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spawn

alirezarezvani/claude-skills

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

What is spawn?

Spawns multiple competing agents that work on the same task in parallel, each in an isolated git worktree. Use this when you have an initialized AgentHub session and want to explore diverse solution approaches simultaneously.

  • Launches N subagents in a single message for true parallelism
  • Isolates each agent in its own git worktree to prevent conflicts
  • Assigns each agent a unique task dispatch with strategy guidance
  • Supports template-based dispatch prompts (optimizer, refactorer, test-writer, bug-fixer)
  • Tracks agent progress via board posts and result summaries
  • Updates session state to running after spawn

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
  • Session ID (from /hub:init or /hub:status)
Claude Code
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How to use spawn

  1. 1.Run /hub:spawn to spawn agents for the latest session, or /hub:spawn {session-id} for a specific session
  2. 2.Optionally specify a template with --template {name} (optimizer, refactorer, test-writer, or bug-fixer)
  3. 3.Agents launch in parallel, each in an isolated worktree with a unique dispatch assignment
  4. 4.Monitor progress with /hub:status
  5. 5.Evaluate results when agents complete with /hub:eval

Use cases

Good for
  • Performance optimization: spawn agents with different optimization strategies to find the best approach
  • Code refactoring: launch agents with competing refactoring strategies and compare results
  • Test coverage: spawn agents to write tests targeting different coverage gaps in parallel
  • Bug fixing: launch agents with different diagnostic and fix strategies to find the most robust solution
Who it's for
  • AgentHub session coordinators
  • Teams exploring multiple solution approaches in parallel
  • Performance engineers optimizing code
  • QA engineers expanding test coverage

spawn FAQ

Can I spawn agents for a specific session?

Yes, use /hub:spawn {session-id} where session-id is the timestamp from /hub:status (e.g., 20260317-143022).

What templates are available?

Four templates: optimizer (performance/size), refactorer (code quality), test-writer (coverage), and bug-fixer (bug resolution). Each provides different dispatch prompts and strategies.

Do agents interfere with each other?

No. Each agent works in an isolated git worktree and cannot read or modify other agents' work or results. All changes are committed independently.

How do I see agent results?

Use /hub:status to monitor running agents, then /hub:eval to compare and evaluate their results once they complete.

Can I modify the session config after spawning?

No. The session config must remain stable after spawn because agents rely on it for task and constraint information.

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:status
  • Evaluate when done with /hub:eval

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