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- Initialized AgentHub session with config.yaml in .agenthub/sessions/{session-id}/
- Git repository with worktree support
- Python environment with session_manager.py available
How to use spawn
- 1.Run /hub:spawn to launch agents for the latest session, or /hub:spawn {session-id} for a specific one
- 2.Optionally specify a template with --template optimizer|refactorer|test-writer|bug-fixer
- 3.Agents will be launched simultaneously in isolated worktrees and begin working on the task
- 4.Monitor progress with /hub:hub-status
- 5.Evaluate and compare results when agents complete with /hub:eval
Use cases
- 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
- AgentHub session managers
- Teams exploring multiple solution approaches in parallel
- Developers optimizing code performance or quality
spawn FAQ
Yes. When using --template, assign each agent a different strategy appropriate to the template and task to maximize parallel exploration value.
Each agent works in its own isolated git worktree and is forbidden from reading or modifying other agents' work or results directories.
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.
Yes, use /hub:spawn {session-id} with the specific session ID to spawn agents for that session's task.
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:
| 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
- Write task assignment to
- 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
runningvia:
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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