/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
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
Load session config from
.agenthub/sessions/{session-id}/config.yaml
For each agent 1..N:
Write task assignment to
.agenthub/board/dispatch/
Build agent prompt with task, constraints, and board write instructions
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"
)
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