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Prp Worktree

CommunityPopular
Wirasm
prp-worktree

Git-native worktree management via a bundled single-file CLI - create, list (with per-worktree git stats), and safely tear down isolated checkouts under .worktrees/ for parallel workstreams. Use when the user or an orchestrating skill wants to "create a worktree", "work on this in a separate worktree", "spin up an isolated checkout", "list my worktrees", "clean up / remove a worktree", or invokes /prp-worktree.

Overview

PublisherWirasm
Repositoryprp
Skill nameprp-worktree
Stars
2.2K
Forks
607
Bundled files
1
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.

  • 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 Wirasm on GitHub. Read the source before you install it.

Installation

Install the Prp 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/Wirasm/prp.git /tmp/prp
mkdir -p .claude/skills
cp -r /tmp/prp/plugins/prp-core/skills/prp-worktree .claude/skills/prp-worktree
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prp 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 Prp 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 Prp 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.

PRP Worktree

Manage isolated git worktrees for parallel workstreams with one bundled script — deterministic create/teardown with safety rails, instead of improvised git worktree incantations.

Input: $ARGUMENTS (if absent, infer the subcommand and worktree name from the conversation).

Run it

All operations are one command (never re-implement them with raw git):

bash
uv run ${CLAUDE_PLUGIN_ROOT}/skills/prp-worktree/scripts/worktree.py create <name> [--base <branch>]
uv run ${CLAUDE_PLUGIN_ROOT}/skills/prp-worktree/scripts/worktree.py list [--base <branch>] [--json]
uv run ${CLAUDE_PLUGIN_ROOT}/skills/prp-worktree/scripts/worktree.py remove <name> [--force] [--delete-branch]
  • create — worktree at .worktrees/<name> on branch <name> (new from --base, or checked out if the branch already exists). Prints the absolute worktree path as its final linecd there to start working.
  • list — every managed worktree with branch, ahead/behind vs base, dirty file count, diffstat (files +ins/-dels), merged-into-base, and last-commit age. --json for machine consumption.
  • remove — refuses if the worktree has uncommitted changes (--force discards). The branch is kept by default (with a pushed-to-origin report); --delete-branch deletes it only if merged into base (--force overrides). Never force without first investigating what would be lost.

Conventions

  • Worktrees live inside the repo at .worktrees/<name> — within sandbox-writable roots on any agent harness — and are auto-excluded from git via .git/info/exclude; they never appear in git status.
  • Branch name = worktree name (slashes allowed in branch; directory name flattens / to -).
  • Default --base is the repo's default branch (origin HEAD), falling back to the current branch.
  • The script always operates on the main checkout, wherever it is run from — including from inside a worktree.

Gotchas

  • Requires uv and git on PATH; the script is stdlib-only (PEP 723, Python ≥ 3.10).
  • remove deletes the checkout, not the work: commits live on the branch and survive; only --force on a dirty worktree discards uncommitted changes.
  • Orchestrating skills compose this by name ("use the prp-worktree skill to create <name>") — one worktree per parallel agent prevents checkout collisions.

Resources

  • scripts/worktree.py — the CLI (run it, don't read it); --help on any subcommand for exact flags.

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 Prp Worktree AI skill do?

Git-native worktree management via a bundled single-file CLI - create, list (with per-worktree git stats), and safely tear down isolated checkouts under .worktrees/ for parallel workstreams. Use when the user or an orchestrating skill wants to "create a worktree", "work on this in a separate worktree", "spin up an isolated checkout", "list my worktrees", "clean up / remove a worktree", or invokes /prp-worktree.

Why use Prp Worktree on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Wirasm/prp/tree/development/plugins/prp-core/skills/prp-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 Prp 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 Prp Worktree?

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

Is the Prp Worktree AI skill free?

Yes. It is published on GitHub by Wirasm 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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