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.
Content & Writing
87 Stars
9 Forks
Updated Jan 18, 2026, 09:43 PM
Why Use This
This skill provides specialized capabilities for basnijholt's codebase.
Use Cases
Developing new features in the basnijholt repository
Refactoring existing code to follow basnijholt standards
Understanding and working with basnijholt's codebase structure
---
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:
```bash
# Install globally
uv tool install agent-cli -p 3.13
# Or run directly without installing
uvx --python 3.13 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 new features (starts from origin/main):
```bash
agent-cli dev new <branch-name> --agent --prompt "Implement the new feature..."
```
For work on current branch (review, test, fix) - use `--from HEAD`:
```bash
agent-cli dev new <branch-name> --from HEAD --agent --prompt "Review/test/fix..."
```
For longer prompts (recommended for multi-line or complex instructions):
```bash
agent-cli dev new <branch-name> --from HEAD --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. Saves your prompt to `.claude/TASK.md` in the worktree (for reference)
4. Opens a new terminal tab with an AI coding agent
5. 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:
```bash
# 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
After spawning, you can check progress:
```bash
# 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:
```bash
# 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 ref (default: origin/main). **Use `--from HEAD` when reviewing/testing current branch!** |
| `--with-agent` | Specific agent: claude, aider, codex, gemini |
| `--agent-args` | Extra arguments for the agent |
@examples.md