Parallel Development with agent-cli dev

SkillProductivity

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Parallel Development with agent-cli dev skill

What this skill tells your AI

The instructions your AI receives, as published by mindroom-ai/mindroom in .claude/skills/agent-cli-dev/SKILL.md and read by ahel’s review.

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

OptionDescription
--agent / -aStart AI coding agent after creation
--prompt / -pInitial prompt for the agent (short prompts only)
--prompt-file / -PRead prompt from file (recommended for longer prompts)
--from / -fBase branch (default: origin/main)
--with-agentSpecific agent: claude, aider, codex, gemini
--agent-argsExtra arguments for the agent

@examples.md

Signals

GitHub stars
274
Forks
15
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
agent-cli-dev
Source
github.com/mindroom-ai/mindroom