Skip to main content
ClaudeWave
EN, English
Slash Command32.8k repo starsupdated today

worktree-init

The worktree-init command creates isolated git worktrees for parallel development workflows, enabling users to work on multiple tasks simultaneously in separate Ghostty terminal panels. Use this when you need to develop several features or fixes concurrently without switching branches, automatically generating kebab-case branch names, setting up directory structures, and providing ready-to-run commands for each isolated Claude instance within its own worktree environment.

Install in Claude Code
Copy
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/davila7/claude-code-templates/HEAD/.claude/commands/worktree-init.md -o ~/.claude/commands/worktree-init.md
Then start a new Claude Code session; the slash command loads automatically.

worktree-init.md

# Worktree Parallel Init

Create multiple git worktrees for parallel development: $ARGUMENTS

## Instructions

You are setting up parallel worktrees so the user can work on multiple tasks simultaneously in separate Ghostty terminal panels, each running its own Claude instance.

### Step 1: Validate Environment

1. Check this is a git repository: `git rev-parse --is-inside-work-tree`
2. Get the repo name: `basename $(git rev-parse --show-toplevel)`
3. Get the main branch name (check for `main` or `master`): `git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@'` — if that fails, default to `main`
4. Ensure working tree is clean: `git status --porcelain`. If dirty, warn the user and ask if they want to continue.
5. Fetch latest: `git fetch origin`

### Step 2: Parse Tasks

Parse tasks from `$ARGUMENTS`. Tasks are separated by `|` (pipe character).

If `$ARGUMENTS` is empty, use AskUserQuestion to ask the user to describe their tasks (they can provide multiple separated by `|`).

For each task description:
- Trim whitespace
- Generate a kebab-case branch name: `claude/<kebab-case-task>` (max 50 chars, alphanumeric and hyphens only)
- Generate a worktree directory path: `../worktrees/<repo-name>/claude-<kebab-case-task>`

### Step 3: Create Worktrees

For each task:

1. Create the parent directory if needed: `mkdir -p ../worktrees/<repo-name>`
2. Create the worktree:
   ```bash
   git worktree add -b claude/<name> ../worktrees/<repo-name>/claude-<name> origin/<main-branch>
   ```
3. Write a `.worktree-task.md` file inside the new worktree with this content:
   ```markdown
   # Worktree Task

   **Branch:** claude/<name>
   **Task:** <original task description>
   **Created:** <ISO date>
   **Source repo:** <path to main repo>
   ```

### Step 4: Check for Dependencies

If a `package.json` exists in the repo root, note that each worktree may need `npm install` (or the appropriate package manager).

Check for:
- `package-lock.json` → npm install
- `yarn.lock` → yarn install
- `pnpm-lock.yaml` → pnpm install
- `bun.lockb` → bun install

### Step 5: Output Summary

Display a clear summary table:

```
| # | Task | Branch | Path |
|---|------|--------|------|
| 1 | ... | claude/... | ../worktrees/repo/claude-... |
```

Then display ready-to-copy commands for Ghostty panels. For each worktree:

```
# Panel <N>: <task description>
cd <absolute-path-to-worktree> && claude
```

If dependencies were detected, add a note:
```
# Note: Run <package-manager> install in each worktree before starting
```

Finally, remind the user:
- Open a new Ghostty panel with `Cmd+D` (split right) or `Cmd+Shift+D` (split down)
- When done with a task, use `/worktree-deliver` to commit, push, and create a PR
- After merging all PRs, use `/worktree-cleanup --all` from the main repo
agent-evaluationSkill

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

agent-expertSubagent

Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>

agent-managementSkill

Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.

agent-manager-skillSkill

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

agent-memory-mcpSkill

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

agent-memory-systemsSkill

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

agent-messagingSkill

Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.

agent-tool-builderSkill

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa