agent-cli-dev

name	agent-cli-dev
description	Spawns AI coding agents in isolated git worktrees. Use when the user asks to spawn or launch an agent, delegate a task to a separate agent, work in a separate worktree, or parallelize development across features.

Parallel Development with agent-cli dev

This skill teaches you how to spawn parallel AI coding agents in isolated git worktrees using the agent-cli dev command.

Installation

If agent-cli is not available, install it first:

# Install globally
uv tool install agent-cli

# Or run directly without installing
uvx agent-cli dev new <branch-name> --agent --prompt "..."

When to spawn parallel agents

Spawn separate agents when:

• Multiple independent features/tasks can be worked on in parallel
• Tasks benefit from isolation (separate branches, no conflicts)
• Large refactoring that can be split by module/component
• Test-driven development (one agent for tests, one for implementation)

Do NOT spawn when:

• Tasks are small and sequential
• Tasks have tight dependencies requiring constant coordination
• The overhead of context switching exceeds the benefit

Core command

For short prompts:

agent-cli dev new <branch-name> --agent --prompt "Fix the login bug"

For longer prompts (recommended for multi-line or complex instructions):

agent-cli dev new <branch-name> --agent --prompt-file path/to/prompt.md

This creates:

1. A new git worktree with its own branch
2. Runs project setup (installs dependencies)
3. Opens a new terminal tab with an AI coding agent
4. Passes your prompt to the agent

Important: Use --prompt-file for prompts longer than a single line. The --prompt option passes text through the shell, which can cause issues with special characters (exclamation marks, dollar signs, backticks, quotes) in ZSH and other shells. Using --prompt-file avoids all shell quoting issues.

Writing effective prompts for spawned agents

Spawned agents work in isolation, so prompts must be self-contained. Include:

1. Clear task description: What to implement/fix/refactor
2. Relevant context: File locations, patterns to follow, constraints
3. Report request: Ask the agent to write conclusions to .claude/REPORT.md

Using --prompt-file (recommended)

For any prompt longer than a single sentence:

1. Write the prompt to a temporary file (e.g., .claude/spawn-prompt.md)
2. Use --prompt-file to pass it to the agent
3. The file can be deleted after spawning

Example workflow:

# 1. Write prompt to file (Claude does this with the Write tool)
# 2. Spawn agent with the file
agent-cli dev new my-feature --agent --prompt-file .claude/spawn-prompt.md
# 3. Optionally clean up
rm .claude/spawn-prompt.md

Prompt template

<Task description>

Context:
- <Key file locations>
- <Patterns to follow>
- <Constraints or requirements>

When complete, write a summary to .claude/REPORT.md including:
- What you implemented/changed
- Key decisions you made
- Any questions or concerns for review

Checking spawned agent results

Claude Code context compaction

Claude Code automatically compacts its context when needed. Treat a high or full context meter as informational, not as a blocker. Never rush, interrupt, clear, or restart a Claude agent merely because its context meter is near 100%. Keep polling normally and let compaction finish. Intervene only on concrete evidence of a stuck command or lost progress, not on context-window usage alone.

After spawning, you can check progress:

# List all worktrees and their status
agent-cli dev status

# Read an agent's report
agent-cli dev run <branch-name> cat .claude/REPORT.md

# Open the worktree in your editor
agent-cli dev editor <branch-name>

Example: Multi-feature implementation

If asked to implement auth, payments, and notifications:

# Spawn three parallel agents
agent-cli dev new auth-feature --agent --prompt "Implement JWT authentication..."
agent-cli dev new payment-integration --agent --prompt "Add Stripe payment processing..."
agent-cli dev new email-notifications --agent --prompt "Implement email notification system..."

Each agent works independently in its own branch. Results can be reviewed and merged separately.

Key options

Option	Description
--agent / -a	Start AI coding agent after creation
--prompt / -p	Initial prompt for the agent (short prompts only)
--prompt-file / -P	Read prompt from file (recommended for longer prompts)
--from / -f	Base branch (default: origin/main)
--with-agent	Specific agent: claude, aider, codex, gemini
--agent-args	Extra arguments for the agent

@examples.md