/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 ../agenthub/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:

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:hub-status
• Evaluate when done with /hub:eval