Worktree Lifecycle logo

Worktree Lifecycle

Organization
MadAppGang
worktree-lifecycle

Use when starting isolated feature work or before executing implementation plans. Manages full worktree lifecycle from creation through cleanup with safety checks and error recovery.

Overview

PublisherMadAppGang
Repositoryclaude-code
Skill nameworktree-lifecycle
Stars
281
Forks
26
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 MadAppGang on GitHub. Read the source before you install it.

Installation

Install the Worktree Lifecycle 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/MadAppGang/claude-code.git /tmp/claude-code
mkdir -p .claude/skills
cp -r /tmp/claude-code/plugins/dev/skills/discipline/worktree-lifecycle .claude/skills/worktree-lifecycle
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Worktree Lifecycle 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 Worktree Lifecycle 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 Worktree Lifecycle 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.

Worktree Lifecycle

Purpose: Manage git worktrees for isolated feature work with safety checks, multi-stack setup, and clean lifecycle management.

When to Use

This skill applies whenever you:

  • Start a new feature that requires isolated workspace
  • Execute risky or experimental changes
  • Need to keep current work untouched while testing alternatives
  • Coordinate parallel agent work (each agent in separate worktree)
  • Follow an implementation plan requiring clean environment
  • Preserve long-running feature work across sessions

Trigger keywords: experiment, prototype, risky, breaking, parallel, isolate, worktree, workspace

Red Flags (Anti-Patterns)

  • Creating worktree without checking if branch already exists
  • Creating worktree without checking if directory path is available
  • Forgetting to add worktree directory to .gitignore
  • Manually deleting worktree with rm -rf (use git worktree remove)
  • Removing worktree with uncommitted changes without user confirmation
  • Using same branch in multiple worktrees simultaneously
  • Working in worktree after returning to main directory (stale CWD)
  • Not storing original CWD before entering worktree
  • Skipping dependency installation in new worktree
  • Not running baseline tests before starting work

6-Phase Lifecycle

Phase 1: Pre-flight Checks

Purpose: Verify all prerequisites before creating anything.

Required checks (all must pass):

bash
# 1. Verify git repository
git rev-parse --git-dir &>/dev/null
if [ $? -ne 0 ]; then
  ERROR: "Not a git repository"
  RECOVERY: Abort with clear error message
fi

# 2. Detect detached HEAD state
if ! git symbolic-ref HEAD &>/dev/null; then
  WARNING: "Currently in detached HEAD state"
  RECOVERY: Ask user if they want to create branch from current commit
fi

# 3. Check if branch already in a worktree
if git worktree list | grep -q "$BRANCH"; then
  ERROR: "Branch $BRANCH already checked out in a worktree"
  RECOVERY: Ask user - reuse existing, rename branch, or cancel
fi

# 4. Check if branch exists (but not in worktree)
if git branch --list "$BRANCH" | grep -q .; then
  WARNING: "Branch $BRANCH already exists"
  RECOVERY: Ask user - use existing branch or create new with different name
fi

# 5. Check if target path is available
# IMPORTANT: Create directory first before checking (git check-ignore needs path to exist)
mkdir -p "$WORKTREE_DIR"
if [ -d "$WORKTREE_PATH" ]; then
  ERROR: "Directory $WORKTREE_PATH already exists"
  RECOVERY: Ask user - choose different path or remove existing
fi

Error Recovery Table:

ErrorDetectionRecovery
Not a git repogit rev-parse --git-dir failsAbort with clear error
Detached HEADgit symbolic-ref HEAD failsAsk: create branch from commit?
Branch in worktreegit worktree list | grep $BRANCH succeedsAsk: reuse, rename, or cancel
Branch existsgit branch --list $BRANCH returns resultAsk: use existing or rename
Path existsDirectory already presentAsk: choose different path or remove
Parent not writableCannot create parent directoryAbort with permission error

Phase 2: Directory Selection

Priority order (cascading, first match wins):

  1. Check for existing .worktrees/ directory → use it (no prompt)
  2. Check for existing worktrees/ directory → use it (no prompt)
  3. Check CLAUDE.md for worktree preference → follow it (no prompt)
  4. Ask user (only if no default found)

For orchestrator integration: Pass WORKTREE_DIR=".worktrees" to skip this phase entirely.

