worktree-check
The worktree-check slash command displays the current status of a Git worktree environment, including branch name, assigned task from a .worktree-task.md file, commit count ahead of main, and file modification summary. Use this command to verify your worktree configuration and review uncommitted changes before proceeding with delivery or when context-switching between worktrees.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/davila7/claude-code-templates/HEAD/.claude/commands/worktree-check.md -o ~/.claude/commands/worktree-check.mdworktree-check.md
# Worktree Status Check Verify the current worktree environment and show task details. ## Instructions You are inside a worktree (or the main repo). Gather and display the current status clearly. ### Step 1: Detect Worktree 1. Get the current directory: `pwd` 2. List all worktrees: `git worktree list` 3. Determine if the current directory is a worktree (not the main working tree). The main working tree is listed first in `git worktree list` output — if the current path matches the first entry, this is the main repo, not a worktree. If this is **not** a worktree, inform the user: > You're in the main repository, not a worktree. Use `/worktree-init` to create worktrees. Then list any existing worktrees and exit. ### Step 2: Show Branch Info 1. Get current branch: `git branch --show-current` 2. Verify it follows the `claude/*`, `claude-daniel/*`, or `review/*` naming convention 3. Show how many commits ahead of origin/main: `git rev-list --count origin/main..HEAD` ### Step 3: Read Task 1. Check if `.worktree-task.md` exists in the worktree root 2. If it exists, read and display its contents 3. If it doesn't exist, note that no task file was found (may have been created manually) ### Step 4: Show Working Status Run and display: 1. `git status --short` — show modified, staged, and untracked files 2. `git diff --stat` — show a summary of unstaged changes ### Step 5: Display Summary Present a clean summary: ``` Worktree Status ────────────────────────────────── Branch: claude/<name> Task: <task description from .worktree-task.md> Commits: <N> ahead of main Modified: <N> files Staged: <N> files Untracked: <N> files ────────────────────────────────── ``` If there are changes ready to deliver, suggest: "Run `/worktree-deliver` when you're ready to commit, push, and create a PR."
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.
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>
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.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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
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.
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