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Dev Worktree

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codeaholicguy
dev-worktree

AI DevKit · Worktree setup and resume guidance for isolated feature work. Use when starting, resuming, switching, or verifying a feature branch/worktree for lifecycle, debugging, implementation, review, or multi-agent workflows.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedev-worktree
Stars
1.6K
Forks
252
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Dev Worktree 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/dev-worktree .claude/skills/dev-worktree
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev Worktree 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 Dev Worktree 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 Dev Worktree 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.

Dev Worktree

Set up or resume the correct workspace before feature work. Keep this skill focused on repository context, worktree isolation, and dependency bootstrap. Do not perform requirements, design, planning, implementation, testing, or review work here.

Phase Contract

  1. Propose the exact workspace plan before changing branch or worktree state.
  2. Confirm the target branch/worktree with the user before switching contexts.
  3. Use feature-<name> for branch and worktree names, where <name> is normalized kebab-case without the prefix.
  4. Prefer a project-local worktree at <project-root>/.worktrees/feature-<name>.
  5. Use no-worktree mode only when the user explicitly requests it.
  6. Run all follow-up commands in the verified target context.

Start Feature Workspace

Use for a new feature start.

  1. Normalize feature name to kebab-case <name>.
  2. Determine the project root, the directory containing .git.
  3. If the user explicitly requests no worktree:
    • Continue in the current repository and branch.
    • Call out that branch/workspace isolation is reduced.
    • Skip to dependency bootstrap.
  4. Otherwise use branch/worktree name feature-<name>.
  5. Ensure .worktrees is listed in the project .gitignore; if not, add it.
  6. If branch does not exist, run git worktree add -b feature-<name> .worktrees/feature-<name>.
  7. If branch exists and the target worktree does not, run git worktree add .worktrees/feature-<name> feature-<name>.
  8. If the target worktree already exists, reuse it after verifying it is clean enough for the requested work.
  9. Verify worktree context with git -C .worktrees/feature-<name> branch --show-current; it must equal feature-<name>.
  10. Return the active workdir path for the next phase.

Resume Feature Workspace

Use when continuing an existing feature.

  1. Check current branch with git branch --show-current.
  2. Check available worktrees with git worktree list.
  3. Prefer <project-root>/.worktrees/feature-<name> when it exists.
  4. Otherwise use branch feature-<name> in the current repository.
  5. Include the selected target in the plan and wait for approval before switching.
  6. After approval, run future phase commands in the selected context.

Dependency Bootstrap

After selecting the target context:

  1. Detect ecosystem from lockfiles, manifests, and tooling configs.
  2. Prefer deterministic lockfile-based installs.
  3. Use the repository-native command:
    • JavaScript/TypeScript: npm ci, pnpm install --frozen-lockfile, yarn install --frozen-lockfile, or bun install --frozen-lockfile.
    • Python: uv sync, poetry install --no-interaction, pipenv sync, or pip install -r requirements.txt.
    • Ruby: bundle install.
    • Rust: cargo fetch, or cargo build when fetch-only is insufficient.
    • Go: go mod download.
    • Java/Kotlin: ./gradlew dependencies, ./gradlew build, or Maven equivalent.
  4. For workspaces whose packages resolve against built artifacts (for example dist/ output consumed via workspace aliases), run the repository build (for example npm run build) after install. Full-suite validation and commit hooks fail on stale or missing artifacts with errors that look like code regressions. State when the build was skipped and why.
  5. If no dependency manager is clearly detectable, continue and state what was checked.

Output

End with:

  • Active workdir.
  • Branch name.
  • Whether worktree or no-worktree mode is active.
  • Dependency bootstrap command run, or why it was skipped.
  • Workspace build command run (when applicable), or why it was skipped.
  • Any workspace risks or blockers.

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 Dev Worktree AI skill do?

AI DevKit · Worktree setup and resume guidance for isolated feature work. Use when starting, resuming, switching, or verifying a feature branch/worktree for lifecycle, debugging, implementation, review, or multi-agent workflows.

Why use Dev Worktree on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/dev-worktree. 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 Dev Worktree?

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 Dev Worktree?

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

Is the Dev Worktree AI skill free?

Yes. It is published on GitHub by codeaholicguy 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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