Spawn

Launch N parallel subagents in isolated git worktrees to compete on the session task. Use when the user runs /hub:spawn or asks to start the competing agents for an initialized AgentHub session.

Published by @Alireza Rezvani·0 agent reads / 30d·0 saves·

/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:

TemplatePatternUse Case
optimizerEdit → eval → keep/discard → repeat x10Performance, latency, size reduction
refactorerRestructure → test → iterate until greenCode quality, tech debt
test-writerWrite tests → measure coverage → repeatTest coverage gaps
bug-fixerReproduce → diagnose → fix → verifyBug 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"
)
  1. 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:status
  • Evaluate when done with /hub:eval

Bundled with this artifact

1 file

Reference files that ship alongside this artifact. Agents pull these in only when the task needs them.

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