Example detection:

bash
if [ -d ".worktrees" ]; then
  WORKTREE_DIR=".worktrees"
elif [ -d "worktrees" ]; then
  WORKTREE_DIR="worktrees"
elif grep -q "worktree.*directory" CLAUDE.md 2>/dev/null; then
  WORKTREE_DIR=$(grep "worktree.*directory" CLAUDE.md | extract_path)
else
  # Ask user via AskUserQuestion
  # Options: .worktrees/, worktrees/, custom
fi

Phase 3: Creation

Steps (must be executed in order):

bash
# 1. Verify .gitignore safety
# IMPORTANT: Directory must exist before git check-ignore
mkdir -p "$WORKTREE_DIR"

if ! git check-ignore -q "$WORKTREE_DIR" 2>/dev/null; then
  echo "$WORKTREE_DIR/" >> .gitignore
  git add .gitignore
  git commit -m "chore: add $WORKTREE_DIR to .gitignore"
fi

# 2. Store original CWD (critical for Phase 6 cleanup)
ORIGINAL_CWD=$(pwd)

# 3. Create worktree
git worktree add "$WORKTREE_PATH" -b "$BRANCH"

# 4. Verify creation succeeded
if [ ! -d "$WORKTREE_PATH" ]; then
  ERROR: "Worktree creation failed"
  RECOVERY: Clean up any partial state, report error
  exit 1
fi

# 5. Change to worktree directory
cd "$WORKTREE_PATH"

# 6. Initialize submodules (if any)
if [ -f .gitmodules ]; then
  git submodule update --init --recursive
fi

# 7. Write statusline worktree marker (persists across compaction)
if [ -n "$SESSION_ID" ]; then
  cat > "$HOME/.claude/.statusline-worktree-${SESSION_ID}" <<MARKER_EOF
{
  "worktree_path": "$WORKTREE_PATH",
  "branch": "$BRANCH",
  "worktree_name": "$(basename "$WORKTREE_PATH")"
}
MARKER_EOF
fi

Error handling: If git worktree add fails, clean up any partial state and report the error to user.

Phase 4: Setup

Multi-stack detection:

bash
# Detect all present stacks
STACKS=()
[ -f package.json ] && STACKS+=("nodejs")
[ -f Cargo.toml ] && STACKS+=("rust")
[ -f go.mod ] && STACKS+=("golang")
[ -f pyproject.toml ] || [ -f requirements.txt ] && STACKS+=("python")
[ -f Gemfile ] && STACKS+=("ruby")

# Run setup for each detected stack
for stack in "${STACKS[@]}"; do
  echo "Setting up $stack..."
  case "$stack" in
    nodejs)
      if [ -f bun.lockb ]; then
        bun install
      elif [ -f pnpm-lock.yaml ]; then
        pnpm install
      elif [ -f yarn.lock ]; then
        yarn install
      else
        npm install
      fi
      ;;
    rust)
      cargo build
      ;;
    golang)
      go mod download
      ;;
    python)
      if [ -f pyproject.toml ]; then
        pip install -e .
      else
        pip install -r requirements.txt
      fi
      ;;
    ruby)
      bundle install
      ;;
  esac
done

Baseline test verification:

bash
# Run tests and capture output (allow pre-existing failures)
for stack in "${STACKS[@]}"; do
  case "$stack" in
    nodejs)
      if [ -f bun.lockb ]; then
        bun test 2>&1 | tee test-output.log
      else
        npm test 2>&1 | tee test-output.log
      fi
      ;;
    rust)
      cargo test 2>&1 | tee test-output.log
      ;;
    golang)
      go test ./... 2>&1 | tee test-output.log
      ;;
    python)
      pytest 2>&1 | tee test-output.log
      ;;
  esac

