spawn

name	spawn
description	Implement — Launch N parallel subagents in isolated git worktrees to compete on the session task.
command	/hub:spawn
executor	LLM_BEHAVIOR
skill_id	engineering.cs_engineering.agenthub.spawn
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["engineering","git","agent"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
output_schema	[{"name":"result","type":"string","description":"Primary output from spawn"}]

/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:

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

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Why This Skill Exists

Implement — Launch N parallel subagents in isolated git worktrees to compete on the session task.

When to Use

Use this skill when the task requires spawn capabilities.

What If Fails

If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.