Claude Code · Community skill

Hub: Spawn — Launch Parallel Agents

Spawn N subagents that work on the same task in parallel, each in an isolated git worktree.

alirezarezvani/claude-skillsexpandedInstallableskill

What this skill covers

This page keeps a stable Remote OpenClaw URL for the upstream skillwhile preserving the original source content below. The shell stays consistent, and the body can vary as much as the upstream SKILL.md or README varies.

Source files and registry paths

Source path

engineering/agenthub/skills/spawn

Entry file

engineering/agenthub/skills/spawn/SKILL.md

Repository

alirezarezvani/claude-skills

Format

markdown-skill

Original source content

Raw file
# /hub:spawn — Launch Parallel Agents

Spawn N subagents that work on the same task in parallel, each in an isolated git worktree.

## Usage

```
/hub:spawn                                    # Spawn agents for the latest session
/hub:spawn 20260317-143022                    # Spawn agents for a specific session
/hub:spawn --template optimizer               # Use optimizer template for dispatch prompts
/hub:spawn --template refactorer              # Use refactorer template
```

## Templates

When `--template <name>` is provided, use the dispatch prompt from `references/agent-templates.md` instead of the default prompt below. Available templates:

| Template | Pattern | Use Case |
|----------|---------|----------|
| `optimizer` | Edit → eval → keep/discard → repeat x10 | Performance, latency, size reduction |
| `refactorer` | Restructure → test → iterate until green | Code quality, tech debt |
| `test-writer` | Write tests → measure coverage → repeat | Test coverage gaps |
| `bug-fixer` | Reproduce → diagnose → fix → verify | Bug fix with competing approaches |

When using a template, replace all `{variables}` with values from the session config. Assign each agent a **different strategy** appropriate to the template and task — diverse strategies maximize the value of parallel exploration.

## What It Does

1. Load session config from `.agenthub/sessions/{session-id}/config.yaml`
2. For each agent 1..N:
   - Write task assignment to `.agenthub/board/dispatch/`
   - Build agent prompt with task, constraints, and board write instructions
3. Launch ALL agents in a **single message** with multiple Agent tool calls:

```
Agent(
  prompt: "You are agent-{i} in hub session {session-id}.

Your task: {task}

Read your full assignment at .agenthub/board/dispatch/{seq}-agent-{i}.md

Instructions:
1. Work in your worktree — make changes, run tests, iterate
2. Commit all changes with descriptive messages
3. Write your result summary to .agenthub/board/results/agent-{i}-result.md
   Include: approach taken, files changed, metric if available, confidence level
4. Exit when done

Constraints:
- Do NOT read or modify other agents' work
- Do NOT access .agenthub/board/results/ for other agents
- Commit early and often with descriptive messages
- If you hit a dead end, commit what you have and explain in your result",
  isolation: "worktree"
)
```

4. Update session state to `running` via:
```bash
python {skill_path}/scripts/session_manager.py --update {session-id} --state running
```

## Critical Rules

- **All agents in ONE message** — spawn all Agent tool calls simultaneously for true parallelism
- **isolation: "worktree"** is mandatory — each agent needs its own filesystem
- **Never modify session config** after spawn — agents rely on stable configuration
- **Each agent gets a unique board post** — dispatch posts are numbered sequentially

## After Spawn

Tell the user:
- {N} agents launched in parallel
- Each working in an isolated worktree
- Monitor with `/hub:status`
- Evaluate when done with `/hub:eval`
Deploy agents, MCP servers, and backends fast logo

Railway - Deploy agents and MCP servers fast

Try Railway