  # Parse test results (do NOT block on pre-existing failures)
  PASSING=$(grep -o '[0-9]* passing' test-output.log | cut -d' ' -f1)
  FAILING=$(grep -o '[0-9]* failing' test-output.log | cut -d' ' -f1)

  echo "Baseline tests: $PASSING passing, $FAILING failing"
  if [ "${FAILING:-0}" -gt 0 ]; then
    echo "WARNING: Pre-existing test failures detected. Proceed with caution."
  fi
done

Phase 5: Handoff

Structured output for orchestrator:

Worktree ready:
  Path: .worktrees/feature-auth-system
  Branch: feature/auth-system
  Stacks: nodejs
  Dependencies: installed
  Tests: 47 passing, 0 failing
  Status: READY

Original CWD: /path/to/project
Worktree CWD: /path/to/project/.worktrees/feature-auth-system

Write metadata to session (for cleanup phase):

bash
cat > "${SESSION_PATH}/worktree-metadata.json" <<EOF
{
  "worktreePath": "$WORKTREE_PATH",
  "absolutePath": "$(realpath $WORKTREE_PATH)",
  "branchName": "$BRANCH",
  "originalCwd": "$ORIGINAL_CWD",
  "createdAt": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
  "stacks": $(printf '%s\n' "${STACKS[@]}" | jq -R . | jq -s .),
  "baselineTests": {
    "passing": ${PASSING:-0},
    "failing": ${FAILING:-0}
  },
  "status": "active"
}
EOF

Orchestrator context passing (for dev:developer):

SESSION_PATH: ${SESSION_PATH}
WORKTREE_PATH: ${WORKTREE_PATH}
BRANCH_NAME: ${BRANCH_NAME}
ORIGINAL_CWD: ${ORIGINAL_CWD}

IMPORTANT: You are working in an isolated worktree.
- cd to WORKTREE_PATH before writing any code
- Session artifacts stay in SESSION_PATH (main worktree)
- When complete, return control to orchestrator for cleanup

Phase 6: Cleanup

Trigger conditions:

  • Orchestrator completion phase
  • Explicit user request via /dev:worktree cleanup
  • Error recovery (abort implementation)

Steps (with safety checks):

bash
# 1. Verify worktree metadata exists
if [ ! -f "${SESSION_PATH}/worktree-metadata.json" ]; then
  ERROR: "No worktree metadata found"
  RECOVERY: Ask user for worktree path manually
fi

# Read metadata
WORKTREE_PATH=$(jq -r .worktreePath "${SESSION_PATH}/worktree-metadata.json")
ORIGINAL_CWD=$(jq -r .originalCwd "${SESSION_PATH}/worktree-metadata.json")
BRANCH=$(jq -r .branchName "${SESSION_PATH}/worktree-metadata.json")

# 2. Return to original CWD if currently in worktree
CURRENT_CWD=$(pwd)
if [[ "$CURRENT_CWD" == *"$WORKTREE_PATH"* ]]; then
  cd "$ORIGINAL_CWD"
fi

# 3. Check for uncommitted changes
cd "$WORKTREE_PATH"
if [ -n "$(git status --porcelain)" ]; then
  # Use AskUserQuestion to resolve
  echo "WARNING: Uncommitted changes detected in worktree"
  # Options: commit now, stash, discard, abort cleanup
  # DO NOT proceed until resolved
fi

# 4. Return to original CWD
cd "$ORIGINAL_CWD"

# 5. Remove worktree (NEVER use rm -rf)
git worktree remove "$WORKTREE_PATH"

# 6. Optionally delete branch (only if merged or user confirms)
if git branch --merged | grep -q "$BRANCH"; then
  git branch -d "$BRANCH"  # Safe delete (only if merged)
else
  # Ask user: keep branch or force delete?
  # git branch -D "$BRANCH"  # Force delete (only with confirmation)
fi

# 7. Update metadata status
jq '.status = "removed" | .removedAt = "'$(date -u +%Y-%m-%dT%H:%M:%SZ)'"' \
  "${SESSION_PATH}/worktree-metadata.json" > "${SESSION_PATH}/worktree-metadata.json.tmp"
mv "${SESSION_PATH}/worktree-metadata.json.tmp" "${SESSION_PATH}/worktree-metadata.json"

