -u origin <branch>
  • ASK the user to confirm before pushing
  • Only after confirmation: push to remote

Step 4: Pull Request

  • Check if a PR already exists for this branch (gh pr view. If it exists, show the URL and stop)
  • Analyze ALL commits on this branch vs the base branch (not just the latest commit)
  • Draft a PR title (under 72 chars) and body with:
    • Summary: 2-4 bullet points
    • Test plan: how to verify
  • ASK the user to confirm or edit the title and body
  • Only after confirmation: create the PR with gh pr create
  • Show the PR URL when done

Step 5: Branch cleanup (optional)

After the PR is created (or if invoked when everything is already shipped), offer to clean up stale local branches:

  • git fetch --prune, then find branches whose upstream is gone: git for-each-ref --format '%(refname:short) %(upstream:track)' refs/heads | grep '\[gone\]'
  • Show the list and ASK before deleting. Delete with git branch -d only.
  • If a branch isn't fully merged (-d refuses), list it separately — deleting unmerged branches needs the user to explicitly say so, every time.

Rules

  • NEVER skip a confirmation step. Each step requires explicit user approval
  • NEVER force-push
  • NEVER commit .env, secrets, or credential files
  • If the user says "skip" at any step, skip that step and move to the next
  • If $ARGUMENTS is provided, use it as the commit message / PR title

Frequently asked questions

What does the Ship AI skill do?

Commit, push, and create a PR for staged changes, with confirmation at each step. Use when your work is done and you want to ship.

Why use Ship on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/poshan0126/dotclaude/tree/main/skills/ship. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ship?

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 Ship?

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

Is the Ship AI skill free?

Yes. It is published on GitHub by poshan0126 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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