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spawn

alirezarezvani/claude-skills

Launch N parallel subagents in isolated git worktrees to compete on solving the same task.

What is spawn?

Spawn multiple competing agents that work in parallel on a single task, each in an isolated git worktree. Use this when you have an initialized AgentHub session and want to explore diverse solution strategies simultaneously via /hub:spawn.

  • Launches N subagents in parallel, each with its own isolated git worktree
  • Assigns each agent a unique task dispatch and strategy template (optimizer, refactorer, test-writer, bug-fixer)
  • Collects results from all agents to .agenthub/board/results/ for comparison
  • Prevents cross-agent interference with strict isolation and read/write constraints
  • Supports session-specific or template-based dispatch prompts

How to install spawn

npx skills add https://github.com/alirezarezvani/claude-skills --skill spawn
Prerequisites
  • Initialized AgentHub session with config.yaml in .agenthub/sessions/{session-id}/
  • Git repository with worktree support
  • Python environment with session_manager.py available
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How to use spawn

  1. 1.Run /hub:spawn to launch agents for the latest session, or /hub:spawn {session-id} for a specific one
  2. 2.Optionally specify a template with --template optimizer|refactorer|test-writer|bug-fixer
  3. 3.Agents will be launched simultaneously in isolated worktrees and begin working on the task
  4. 4.Monitor progress with /hub:hub-status
  5. 5.Evaluate and compare results when agents complete with /hub:eval

Use cases

Good for
  • Performance optimization: spawn agents with different optimization strategies and compare results
  • Code refactoring: run multiple refactoring approaches in parallel and keep the best
  • Test coverage: have competing agents write tests and measure coverage improvements
  • Bug fixing: launch agents with different debugging strategies to find the root cause faster
Who it's for
  • AgentHub session managers
  • Teams exploring multiple solution approaches in parallel
  • Developers optimizing code performance or quality

spawn FAQ

Can I run agents with different strategies?

Yes. When using --template, assign each agent a different strategy appropriate to the template and task to maximize parallel exploration value.

How do agents avoid interfering with each other?

Each agent works in its own isolated git worktree and is forbidden from reading or modifying other agents' work or results directories.

What happens after agents finish?

Each agent writes a result summary to .agenthub/board/results/agent-{i}-result.md with approach, files changed, metrics, and confidence level. Use /hub:eval to compare and select the best.

Can I spawn agents for a past session?

Yes, use /hub:spawn {session-id} with the specific session ID to spawn agents for that session's task.

What templates are available?

optimizer (performance/size reduction), refactorer (code quality), test-writer (coverage gaps), and bug-fixer (competing debugging approaches).

Full instructions (SKILL.md)

Source of truth, from alirezarezvani/claude-skills.


name: "spawn" description: "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." command: /hub: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 ../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:hub-status
  • Evaluate when done with /hub:eval

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