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Using Git Worktrees

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GanyuanRan
using-git-worktrees

Use when a coding task needs a concurrent checkout, unrelated dirty state blocks safe branch switching, or the user or repository explicitly requires a worktree.

Overview

PublisherGanyuanRan
RepositoryAegis
Skill nameusing-git-worktrees
Stars
1.2K
Forks
52
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 GanyuanRan on GitHub. Read the source before you install it.

Installation

Install the Using Git Worktrees 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/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/skills/using-git-worktrees .claude/skills/using-git-worktrees
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Git Worktrees 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 Git Worktrees 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 Git Worktrees 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.

Using Git Worktrees

Overview

A worktree is an exception for simultaneously checking out another Git history, not a general safety badge. Prefer the current workspace and reuse an existing suitable worktree. A branch decision and a worktree decision are separate.

Core principle: create only with necessity and ownership evidence; pair every created worktree with a cleanup path.

Announce at start: explain which concrete checkout conflict requires a worktree.

Step 0: Environment Detection

Before mutation, record the repository root, HEAD, branch/detached state, staged/unstaged/untracked paths, active Git operations, and:

bash
git worktree list --porcelain

Classify the current host surface's task/chat-bound managed workspace semantics as managed, non-managed, or unknown. Use trusted host/session context, an explicit host contract, or a host-native lifecycle result; do not infer the classification from a product name, directory prefix, command-level workdir, missing tool, or absent metadata. non-managed requires positive evidence that the current surface lacks task-bound managed semantics. When the host exposes workspace fields, record the task workspace and the command's default cwd without a per-command directory override.

Stop on unresolved conflicts, an active merge/rebase/cherry-pick/revert/bisect, or an unexplained detached HEAD. A detached HEAD is acceptable only when trusted host binding evidence identifies it as the intended state of the current managed worktree; Codex-managed worktrees, for example, start detached by default. Preserve user state: no automatic stash, reset, clean, broad staging, or commit.

Before deciding necessity or placement, read the smallest relevant project AGENTS.md, CLAUDE.md, current authority, and existing worktree convention. Repository policy may require or forbid isolation and owns any approved in-repo location; do not discover that policy after creating the checkout.

Step 1: Necessity Decision

Create at most one task-owned worktree only when one of these is true:

  • another branch must remain checked out while this task proceeds;
  • unrelated dirty state prevents a safe checkout in the current workspace;
  • explicit user or current repository authority requires it.

Task complexity, planning, TDD, use of subagents, or a main/master branch name is not sufficient. Same-task subagents share one workspace; do not create per-subagent or nested worktrees. If independent history is unnecessary, do not create a branch either.

Record: reason, branch, exact path, creating coordinator, and expected cleanup event. If the owner or cleanup condition is unknown, stop before creation.

Step 2: Safe Placement

Use an existing repository convention only when the target directory is already ignored (git check-ignore). Never edit or commit .gitignore solely to host a worktree. If no safe in-repo convention exists, use a task-specific external user-level temporary directory outside the repository. Resolve and verify the exact path before creation; do not target a broad home, workspace, or repo root.

Step 3: Create the Minimum Checkout

Prefer reusing an existing branch carrying the same goal. Create a branch only when the approved Git lifecycle requires independent history.

Managed-host binding gate

When the host surface declares task/chat-bound managed workspaces, use its native Worktree/Handoff lifecycle instead of shell creation. Reuse the current task only when trusted host evidence says it is already bound to the intended workspace. If the native operation creates or moves work into another task, continue only in that bound task.

After the native lifecycle operation, or immediately when entering/reusing an already-bound managed workspace, and before the first task content or Git history write, read back one joint postcondition:

  • trusted task workspace;
  • default command cwd without a command-level directory override;
  • intended Git worktree root;
  • intended HEAD and branch/detached state.

All four must describe the same execution environment. git worktree list, a managed-looking path, or a per-command workdir proves only part of that postcondition. If native creation/handoff is unavailable, only UI-driven, or cannot be verified, stop before shell worktree creation or task content/history writes and give the user the host-native re-entry action. Do not silently fall back on git worktree add. Compare resolved path identity using host-appropriate semantics, not raw path strings. The default cwd may equal the worktree root or be inside it; running Git from that default cwd must resolve to the intended worktree root. An intended host-managed detached HEAD satisfies the Git-state component; do not create a branch merely to make that state look like a generic checkout.

If an unbound manual worktree already exists, preserve it and report its exact path, HEAD, branch, status, and ownership evidence. Dirty, untracked, or ownership-unknown state is not auto-migrated, deleted, stashed, reset, or overwritten. A later commit/patch transfer follows normal Git authorization and must be verified in the new bound task before cleanup is offered.

Generic Git fallback

Use the shell path below only when trusted evidence positively classifies the current host/CLI surface as non-managed. An unknown classification fails closed; an absent native tool or missing binding metadata is not generic-fallback evidence.

bash
# Existing branch
git worktree add <exact-path> <branch>

# New independently justified branch
git worktree add -b <branch> <exact-path> <start-point>

Read back git worktree list --porcelain, the new worktree's HEAD/branch, and its status. Do not use force flags. A command-level workdir may target this checkout on a generic host, but it never proves managed task binding.

Step 4: Authority-Led Setup and Baseline

Read the project's install docs, lockfiles, and existing setup scripts. Run only setup the project explicitly requires. Do not infer npm install, cargo build, pip install, poetry install, or dependency downloads merely from manifest presence.

Run the smallest relevant baseline verification. If setup or tests fail, report the exact failure and preserve the worktree; do not hide it by mutating unrelated files.

Step 5: Handoff and Cleanup Ownership

Report the exact path, branch, start HEAD, baseline result, owner, and cleanup trigger. The creating coordinator remains responsible until it proves cleanup or explicitly hands ownership off.

Use finishing-a-development-branch after the task commit or integration decision. A worktree may usually be removed while its unmerged branch remains; an open PR does not require a permanent checkout.

Red Flags

Never:

  • create a worktree only because a task modifies code;
  • modify .gitignore merely to make worktree creation possible;
  • install dependencies blindly;
  • create one worktree per subagent;
  • treat Git readback, a path prefix, or command-level workdir as proof of managed task binding;
  • use shell worktree creation merely because a host-native capability is deferred, UI-only, or absent from the initial tool list;
  • run global prune or force cleanup as routine hygiene;
  • remove dirty, untracked, locked, user-owned, or ownership-unknown resources.

Integration

Called conditionally by planning/execution workflows only after the necessity decision. Pairs with finishing-a-development-branch for evidence-led cleanup.

Frequently asked questions

What does the Using Git Worktrees AI skill do?

Use when a coding task needs a concurrent checkout, unrelated dirty state blocks safe branch switching, or the user or repository explicitly requires a worktree.

Why use Using Git Worktrees on TypingMind?

Because you install it once and use it with any model. Using Git Worktrees 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 Git Worktrees in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GanyuanRan/Aegis/tree/main/skills/using-git-worktrees. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Using Git Worktrees?

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 Git Worktrees?

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

Is the Using Git Worktrees AI skill free?

Yes. It is published on GitHub by GanyuanRan 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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