# 8. Remove statusline worktree marker
if [ -n "$SESSION_ID" ]; then
  rm -f "$HOME/.claude/.statusline-worktree-${SESSION_ID}"
fi

Error handling during cleanup:

ErrorDetectionRecovery
Currently in worktreeCWD matches worktree pathAuto-return to original CWD
Uncommitted changesgit status --porcelain not emptyAsk user: commit/stash/discard
Worktree in usegit worktree remove failsAsk: force removal with --force?
Branch not mergedgit branch -d failsAsk: keep branch or force delete?
Stale marker fileMarker exists but worktree removedAuto-cleaned by statusline (marker ignored if worktree gone)

Error Recovery

Critical errors (abort workflow):

  • Not a git repository
  • Permission denied creating directory
  • Git worktree creation fails without clear reason

Recoverable errors (ask user):

  • Branch already exists → Use existing or rename
  • Path already exists → Choose different path or remove
  • Uncommitted changes during cleanup → Commit/stash/discard
  • Tests fail during baseline → Proceed or abort

Quick Reference Card

Before starting isolated work:

  1. ✅ Run Pre-flight Checks (Phase 1)
    • Git repo valid
    • Branch available
    • Path available
  2. ✅ Select or detect worktree directory (Phase 2)
  3. ✅ Create worktree + verify .gitignore (Phase 3)
  4. ✅ Install dependencies for all stacks (Phase 4)
  5. ✅ Run baseline tests (Phase 4)
  6. ✅ Store metadata and hand off to agent (Phase 5)

After completing work:

  1. ✅ Return to original CWD if in worktree
  2. ✅ Check for uncommitted changes → resolve
  3. ✅ Remove worktree with git worktree remove
  4. ✅ Optionally delete branch (only if merged or confirmed)

Remember: Always use git worktree remove, never rm -rf.

Orchestrator Integration

/dev:feature Integration

Phase 0 (after session init):

  • Add AskUserQuestion: "Create isolated worktree?"
  • If yes: Run Phases 1-5, pass WORKTREE_PATH to all agents
  • If no: Continue as normal

Phase 8 (completion):

  • If worktree created: Ask cleanup options (PR, merge, keep, discard)
  • Execute Phase 6 with chosen option

/dev:implement Integration

Phase 1 (lightweight version):

  • Add AskUserQuestion: "Use isolated worktree?"
  • If yes: Run Phases 1-5 with defaults (no directory selection)
  • Use .worktrees/ automatically, skip multi-stack detection

Phase 6 (finalization):

  • If worktree created: Ask "Merge and clean up?"
  • Execute Phase 6 with merge or keep option

Subagent Context Template

When delegating to agents in worktrees, always include:

SESSION_PATH: ${SESSION_PATH}
WORKTREE_PATH: ${WORKTREE_PATH}
BRANCH_NAME: ${BRANCH_NAME}

IMPORTANT: You are working in an isolated worktree.
- cd to WORKTREE_PATH before writing any code
- Read from ${SESSION_PATH} for architecture/requirements
- Write code to WORKTREE_PATH (current branch: ${BRANCH_NAME})
- Session artifacts stay in ${SESSION_PATH} (main worktree)

Integration with Other Skills

  • test-driven-development: Baseline tests verify environment readiness
  • verification-before-completion: Worktree cleanup requires clean state
  • systematic-debugging: Isolated worktrees for reproducing bugs
  • context-detection: Multi-stack detection feeds into setup phase
  • agent-coordination-discipline: Parallel agents use separate worktrees

Frequently asked questions

What does the Worktree Lifecycle AI skill do?

Use when starting isolated feature work or before executing implementation plans. Manages full worktree lifecycle from creation through cleanup with safety checks and error recovery.

Why use Worktree Lifecycle on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MadAppGang/claude-code/tree/main/plugins/dev/skills/discipline/worktree-lifecycle. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Worktree Lifecycle?

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 Worktree Lifecycle?

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

Is the Worktree Lifecycle AI skill free?

Yes. It is published on GitHub by MadAppGang 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